SAP Community - SAP Analytics Hub

SAP: Future of Analytics webinar Sept 8th. Your questions answered

2020-09-24T13:09:31+02:00

On Sept 8th we delivered the public webinar: the Future of SAP Analytics hosted by Oliver Conze and Iver van de Zand. A massive amount of people attended. If you missed that webinar, you can find the recording here.

In the webinar, we received numerous questions. We grouped them and try to answer them with below.

What is the difference between SAP Analytics Catalog and SAP Analytics Hub

SAP Analytics catalog & SAP Analytics Hub are complementary services. Both solutions can be quickly operationalized. They also help your IT team point all types of content to a single access point. At the same time, it frees IT from mundane tasks by allowing users to access the analytics they need on their own and manage the lifecycle for content retention and validation.

SAP Analytics Catalog is a new feature of SAP Analytics Cloud that offers all SAC users single user-friendly landing page to all SAC and non-SAC content. It is recommended for all customers who do not yet have Analytics Hub.

SAP Analytics Hub is only recommended for customers who do not have an SAP Analytics Cloud, do not want it in the short term and have a lot of non-SAP analytical reports on premise.

SAP Analytics Cloud vs S/4 Hana Embedded Analytics

SAP Analytics Cloud integrates seamlessly with SAP S/4HANA, combining all lines of business into one powerful and robust analytics solution

There exists a standard interface between SAP S/4HANA embedded analytics and SAP Analytics Cloud.

With the new app “Create Tile for SAP Analytics Cloud Story”, the Analytics Specialist in SAP S/4HANA Cloud Edition can view the list of all the available stories created in Analytics Cloud system. Key user can then create a tile in SAP S/4HANA system which can launch a story directly created in Analytics Cloud system.

Is it Possible to connect Smart Predict to SAP HANA tables and SQL Views?

Smart Predict on live SAP HANA is included with the 2019 Q4 quarterly release of SAP Analytics Cloud. It is possible to connect Smart Predict to SAP HANA tables and SQL views.

Smart Predict Live currently works today against on-premise SAP HANA, not on the cloud deployments of SAP HANA.

Regarding SAP BW on HANA and SAP BW/4HANA customers, please note:

SAP BW customers with SAP HANA Enterprise Edition or SAP HANA Predictive Option licenses have the right to create tables & SQL views. Smart Predict on live SAP HANA can be used by these customers from Q4 2019.

SAP Smart Predict on live SAP HANA

Does SAP Analytics Cloud for planning replace SAP BPC?

SAP Analytics Cloud does not replace any of the versions of SAP BPC. SAP Analytics Cloud is our strategic direction for planning, integrating with S/4HANA and other solutions within the SAP portfolio. SAP BPC is still available for customers looking for an on-premise tool, or customers looking for a solution with both planning and consolidation capabilities.

One can import SAP BPC data into a new or existing model

SAP Analytics Cloud enables you to import data from both SAP Business Planning and Consolidation (BPC) for NetWeaver and Microsoft Platform versions

One can also export planning data back to your SAP BPC system, to synchronize with enterprise data

To setup a connection to SAP BPC Microsoft Platform version, you’ll require the installation and configuration of the SAP Cloud Connector and SAP Analytics Cloud Agent, whereas the NetWeaver version requires only SAP Cloud Connector

After the connectivity components setup, a connection can be created by providing SAP BPC system details and user login credentials, then the data needs to be imported into a model

Is data replicated to SAP Analytics Cloud when using a Live local connection to SAP S/4HANA?

One can create live data connections to on-premise and cloud systems. Data is “live”, meaning that when a user opens a story in SAP Analytics Cloud, changes made to the data in the source system are reflected immediately. In SAP Analytics Cloud, you can create models from data sources in on-premise or cloud systems, build stories based on those models, and perform online analysis without data replication. This feature allows SAP Analytics Cloud to be used in scenarios where data cannot be moved into the cloud for security or privacy reasons, or your data already exists on a different cloud system

SAP Analytics Cloud data connections environment

How does the SAP S/4HANA embedded analytics architecture look like?

S/4HANA embedded analytics architecture overview

Provide embedded insights within the solution, to optimally support business user in defined business processes with decision support and information at the users fingertips.

Integrate seamlessly into the enterprise intelligence offerings such as SAP Analytics Cloud and SAP BW/4HANA

Provide real-time access to solution data as well as replication of data and blending with multiple other sources for end2end enterprise intelligence

To join the next webinars in this series and learn more about extending your analytics to the cloud, visit this resource page

Commenting in SAP Analytics Cloud

2020-12-28T05:18:37+01:00

This blog describes:

Commenting in SAP Analytics Cloud
- Story Commenting
- DataPoint Commenting
- Comment Widget
- System Configuration

Commenting in SAP Analytics Cloud

Commenting is an integral part of the SAC Stories/Apps, comments have a version history and the ID of the user who wrote the comment is stored. With comments, users can collaborate quickly and easily on different topics, and learn from other users.

What do users need in the way of commenting functions?

Users need to add comments on the numbers in a report.

They want to add a comment to a diagram or table.

Or for the report as a whole. These are report comments.

Or users want to add a comment for a single cell or value.

Commenting as a process: Comments might be used as part of the planning process, for instance, to explain why a particular budget was set for an item.

And so on.

SAC Commenting workflows :

Allow a user to create and view comments on story pages or widgets.

Allow a user to create and view a comment on a cell in a data table. Each cell is considered a data point.
1. These must be tied to a model version but potentially published to actuals
2. They can be surfaced to any grid using the model / version

Allow a user to see all comments for the given context (a story and all its models) in a right hand panel
1. The effective set of comments shown in the right hand panel would be all comments for all InA queries coming from all widgets within the story across all drill levels

Users must only see comments for that they have access to - because they can access the story or the data points which those comments are attached to.

We must "aggregate" data point comments so that we can show an indicator on table at a higher drill level to let users know that lower drill levels have comments to display. The aggregated data is currently not persisted anywhere in Orca.

We must display a snapshot of the previous data in the case the data in the table has changed. This allows user to view the comment with the right context.

Story Commenting

In SAP Analytics Cloud, you can add a comment to a specific page or widget within a story and other users can reply or like your comment.

To add a comment to a story page, on the page tab bar, select the Drop-down menu → Comment → Place Comment → Enter the comment in the displayed box.

To add a new comment, choose the diagram and then click the Add -> Comment Option.

Add comment Option on Story Widgets

You can enter your text in the box that then appears. You can address your comment to specific members of your team by marking it with @.

Choose Place Comment to save your comment. SAP Analytics Cloud can store up to 200 comments for each story.

It sorts them automatically into threads and numbers them in date order. You can change the sort order and sort comments by page and comment number. The comments are published by saving the story. If another user logs on and calls up the same story, they can view all the comments in it by activating Comment Mode. Users can “like” comments and reply to them.

Even if the user is not authorised to change charts or other widgets of the story, they can still add comments for other elements. That way, they can share important information with their team.

When you create a comment, the system assigns it a thread number, which it displays on the right-hand side of the collaboration area as a reference. In the comment itself, you can respond to earlier comments; the new comment is added to the thread. By moving the mouse over a comment you can “like” the comment or, provided you entered the comment, delete it.

Supported:

User can leave 200 story comment threads per story. Each comment thread can have 100 replies

Story widget/Tile can have max 4 threads (ex: Chart, shapes...)

User can "@" mention others in story comments

User can see all story comments in the commenting side panel

Support all widget type except input control

User can enter 3000 characters for each comment.

Not Supported:

Not supported on mobile device nor the device preview in story

Not supported on input control

Creating a copy of a story – the copy will not contain any comments.

While using Save As option to create a new story, comments will not be saved in new file.

Duplicating a widget or a story page - the copy will not contain any comments.

Exporting a story to PDF. - No Comments will be exported

Story comments cannot be viewed or added in the Digital Boardroom.

DataPoint Commenting

In SAP Analytics Cloud, you can add comments for particular values in a table. To add a comment to a cell in a table, choose the cell and the value. SAP Analytics Cloud applies the same access rights to the comment for a given data point as would apply otherwise. For instance, a user can display data for a certain region only. If they then enter a comment for one of the values, the comment is displayed only for users who also have access rights that permit them to display data for that region. If a user calls up the table who does not have the necessary access rights, they cannot see the comment.

User can add comments to the data cells in the crosstab. The data source must have been refreshed before you add a comment to a data cell. A comment is available internally as long as it is not explicitly deleted. For example, you add a comment to a cell and then change the view on your data. In the new navigation state, the cell with the comment is not visible. As soon as you change the navigation state so that the cell with the comment is again visible in the crosstab, the comment is also available.

The comments are associated with the selection of the cell (context). The selection includes all selected values for the dimension, for example prompt, filter and background filter values. Analysis stores for each dimension the single member selection if the selection is a single member selection, for example a single value in the prompting definition. For other selections, Analysis stores that there has not been a single member selection for this dimension. Note that a comment that is placed on the total cell of a dimension also is considered to not have a single member selection associated for this dimension. A comment is visible in a cell if the cell has for all dimensions the matching single member selection or the comment and the cell both do not have associated a single member selection.

Datapoint Comments are with Model not Story. If new story/App created with same model, user can see all the comments added from any story on this model.

Add Comment option on Table Data Cell:

User can Create separate Table column to see Comments:

Supported:

Comment on a single cell within the table.

Supported for planning Model and Analytic Model

User can leave up to 3000(Configurable) datapoint comment threads per model. Each comment thread can have 100 replies

Only supported for table, both in grid or as a table widget in the canvas or responsive page

Supports default currency and Local Currency

Restricted Measure (starting in 2018.13+)

Supported for private version.

Comments will continue to show up when any hierarchy has been changed (e.g. from YQM to YM).

Comments will display the previous data value if more data has been acquired or edited.

Datapoint comment is saved in real time.

User can enter 3000 characters for each comment.

Not Supported:

Comment on non-data cell or multiple cells are not supported

Commenting not supported when cell is being edited.

Commenting is not supported when there are no dimensions in the row or column of a table.

Not supported for blended tables.

Not supported for total cells.

Datapoint Commenting is not supported for
- Remote Model
- Key Figure Model
- Private / Embedded Model

Not supported for any other chart types

Not supported for calculated Measures or model calculations
- Exception: commenting is supported for restricted measures

Not supported with variables.

Not supported with navigational attribute (a.k.a dimension property). Commenting is disabled when there are navigational attributes in the table.

"@" mention is not supported

Not supported on mobile device nor the device preview in story

Not supported when users have OR in their role level security or filter.

Not supported for combinational filter

Not supported for geometry filter.

Creating Version by copying - the new copied version will not contain any comments.

Comment Widget

This Feature introduced in 2020.14+ SAC Releases.

Story/App Creator can create new widget type called "Comment Widget" . This widget is used for adding Comments on specific Datapoint of model selected in widget builder panel.

In below image , Comment widget created on model "APDProfitAndLoss" and dimension context is Version : Actual and Account: Key Performance Indicator.

Procedure

From the Insert menu, select Add Comment Widget.

A comment widget is placed on the story page and the Builder panel displays the widget's filters.

Add a dimension filter, selecting all dimension members or a subset.

Comments in Comment widget are also Datapoint Comments, so User can see same Comments in Table if same dimension context exist .

Datapoint Comments are with Model not Story. If new story/App created with same model, user can see all the comments added from any story on this model.

System Configurations :

To set or change the Configurations,

Go to System Administration Limit of comment threads per Model.

Admin can turn ON/OFF Commenting Enable on Story Embed mode.

Default is OFF

Admin can set the Limit of Comment Threads per Model for Datapoint Commenting.

Default value is 3000

Admin can set the Dimension member limit to enable Commenting on Analytic Models.

Default value is 50000

Support and Empowerment for your SAP Analytics projects

2021-03-23T21:09:23+01:00

You may already have heard about SAP Enterprise Support value maps – the empowerment and support program, which leads you through the knowledge, skills, and services needed to address business challenges and adopt intelligent technologies.

There are several topics and solutions areas supported within value maps including SAP S/4HANA, SAP SuccessFactors, ALM, SAP Customer Experience Solutions and many more including SAP Analytics solutions.

In my role, as the global topic owner for the SAP Analytics solutions value map, I am responsible for the day to day operations, joining calls with customers, and developing and updating our content.

How can value maps support you with your SAP Analytics project?

Check out our new 3 minute video which will provide you with an introduction to what we do:

Our content is constantly evolving and we plan to bring in more Learning Journeys, Learning Programs and further assets on the topic of Business Technology Platform analytics very soon.

The value maps will guide you through the knowledge, skills, and services needed to address your business challenges, and enable your company to lead in the experience economy.

So why wait?

Register for the SAP Analytics Solutions value map

Note: To register for the value map, you may require access to SAP Learning Hub, edition for SAP Enterprise Support. Already-registered members can access the value map directly via the SAP Enterprise Support Value Maps learning room

SAP Analytics Cloud, Analytics Catalog, a new opportunity to centralize and easily access all types of analytical content

2021-04-29T06:38:10+02:00

Preliminary remark: To simplify reading, SAP Analytics Cloud, Analytics Catalog will be called SAC Analytics Catalog throughout the document.

To simplify how our customers today use SAP Analytics Cloud and based on the feedback over the years from our users, we continue integrating SAP Analytics Catalog features into the core of SAP Analytics Cloud capabilities. Access to analytics assets is vital and needs to be user friendly and easily accessible for everyone. We are convinced that the addition of a catalog experience in SAP Analytics Cloud is a huge benefit for business users and administrators. This change will bring a new direction and going forward we have decided to focus on SAC Analytics Catalog and discontinue new investments in SAP Analytics Hub. At the same time, we will place SAP Analytics Hub into sunset and remove it from the price list in the summer of 2021.

Customers who are running SAP Analytics Hub can be confident: SAP will respect all existing contracts and support all existing Analytics Hub customers who are not ready yet to migrate until the end of their subscription. However, at the same time we recommend these customers to evaluate and take a closer look at SAC Analytics Catalog. To minimize the impact on existing applications, our customers who wish to do so will have the option of renewing their existing SAP Analytics Hub contracts until the end of 2021.

Analytics Catalog

SAC Analytics Catalog provides a very rich set of features from content management to enriching asset metadata, easy navigation access and a great consumption experience. Inside this blog you can find more details of these capabilities: https://blogs.sap.com/2020/06/22/sap-analytics-cloud-analytics-catalog/.

We acknowledge that some Hub customers might find certain constraints with the custom theming, URL filtering, custom Catalog template and validation workflows because we don’t plan to target complete parity between the two solutions, but we aim to focus on active SAC Analytics Catalog customer enhancement requests in the future.

Future

For the above use cases we consider building new enhancements in SAP Analytics Cloud. With the workflow functionality, the validation workflow will go in a two step process: Users can start by creating or enriching a story. Next Catalog administrators can take action to publish for business users to access these assets inside the Catalog.

SAP Analytics Hub customers should be informed we do not plan to invest in supporting an automated migration at this time. The layout design and asset creations would be a manual process while all links to the existing content will remain the same in SAP Analytics Cloud.

We have been asked if we plan to provide a standalone version of SAC Analytics Catalog in the future. We have currently no plans for this. Each customer who wishes to benefit from SAC Analytics Catalog innovations can do so since Analytics Catalog is by default part of their existing SAP Analytics Cloud BI, Planning Public or Private Tenant(s).

SAP Analytics Hub customers are recommended considering moving to SAC Analytics Catalog. Both SAP BI and Planning public and private edition include SAC Analytics Catalog functionality as part of the SAP Analytics Cloud core subscription license. SAC Analytics Catalog feature is tightly Integrated with SAP Analytics Cloud content management workflows. Users can connect to any external content residing in different landscapes and establish SAC Analytics Catalog as a discovery experience for all analytical content, whether SAP or not.

Relevant Links

Access to Product information, Learning track and much more is here

Give customers the opportunity to influence SAP software development decisions is here

Find out here in SAP Roadmap Explorer what SAP Analytics Cloud innovations are planned for 2021

SAP Analytics Cloud: Technical Guide

2021-09-30T15:33:56+02:00

Overview:

SAP Analytics Cloud solution is a reporting apparatus used to analyze Core business processes utilizing various capabilities such as Business Intelligence (BI), augmented analytics, predictive analytics, and enterprise planning within a single environment.

The below mentioned are collaborative capabilities provided by the application suite:

Digital Boardroom used for real-time, interactive boardroom presentations.

Mobile Apps for iOS or Android devices to retrieve analytics data from anywhere and anytime.

SAP Analytics Hub a centralized repository for organizational analytics.

The technical environment has the flexibility to be deployed on Premise, in the cloud and the combination of both option known as a Hybrid solution.

Source: Ingo Hilgefort, Global Product Evangelist, SAP Analytics Cloud,2020

Documentation URL: https://www.sapinsideronline.com/using-sap-analytics-cloud-in-combination-with-sap-businessobjects-bi/

Figure 1: SAP Analytics Cloud Overview

Solution Architecture:

The solution architecture accommodates various Data source deployment options required for different reporting capabilities within every tier of the SAP Analytics Cloud Platform:

Core Capabilities: Mainly focuses on the connection options to the data available from either the cloud or on premise for analytic capabilities.

Analytic Capabilities: Mainly focused on the Analytic capabilities involved using Business Intelligence, Predictive analytics and enterprise planning.

Applications: Mainly focuses on the presentation of the data, Usually in SAP Digital Boardrooms, SAP Analytics HUB, and mobile apps.

Source: SAP Help Portal, SAP Analytics Cloud Q3 2021(2021.14)

Documentation URL: https://help.sap.com/viewer/2d7115b0e0aa4f78bfd9c06fdc1fe4f6/release/en-US

Figure 2: Solution Architecture

The below depicts the Data connection environment options such as OData services, SAP ERP, SAP S/4HANA required for data acquisition by SAP Analytics Cloud.

Source: SAP Help Portal, SAP Analytics Cloud Q3 2021(2021.14)

Documentation URL: https://help.sap.com/viewer/00f68c2e08b941f081002fd3691d86a7/release/en-US/bdf055159cbb4f36b26c93ccb0c56066.html

Figure 2.1: Data Connections Environment

Installation Steps:

The below Steps are to be performed when installing all the supporting component of the SAP Analytics cloud solution:

1.Install Tomcat application server:

Source: Apache Tomcat Organization

Documentation URL: http://tomcat.apache.org/

Figure 3: Tomcat application Server

2.Install SAPCP Cloud Connector:

Source: SAP Help Portal, SAP Analytics Cloud Q3 2021(2021.14)

Documentation URL: https://help.sap.com/viewer/00f68c2e08b941f081002fd3691d86a7/release/en-US/ae39ab60b1154c179e2baabd26aa249c.html

Figure 4: SAPCP Cloud Connector

3.Install SAP Analytics Cloud Agent:

Source: SAP Help Portal, SAP Analytics Cloud Q3 2021(2021.14)

Documentation URL: https://help.sap.com/viewer/00f68c2e08b941f081002fd3691d86a7/release/en-US/565e96275ed341658f24bd87a6209685.html

Figure 5: SAP Analytics Cloud Agent

4.Install SAP JAVA Cloud connector if you intend on importing data from SAP ERP or SAP BW:

Source: SAP Help Portal, SAP Analytics Cloud Q3 2021(2021.14)

Documentation URL: https://help.sap.com/viewer/00f68c2e08b941f081002fd3691d86a7/release/en-US/ffe3b941e82641bc97b76ce522423d83.html

Figure 6: SAP JAVA Cloud connector

5.SAP Cloud connector configuration:

Source: SAP Help Portal, SAP Analytics Cloud Q3 2021(2021.14)

Documentation URL: https://help.sap.com/viewer/00f68c2e08b941f081002fd3691d86a7/release/en-US/8d8511532794429caa243b6fb7c79989.html

Figure 7: SAP Cloud connector configuration

6.SAP Analytics Cloud connection:

Source: SAP Help Portal, SAP Analytics Cloud Q3 2021(2021.14)

Documentation URL: https://help.sap.com/viewer/00f68c2e08b941f081002fd3691d86a7/release/en-US/5339a2395ccd4befb047c625a15f8481.html

Figure 8: SAP ERP Connection

Figure 9: Organizational statistics at a Glance

Thank you all for taking a glimpse into the above Content. Please don’t forget to like, comment and share.

Yours Truly,

Unleash the value of your data with Analytics at SAP TechEd in 2021

2021-10-29T00:04:35+02:00

It's almost November and you know what this means: it's time for SAP TechEd in 2021 - all digital, all global and of course all access! You heard right: our event series is returning and this year again, you can simply join without any hassles or obligations.

You're already convinced? Then go ahead and secure your spot today! As always, we made sure to provide you the best of Analytics that helps you gain the maximum value from your data.

Why should I attend SAP TechEd in 2021?

Analytics at SAP TechEd 2021

How to register and further information

1. Why should I attend SAP TechEd?

This question was never really an issue. And the answer never changes. SAP TechEd is THE event to upskill yourself and get all the details about SAP's latest innovations and developments. On top of that, you can experience our solutions in workshop sessions and directly get in touch with experts from SAP to discuss your questions.

But that's not everything. SAP TechEd in 2021 will all digital, all global, all free. It doesn't make a difference where you are on the planet: just sign up, save your spot and participate in the event. No costs charged and no travel needed. Learning has never been easier!

Also, missing a session is no problem at all! Just watch the recordings and get back on track right away!

And before I forget - one last thing: workshop seats are limited. So grab them today!

Go back to Table of Contents

2. Analytics at SAP TechEd in 2021

For us, learning and innovation is the most important thing at SAP TechEd in 2021. This is, why we are convinced that you will not only see our latest developments but also get the full picture of how SAP can help you to gain the most value of your data.

We prepared various formats for you to consume the content you need and spend the time you have. Find below a selection of formats:

Strategy Talks: the ideal way to kick off SAP TechEd. Join our leaders and hear more about their vision on Analytics and the way forward.

Lectures: spend 30 minutes to get an introduction to a topic and see all the details

Breakouts: in up to 20 minutes, you will get the idea of a concept or feature

Workshops: time to get hands-on! Block your calendar for two exciting and insightful hours and get the most direct product experience possible!

If you want to see all Analytics sessions at a glance, click here and view our session catalog!

Do you want more details? Then check out our subtracks below and pick the content which interests you the most!

Every chapter will carry links to the respective session catalog and subtrack overview. This way, you can easily add sessions to your personal agenda.

Go back to Table of Contents

2.1. Channel 1 - Data Warehousing & Analytics

Channel 1 at SAP TechEd will be the perfect entry point for the ultimate experience. Over the course of the whole event, you will see exciting announcements, strategy talks from high-level executives and of course a lot of opportunities to ask questions and get answers.

Of course, we prepared a lot of great content for you in Channel 1. Make sure to join the stream and get the latest insights into our strategy and vision around our solution portfolio and ask your questions directly to our executives.

Click here to find out more about Channel 1 and the format.

Go back to Table of Contents

2.2. Discover Enterprise-Ready Data Warehousing

Your data warehouse has never been more important. It not only serves as your central go-to-place to get insights into your business but also provides the core of your business decisions: your data and your expertise.

Explore this track to find out how SAP's offerings in data warehousing help your business and your users to easily access the world of data modeling and expand your data reach to the maximum range.

Click here to view all sessions and add them to your agenda!

Go back to Table of Contents

2.3. Making Strategic Decisions with SAP Analytics

Data is the new gold. Haven't you heard that sentence lately? But that's just half of the truth. What's you data worth if you are not able to understand it? This is why Analytics is the key to your success.

Join this track and find out how we at SAP support you on your journey towards intelligent and reliable insights. Find out how to derive data-driven business decisions and step up your game.

Click here to view all sessions and add them to your agenda!

Go back to Table of Contents

2.4. Applying Analytics End to End

Are you already thinking end-to-end? Well, you definitely should. Bringing together all the drivers for success into one harmonized experience is the ultimate game-changer and your competitive advantage!

In this track, you will find out how our portfolio integrates into end-to-end scenarios and provides you the ultimate power to harness the value of your data. Make sure to add the sessions to your agenda and learn more about our great offerings.

Click here to view all sessions and add them to your agenda!

Go back to Table of Contents

3. How to register and further information

Can't wait for SAP TechEd in 2021? Me neither! So don't forget to head over to the official event homepage TODAY and sign up for the event of the year.

And don't forget to prepare your personal experience. Explore the whole session catalog and follow our social media channels to get the most current updates.

SAP TechEd Session catalog

SAP TechEd @ YouTube

SAP TechEd @ Facebook

SAP TechEd Global homepage

Do you have any additional questions or want to leave some feedback? Just drop a comment or feel free to reach out to me!

Go back to Table of Contents

SAP Analytics Cloud - Removing Concurrent Session licenses and other updates around managing licenses with roles and teams

2021-12-15T17:34:17+01:00

The article I published back in March 2020 about SAP Analytics Cloud – Managing Licenses with Roles and Teams has remained entirely valid since then and I've only recently found the need to update it.

The updates are needed because of improvements made to SAP Analytics Cloud and the fact that some customer are starting to move away from Business Intelligence Concurrent Session licenses.

[Update October 2022: the following is no longer applicable because of improvements made to SAP Analytics Cloud. It means you should ignore the following in ~~strikethrough~~]

If action isn't take it could mean users will not be able to log-on to SAP Analytics Cloud once the concurrent session licenses have been removed.

~~The update includes a good of points and in particular:~~

- Important tasks you need to perform either before or after Business Intelligence Concurrent Session licenses are removed. This is to ensure all your users can log-on to SAP Analytics Cloud after those licenses have been removed. There are two scenarios:
  - ~~A regular contract change resulting in the concurrent session licenses being removed where the SAP Analytics Cloud Service remains otherwise unchanged~~
  - ~~A platform migration from 'NEO' to 'Cloud Foundry'. Here the underlying Service is different and the contents are migrated by SAP.~~

Both scenarios show slightly different behaviour for the users that log-on after either of these 'events'. The good news is, both are very temporary issues and are expected to be fully resolved with SAP Analytics Cloud wave version 2022.3. Until then the article has all the details you need to avoid any surprises and allow your users to log-on.

Links to my SAP Analytics Cloud SCIM API Sample Scripts to help update users, or teams of users, with a different Business Intelligence license type. ~~They can also provide a workaround for when concurrent session licenses are to be removed.~~

Additional FAQs. I've added a bunch more to help you self-serve.

The updated article remains in the wiki and you can also download or preview the PPT edition (version 1.2.1). Both this blog post and the original blog post point to the same article. Feel free to follow either for updates.

The current list of FAQs included in the article are:

Q. For Business Intelligence Roles, why are no Teams listed under the ‘Concurrent Session License’ tab

Q. What Business Intelligence license will new users be assigned when defining Default Roles for Analytics Hub Role AND a Business Intelligence Role (and assigning the ‘concurrent session’)

Q. Is there any difference, from a license perspective, between the different roles of the same License Type

Q. Is there something special about the ‘Admin’ Roles provided by the Service

Q. What license type will be consumed by the ‘System Owner’

Q. Why is the Business Intelligence User (Concurrent Session License) Count at 0

Q. How can I see which users are members of which Teams and Roles

Q. What happens when the concurrent session license limit is reached

Q. How do you un-assign licenses

Q. Is it possible to de-activate (suspend or disable) a user

Q. How can I easily see which users are assigned which licence

Q. How do you determine who uses SAP Analytics Cloud more frequently than others

Q. Can you use SAML attribute mapping to define which user license should be used

Q. Is it possible to create a user, without any roles and without being a member of any teams, and still set the ‘Business Intelligence’ license type to ‘concurrent’

Q. Can I have 2 ‘default’ roles

Q. Are Concurrent Session licenses available

Q. Is it possible to set the users’ ‘Business Intelligence’ license type to ‘concurrent’ even when the SAP Analytics Cloud Service doesn’t have any ‘concurrent session’ licenses

Q. Can a request role be set to switch the license type to concurrent

Q. What happens when Concurrent User licenses are removed from the Service

Q. Why can’t I change the licenses assigned

Your feedback is most welcome. Before posting a question, please do read the article carefully and take note of any replies I’ve made to others in the original blog post. In general, please post questions to the original blog post, rather than here. It will just help other customers by keeping things in one place.

Please hit the ‘like’ button on this article or comments to indicate its usefulness.

Many thanks

Matthew Shaw @MattShaw_on_BI

Introduction to EU Taxonomy

2021-12-17T12:12:32+01:00

Hi, everyone, my name is Jingnan Wang, I work in SAP Profitability and Performance Management sustainability content development team and I would like to give you an introduction to EU Taxonomy. In particular what is EU Taxonomy, how to calculate the EU Taxonomy alignment, as well as to walk you through the EU Taxonomy part of the Financing and Investment Sustainability sample content.

What is EU Taxonomy?

The EU Taxonomy is a classification system aimed at investors, companies, financial institutions and policymakers, helping them establish whether an activity can be considered environmentally sustainable. It is intended to facilitate allocation of capital towards sustainable activities and projects, and therefore, navigate the transition to a low-carbon economy and meet the EU’s climate and energy targets. The EU Taxonomy establishes an EU-wide list of economic activities which meet the EU’s environmental requirements across a broad range of industry sectors enlisted below (based on NACE Classification):

Forestry

Environmental protection and restoration activities

Manufacturing

Energy

Water supply, sewerage, waste management and remediation

Transport

Construction and real estate activities

Information and communication

Professional, scientific and technical activities

Financial and insurance activities

Education

Human health and social work activities

Arts, entertainment and recreation

The EU Taxonomy Regulation was published in the Official Journal on 22 June 2020 and entered into force on 12 July 2020. On 21 April 2021 the European Commission adopted an ambitious and comprehensive package called: Sustainable finance package, which includes, inter alia, updates on the EU Taxonomy regulation. A first delegated act on sustainable activities for climate change adaptation and mitigation objectives (The EU Taxonomy Climate Delegated Act) was published on that day and it was formally adopted on 4 June 2021. A second delegated act for the remaining objectives will be published in 2022. (reference: https://ec.europa.eu/info/business-economy-euro/banking-and-finance/sustainable-finance/eu-taxonomy-sustainable-activities_en).

According to the taxonomy regulation, an economic activity is qualified as "environmentally sustainable" if it makes a “substantial contribution” to one of the following six environmental objectives:

Climate change mitigation

Climate change adaptation

Sustainable use and protection of water and marine resources

Transition to a circular economy

Pollution prevention and control

Protection and restoration of biodiversity and ecosystems

At the same time, the activity should "do no significant harm” to any other environmental objective and should comply with minimum “social safeguards”.

Large undertakings which are required to publish non-financial information under the Non-Financial Reporting Directive (NFRD) must disclose 3 KPI’s: Turnover, CapEx and OpEx, for each economic activity and the total KPIs for all economic activities, with regards to the EU Taxonomy. 

In order to assess the EU Taxonomy alignment at instrument, issuer and portfolio level in SXV sample content, the following steps were taken:

Step 1 – Identify EU-Taxonomy eligible activities
Step 2 – Identify environmental objective/s to which the activity is making a substantial contribution
Step 3 – Identify the category of each of the eligible activities (transitional or enabling)
Step 4 – Check if the substantial contribution criteria for the selected environmental objective is met
Step 5 – Check if the “do no significant harm” criteria for the remaining environmental objectives is met
Step 6  – Check if there are any negative impacts on minimum safeguards
Step 7 – Calculate the EU Taxonomy alignment
Step 8 – Calculate the taxonomy aligned, not-aligned and not eligible KPIs for Turnover, CapEx and OpEx (where relevant), as well as the taxonomy alignment results for different environmental objectives, including proportions of taxonomy-alignment (turnover) for climate change mitigation - transitional / enabling, climate change adaptation - transitional / enabling.

How to calculate EU Taxonomy alignment? -- A walk-through the EU Taxonomy part of the SAP Profitability and Performance Management sample content

One of the features of the SAP Profitability and Performance Management Financing and Investment Sustainability Management Sample Content is the EU Taxonomy Assessment which can be performed at 3 levels: issuer level, instrument level and portfolio level. More details on this assessment are provided below.

EU Taxonomy Assessment (Issuer level)

Firstly, the taxonomy-aligned turnover, CapEx and OpEx (%) per activity and per issuer are calculated, as long as a given economic activity passes all six steps of the EU Taxonomy screening test described above. Then, taxonomy alignment for climate change mitigation environmental objective – enabling and transitional activities, as well as for climate change adaptation enabling and transitional activities is calculated (CAEUT calculation function). For example, taxonomy alignment for mitigation-enabling activities is calculated by summing up the taxonomy-aligned turnover (%) per activity of each issuer if the environmental objective is "CCM" and category of activity is "enabling".

CAEUT calculation function in the modelling environment:

Overview of results in the report:

EU Taxonomy Assessment (Instrument level)

The alignment results from the first step – EU Taxonomy Assessment at the Issuer Level – are adjusted in the derivation function DEEUT, based on Green certification (G_CERTF) and Taxonomy-eligibility check based on purpose (EUT_ECHK). If an instrument's Taxonomy-eligibility check is "No", then it is considered to be Taxonomy non-eligible directly, i.e. the results of Taxonomy non-eligible turnover / CapEx / OpEx (%) need to be adjusted to 100%. If an instrument has green certificate, then it is considered as Taxonomy aligned, i.e. the results of Taxonomy-aligned turnover / CapEx / OpEx (%) equal 100%. Green flag will be assigned to a specific instrument if its Taxonomy-aligned turnover (%) exceeds 90% (“Green flag” is a threshold which can be customized by the user, in this sample content it’s set to 90%).

DEEUT derivation function in the modelling environment:

Overview of results in the report:

EU Taxonomy Assessment (Portfolio level)

Finally, in the calculation function CADBI, I calculate EU Taxonomy alignment results at the level of the whole portfolio, including proportions of Taxonomy aligned / not-aligned / non-eligible activities. I also calculate the taxonomy alignment results for different environmental objectives, including proportions of taxonomy-alignment (turnover) for climate change mitigation environmental objective for transitional and enabling activities, climate change adaptation environmental objective for transitional and enabling activities. After multiplying the assessment results at instrument level by the weight of instruments in the portfolio, the KPIs for Turnover, CapEx and OpEx can be obtained by summing the results of this multiplication.

CADBI calculation function in the modelling environment:

Overview of results in the report: the dashboards below represent the overall assessment results at portfolio level for EU Taxonomy part.

Thank you for reading this blog post. In the next EU Taxonomy section blog, I will demonstrate how the Taxonomy alignment calculations are performed at a company level.

SAP Analytics Cloud Performance Benchmark Tools

2022-06-23T11:27:21+02:00

Introduction

SAP released the Benchmark Tool for analysing your system performance as part of the performance analysis tools with Wave 2022.08 and 2022.Q2 QRC Release.

The Performance Benchmark helps to measure your client hardware score and network speed. These have a measurable influence on your SAP Analytics Cloud user experience.

How-To

Accessible for all users per default under System/Performance/Benchmark Tool

With this you are able to run a performance test not on your stories or applications but on your client or network.

Every user is able to perform their own client and network performance test here to determine system benchmark score and network latency and their bandwidth.

Network performance and ping rate have a significant impact on Stories and Analytic Applications loading times and it is advisable to keep an eye out for these.

Client Benchmark Score can be influenced by CPU intensive programs, applications on your client as well as firewalls, background downloads or business conferencing tools etc.

Score determined by SAP would be:

The Number can be improved by turning off CPU intensive applications, business conferencing tools etc. while loading the SAC stories and analytic applications.

Conclusion

This can be helpful if a story/analytic application which usually runs rather quickly, suddenly slows down significantly or if endusers report slow stories/dashboards performance which have a good performance on your own client.

Sources: https://help.sap.com/doc/00f68c2e08b941f081002fd3691d86a7/2022.8/en-US/6ae71d6ad2eb4622b0ef9feef61efba4.html

https://blogs.sap.com/2022/04/01/sap-analytics-cloud-performance-benchmark/

SAP Analytics Cloud: Workspaces

2022-06-23T11:43:02+02:00

General

SAP Analytics Cloud is a powerful frontend and offers multiple options to collaborate between users.

Creating stories in SAC can be done in the user individual folders and then shared with specific users or teams.

Furthermore, there is the public folder structure where users are authorized to see/create/change stories depending on their authorization on folders via team assignments or individual assignments.

And then there is the possibility of creating virtual workspaces.

Virtual Workspaces can be set up to collaborate between different teams outside of the public folder structure but within the confines of a shared workspaces.

It is possible to create workspaces according to the company hierarchy structure.

For example: Workspace Finance contains the folder structure according to the company structure for the Finance Department with folders like accounting, controlling, ledger a.s.o.

Teams are then assigned to the workspace and are able to share stories within the workspace Finance. The teams assigned to the workspace are able to see everything that is shared within the workspace.

Creating Workspaces

In order to create workspaces, a user has to be authorized accordingly. The user becomes the Administrator, who can manage and delete workspaces.

After setting up the authorization the admin user is able to go into the Workspace Management

And create a new Workspace

Afterwards it is important to set-up the workspace. Give a Name and Description

Assign the teams that should have access to this workspace

Assign specific Workspace Administrator

By assigning a user as a workspace administrator, the assigned user can organize the workspace content into folders, edit or delete files, and move content in and out of a workspace without relying on privileges.

Workspace administrators are also given read-only rights to the Workspace Management area. There, they can view the details of the workspaces they are assigned.

These are able to admin within the workspace but are not able to delete the whole workspace in general.

For example, there are 3 workspaces available

But only 2 are visible at the user interface according to the team assignments.

Working with Workspaces

Roles

In order to understand the different roles within the workspace set-up, below is an overview of the different roles*:

*https://help.sap.com/doc/00f68c2e08b941f081002fd3691d86a7/2021.20/en-US/aa613f0fc51f401ab1a2cdf47708c0d9.html

Development

In order to create stories and models within Workspaces, select the workspace when creating a new story. The Story is then saved within the specific workspace. Stories and Models created within the workspace are visible to the users who are assigned via their teams.

Furthermore, when trying to select the model for a specific story, SAC only shows the models which are available within the workspace without having to click through any other folder structures.

Move

Moving files from the public folder to a workspace is ONLY possible via the System folder and NOT via My Files.

If you want to move a story/model from the public folder to a designated workspace, go to System.

Select the story from the path leading from System/Common/Public … and then the subfolder beneath and go to Move To..

SAC will show you not only the Common/Public areas but also the Workspaces for selection.

Transport

Transportation is done via the Content Network Storage

Where you are able to select specific from the workspace area

Note: since I am an administrator, I can see all the workspaces but if you are only assigned to one or two workspaces, only the ones your team is assigned to, are shown.

It is possible to transport a single story from a workspace or the whole workspace.

Deletion

It is important to mention, if a workspace is deleted, everything within the workspace is deleted as well and forever lost. Use with extreme care!

Final Thoughts

Overall, it is a possibility to bring structure into your SAC space and prevent an overflow of the public folder.

Furthermore, developing within the workspaces is a bit more practical then within the usual public folder structure and can be shared with other developers and afterwards published to the public folder structure or other workspaces. Also for transporting developments, Workspaces can eliminate collecting difficulties since you can transport whole sections of one tenant workspace to the other tenant with one collection within the workspace.

It could be a good idea for 1-Tennant Clients with a big user structure if set-up from the start.

If you have an existing working public structure already, it is not necessary to change to workspaces, since it’s quite tricky to get the stories to the workspaces and requires time and effort.

Data Catalog with SAP - Quo Vadis?

2022-09-17T12:56:55+02:00

Update 03.11.2022 - current roadmap items for data cataloging

As mentioned in my Data Fabric blog, the data and analytics landscape in companies become more and more distributed and decentralized concepts as Data Mesh are on the rise. If we see data as an asset, as the new oil or want to make our company data driven, tools to get an overview are more and more important.

Data Catalogs could be the kind of software delivering what is needed here.

A from my perspective good definition of Data Catalog is from Gartner:

"A data catalog maintains an inventory of data assets through the discovery, description and organization of datasets. The catalog provides context to enable data analysts, data scientists, data stewards and other data consumers to find and understand a relevant dataset for the purpose of extracting business value."

- Gartner, 2017

Now if you screen the market you find a lot of - often marketing-driven - terms, concepts and meanings here. Some speake about Data Discovery as a main task to gain value from (distributed) data. Lately I have seen a study challenging the term Data Intelligence, what is seen from some vendors as an advancement of Data Catalogs.

In practice introducing a data catalog in a company can be a high effort depending on sources, complexity of data assets, people skilling up and bring into new roles (Data Owner, Data Stewardship, …), processes for data validation and data protection and so on.

A lot of companies start just with a spreadsheet or tools like MS OneNote to provide information about there data and analytics assets in the company. And this could be sufficient depending on your complexity and organization.

On the other hand we have solution specific metadata handling and search capabilities and specialiced solutions. If we have a look into the SAP specific portfolio we find different approaches from an (non-technical) user perspective:

Fig. 1: Overview of end user metadata usage in SAP's Data & Analytics solutions

For sure there are further solutions working strongly with metadata like SAP Information Steward or SAP Power Designer. They are relevant in this context but more bound to a specific group of users like Data Stewards or Data Modelers.

Maybe you know SAP communicated new developments integrated into SAP's cloud-based Unified Data & Analytics Solution Portfolio. Currently there is some discussion about project Data Suite to redesign the full portfolio and break it down into a more flexible, service-based offering. Maybe on effect we could see is the usage of Data Flow capabilities from SAP Data Intelligence into SAP Data Warehouse Cloud. But not much further information is public available here. If we have a look on

SAP Analytics Cloud

SAP Data Warehouse Cloud

SAP HANA Cloud

SAP Data Intelligence Cloud

all these solutions have own approaches, terms and concepts to handle data assets today. Therefore to bring that together for a unified management with a streamlined Data Catalog-approach could be interesting. The last information I found is:

Fig. 2: ONE Catalog Vision of SAP

From time to time I have a look into the roadmap for solutions. You can found that for the current Data Catalog offering within SAP Data Intelligence (DI) there is not much on the roadmap but the tighter integration of capabilities with DWC, SAC and S/4HANA. But my understanding is, DI Data Catalog capabilities will be transfered to the new Data Catalog solution over time. The roadmap announces the Business Data Catalog based on SAP Data Warehouse Cloud:

Fig. 3: SAP road map for Data Cataloging capabilities in SAP Data Warehouse Cloud

This means for 2023 we can look forward for new features, functionalities and the chance of a better usage of our data assets in an overarching approach using a new Data Catalog solution. This approach seems to focus on SAP solutions especially those in the cloud. But let's see what comes.

Update 03.11.2022 - new items and differentiation in the roadmap

For Data Catalogs, many things has to be considered. Companies not only have SAP systems and there are a lot of specific Data Catalog products out there. SAP is not going into a blue ocean.

What SAP ever did best was to integrate SAP with SAP. The question is, will this be enough? Are there approaches like leveraging add-ons and partnerships like SAP already does with BigID or is possibly a concept of Catalog of Catalogs via open interfaces a doable way?

What is your perspective? What is your approach of keeping an overview of your data and analytics assets in your company to create value from data?

Discover the SAP Cloud ALM Analytics API

2023-01-13T09:00:25+01:00

To learn about and experiment with the SAP Cloud ALM Analytics API , you can check out this Postman Collection.

You just need to retrieve your apiKey from SAP API Business Hub to try the OData and REST interfaces with the Postman client.

Another way to discover the interface with the mouse is to use the SAP ALM Plug-in for Grafana with a data source pointing to the SAP API Business Hub Sandbox.

The SAP API Business Hub's sandbox for the SAP Cloud ALM Analytics API is the SAP Cloud ALM Public Demo Tenant.

Customer receipts automation through payment service provider integration

2023-03-29T01:22:10+02:00

This document explains how to create an inbound interface between payment service providers and SAP. The solution focuses on the automation of customer payment transactions made via various payment methods on the client's e-commerce website.

This solution would be cost-effective and appropriate for customers who run their businesses in SAP ECC.

Business case:

Key Drivers for the automation of the process: -

When processing the transaction into SAP, the client followed a manual process that was prone to errors.

The Finance Business user logs into the payment provider website (PayPal, WorldPay, Amex & Klarna) and downloads a file containing payment transactions from a particular date, converts the file into a WinShuttle format, and then loads those transactions into SAP. A file downloaded today would contain sales from yesterday, since the user tends to check for and process the files on Mondays and Fridays, but with additional sensitivity around month-ends.

Before uploading the files into SAP, the user must follow certain steps to manipulate the files downloaded from the payment provider's portal.

The unloadable file should include a few standard transaction codes of the payment providers.

Manual processing and file manipulation would depend on the solution designed to process the uploaded file.

Payment transactions will be posted by the process file, calling SAP transaction code F-28, debiting the bank GL account and crediting the customer account. This process is not the same for all other payment providers. The receipt amount is net of the fee paid to the payment service providers.

Additional transactions such as refunds, chargebacks, chargeback fees, etc, will also be captured and posted into finance along with receipt postings.

Key Rationale:

Reduce manual effort and remove risk of human error: The incoming transaction records are processed, manipulated and transformed into a loadable format with minimal human effort.

Provide future-proofing in the event that sales transaction volumes increase.

Provide a daily stock reconciliation process similar to the one already in place for the various integrated 3PLs.

Improve stock and payment reconciliation (removing write-offs where data cannot currently be reconciled) by aligning BRUSA with Business Intelligence processes, especially in capturing data at the order level (rather than at the summary level).

Components of Integration –

In order to automate and integrate the Payment service providers with SAP, the development of SAG, PI, and SAP ECC is proposed.

SAP PI can be integrated with the payment service provider's portal to retrieve transaction files from their SFTP folders. Based on the country in which the transactions were originated, the PSP will make the files available at pre-defined intervals and they will be transmitted to SAP AL11 folders for creation of Idocs.

PI (Process Integration) integrates with SAP to process transaction files automatically.

Through context types and mapping of source values to target values, the value mapping table can be used to derive certain important target parameters into the inbound interface. When mapping a transaction file to an Idoc, SAP PI will perform a lookup.

Duplicate check functionality to prevent processing of transaction files more than once. In order to store all processed files, a bespoke table was created, and before processing any file, PI checks the bespoke table to see if any other files with the same name exist. If PI identifies a duplicate file, the file will not produce an IDOC.

PI -

Sender File Adapter with Content Conversion to maintain node names and field names as per the Payment Transaction file.

Create Structure of the file in Enterprise Service Repository with field names based on the File Content Conversion Parameter defined in Integration Builder.

Create Mapping transformation (Node & Field level mapping logics) from Payment Transaction file to IDOC structure in Enterprise Service Repository.

Create Integrated Configuration with IDOC Receiver Adapter.

Value Mapping –

In Payment providers integration, the value mapping functionality plays a pivotal role in converting a parameter from a source format to target value format to populate the parameters into the FI Idocs.

Context types are created to group the source information on the origin of the data

In the current model, the value mapping replication happens from source system to the target PI (Process integration) system.

File Formats -

The payment transaction file from each payment service providers will be distinct.

A complex data mapping should be designed, to read, transform and translate the source file format into a target application document (SAP Idoc & SAP document).

Each field and column of the payment transaction files provided by the payment providers should be read by the data mapping design.

Payment files supplied by payment providers could be in any format, for example, CSV or TXT.

PSP file formats are very distinct, so it is preferred to have separate PI mapping logic for each file format from the PSPs (ex. PayPal, Worldpay, Amex, Klarna).

Each field in the transaction file should be translated and populated, into the fields of the Idoc segments.

Because the fields are interpreted differently by each payment provider, the data mapping will be complex.

A “unique transaction type or event type code” identifies each transaction in the file, which is different for each payment provider.

PSP portals and SFTPs create different file formats, and payment providers are very reluctant to modify the formats.

Despite the apparent simplicity of the overall solution design, mapping the fields and realizing the business need will be extremely challenging.

Business Impact: -

Through the integration of payment service providers, the customer receipts process will be automated, reducing the manual effort in clearing receipts items during month-end closing by matching the same reference field from the O2C sales order automation.

The financial impact on recording the receipts, tax and fees is unique to the payment service providers.

Users can reconcile the total receipts posted through automation with those in the file downloaded from the portal.

When a PSP sends a file with new transactions, it may include a new event code that has never been mapped to a business rule in PI. To identify and post correction journals by the business, we have designed a solution to post to the same bank GL account with unique line-item text.

S/4 Hana Road map (SAP Digital payments Add-on solution): -

The SAP digital payments add-on integration offers an out-of-the-box alternative to current custom payment service provider (PSP) integrations. This integration makes use of SAP's digital payments add-on with ready-to-use PSP connectivity.

Summary: -

By establishing an inbound interface between payment service providers and SAP, customer payment transactions can be automated in a simple manner, especially for customers who run their businesses in SAP ECC.

Stop Working, Start flowing with SAP SAC Workflow Management: Input Task

2023-06-08T00:03:57+02:00

I am Monica Elam Parithi, a Senior SAP Consultant working in SAC and ByD.

In this article, I would like to provide comprehensive insights into the “Input Task” in SAC.

We will generate an Input Task rather than share the version with each responsible member and risk getting out of track.

In most cases, we share the version with responsible members and don’t know which task is performed by which member. This is where the Input task plays the key role.

What is an Input task?

Input Task is used to obtain feedback or value changes or other additional information from colleagues.

It can be assigned to one or more colleagues and can be used to work iteratively on different assignments.

Here each Assignee works individually on the assigned task in a private version so it will not disturb the actual values (Original Story remains unchanged) and will also not be visible to any other users.

Once the task is completed and after it is reviewed by the owner of the input task it will be published from the private to the public version.

What are the Prerequisites to Perform Input Task?

You need to start by creating a Story using the Planning Model

The model must have at least one dimension by which the responsible users (Assigner/Assignee) can be identified.

There must be an active Private Version to create a task.

To perform this process, the Story must have the Actual values for the data which has to be updated.

Process Flow of Input Task

Assign the responsible person in the dimension of the Data Model.

A brief example for the Input Task: Let us consider that the owner of the model needs to assign the task to “NJACOB” (the Assignee). In this case, it is the Sales Head and he wants to update the sales team’s working hours and cost and it is for that reason that he is using the “Input Task.”

Step-1:

Open the Story where the Input Task is to be created.

The Story must have the Private version for task allocation, here I have used the private version named “Input Task.”

Choose the “More” icon and select the “Create Input Task.”

Step-2: The summary page tab will be created on the left side of the story page

Specify the due date.

Enable “Cancel task on the due date automatically.” This is needed if you wish to cancel the task on the due date upon completion.

You can also add a reminder to inform the Assignee about the task.

Enable the “Models” Check box.

In “Versions,” select the private version against which we are going to assign the task.

Under “Distribution” select the Dimension against which the Person responsible is assigned in the data model.

Enable the checkbox against which the assignee is going to work. I have assigned for “Refining” and “Corporate” members.

Before clicking on “Send” make sure there are no other Versions in edit mode.

Click on “Send” (The task will be sent as mail and notified in the SAC).

Step-3: Action to be performed by the Assignee NJACOB:

The Assignee receives the task in the notification and selects the task.

Assignee will be able to see the Task for active members. Initially, the status of the task will show “In Process.”

Choose one of the “Active Members” to switch to the active Story where you wish to edit.

On selecting the member “Sales,” we will be navigated to the Sales Cost center on the Story page.

Initially the value of Sales is “0.”

The value is specified by the owner of the Cost Center “Sales”

After completing the task, click on “Submit.” Add the comment if needed and then Submit.

Step-4: The Owner of the task gets the notification in both mail and SAC. The owner opens the Input Task and that changes the Task Status to “Submitted.”

Check the values updated by the Assignee; the Assigner can either Approve or Cancel the submitted task.

Once the task is approved or cancelled by the owner of the task, the Assignee will be intimated through mail and SAC tenant.

Now the Status is changed to “Successful.”

In this case, the task is approved, and after the approval, the updated values are available in the Original Story. Now you can convert the Private Version to the Public Version, which makes it visible for all the users.

In conclusion, we can easily assign the task and track the work by the owner of the story.

Always try to simplify and streamline the work. Make it easy, rather than complicated.

Thanks for investing your time in reading this blog. Please feel free to ask a question in the comments below if you need any further information.

If you liked my blog and thought it was informative, follow me at Monica Parithi.

SAP Analytics Cloud - Changes with managing licenses (Q3 or Q4 2023)

2023-07-05T16:47:08+02:00

I'm pleased to announce many updates to my article on managing licenses with teams and roles for SAP Analytics Cloud. This update is needed because many significant changes are coming. This blog provides a high-level summary covered in-depth in my original, now updated article.

Improvements in SAP Analytics Cloud, from early November 2023:

Recent improvements:

Users can be deactivated, freeing them from all license consumption

This means no need to delete dormant users to remain compliant with SAP licensing

Introduced in Q2 QRC, May 2023, for all customers

https://blogs.sap.com/2023/04/12/sap-analytics-cloud-activate-deactivate/

Changes already now in place, effective early November 2023:

‘Named-user license entitlement’ will be enforced
- SAP Analytics Cloud will prevent the number of named-user licenses from exceeding their entitlement
- This will be enforced at the time of consuming a new license when:
  - creating named-users
  - assigning roles
  - updating team membership of roles and users

~~Planned change is coming in Q3 QRC, August 2023, for all customers~~

The planned change is for the end of Q3 or early Q4 2023, for all customers. There is no longer an alignment with the QRC release, meaning the planned change will be applied mid-way through the update cycle.The exact dates are still not confirmed, however, they might be
- ~~Thursday 19th October 2023 for Asia-Pac,~~
- ~~Thursday 26th October 2023 for EMEA,~~
- ~~and Thursday 2nd November 2023 for the Americas.~~
~~The dates remain subject to change and they could be changed without notice~~

The license entitlement is now enforced for all customers worldwide. There are no exceptions.

https://blogs.sap.com/2023/04/12/sap-analytics-cloud-license-usage-limits/

Important steps needed to prepare

Essential steps may be required in preparation for this change

Action is needed if the license limit has been exceeded, as shown in the ‘Security-Monitor’ interface
- a toast warning is also shown in the ‘Security-Users’ interface

If no action is taken:
- Existing users will still be able to log in, and any schedules will keep running
- Users will not be locked out; they can continue to log in and enjoy SAP Analytics Cloud as they did before

However:
- New user creation and team updates will fail if a new license assignment exceeds the entitlement
- For example, the following operations will fail:
  - new user creation, if there are not enough available licenses
  - any role-to-user, or role-to-team assignment change, if there are not enough available licenses
  - any team change, if any user of that team requires a license that isn’t available

What named-user license enforcement means

Previously, SAP Analytics Cloud allowed organisations to consume more licenses than those entitled
- For example, you could create 100 named users on an SAP Analytics Cloud Service that only had 50 named user licenses, although a warning would be shown in the System-Monitor screen

Previously, this meant you, the customer, had the responsibility to prevent over-provisioning

These changes mean
- SAP is taking on the role of preventing the over-provisioning of license entitlement
- Workflows that would over-provision the license entitlement will now fail, whereas before, they would not

Scope

Scope of the changes:

Applicable for all Enterprise Edition service types:
- i.e. regular production service/tenant (non-Test), Test & Test Preview
  - A regular production service (non-Test ) can be used as ‘Dev’, or ‘QA’.
- Makes no difference if the service is ‘public’ or ‘private’

It is only named users, this is not applicable for users with a ‘Business Intelligence concurrent session’ license

Applicable for all forms of user management
- Using the user interface, including the ‘import-users’ file option
- Includes ‘dynamic user creation’ available with SAML SSO
- SCIM API and user provisioning jobs

Not applicable for the Business Technology Platform Embedded Edition
- This is the cut-down edition and provides live connectivity to SAP HANA Cloud only
- This edition has a fixed 150 concurrent user session limit

To understand more about Test and regular production service/tenant (non-Test) services please visit the blog https://blogs.sap.com/2020/04/20/sap-analytics-cloud-landscape-architecture-life-cycle-management/#MRPT

Resolving exceeded license limits

Any currently exceeded license can be resolved either by

Deactivating users, which means they don’t consume a license

Assigning users so they consume a different license not yet at capacity
- Any users consuming a ‘Business Intelligence’ license could be assigned a ‘Business Intelligence concurrent session’ license if you have one of these, now legacy licenses. There are no limits to the number of users that can be assigned such licenses
- Users currently consuming a Planning license could be assigned a ‘Business Intelligence’ license

Deleting users

Or, for some services and license types, add more licenses by purchasing them from SAP

Understanding new license consumption rules

There are new license calculation rules

And these rules that govern license consumption may not be entirely obvious

For example, some licenses may need be consume a higher license type
- For instance, in this example, there are 11 Business Intelligence licenses assigned
- This 11 exceeds the 10 licensed by 1
- Since there are Planning Standard licenses available, and given such licenses include Business Intelligence licenses, this excess of 1 license can be consumed from the Planning Standard license entitlement as only 2 of the 5 are assigned, making 3 available

Comprehensive details and many more examples are provided in the updated article

Current errors may disappear after the update

These new license calculation rules are not applicable before the named-user license enforcement

This means existing warnings could disappear since the new calculation rules can consume a license from a ‘higher’ license type
- In this instance:
  - There are 100 ‘Planning Professional’ licenses, of which 46 are consumed, leaving 54 available
  - There are no ‘Planning Standard’ licenses
  - The screenshot shows an ‘error’ even though there are 54 ‘Planning Professional’ licenses available
  - Once the new rules are applied, this 1 ‘Planning Standard’ assigned license will be consumed from the ‘Planning Professional’ license type resolving the error message

Important next steps

The article ‘Managing License with Roles and Teams’ has received a significant update to reflect these changes
- This updated article strives to answer all your questions, or possible concerns this blog might raise
- All the thinking has been done for you
- All you need to do is read this article carefully and take the appropriate action

It provides comprehensive details, including:
- How licenses are assigned to users
- What the default license is, and how is it consumed when no roles are assigned
- How licenses are calculated and examples of consuming ‘higher’ licenses
- License assignment examples, including the new ‘deactivate’ users feature
- Examples of user creation failure and suggested solutions due to lack of licenses
- Examples of team and user update failure including suggested solutions related to Analytics Hub roles

It has also received an update related to the Business Intelligence concurrent session licenses as there is a subtle but important change planned that is a common ask from customers. This change means that when best practices are adopted, removing previously assigned Planning roles will revert the user back to a concurrent session license, unlike before, thus saving the effort to re-assign the concurrent license

Other updates have been made to best practices to reflect subtle changes to the SCIM API, including the updated SCIM2 API that supports the PATCH method and what this means for user provisioning with SAP IPS

Finally, a significant number of new FAQs have been added, and this includes many on ‘deactivated users’

Please proceed to this blog that introduces the updated article for further information and best practices to prepare you for these upcoming changes

Many thanks

Matthew Shaw @MattShaw_on_BI

My Success with HANA Express

2023-07-20T10:38:44+02:00

Some friendly queries on embedded Analytics Data Science Machine learning made me interested in SAP HANA ML.

First my sheer chance I chose PAL (nor APL)

Hands-On Tutorial: Leverage SAP HANA Machine Learning in the Cloud through the Predictive Analysis Library | SAP Blogs

I requested advice from blog author how to try this
He suggested HANA Express

Install SAP HANA 2.0, express edition on a Preconfigured Virtual Machine
https://developers.sap.com/group.hxe-install-vm.html

Excellent link

My home PC is CENTOS8 with 16GB RAM
I prefer Linux as one can spin up a server with 24 or 32GB RAM for an experiment and after task is over delete the instance in any cloud provider like GCP AWS .. I prefer GCP Google Cloud Platform

The PAL example crashed in my own Centos8 as it faced a lot of memory problems.
though I ran VMware Linux Xwindows version
I was not keen on X-Windows on GCP as thought nested VMs may not work
Docker is not VM and is container

VMWare VirtualBox ova files can be converted to docker image

Saplabs has a docker but does not support ML
https://hub.docker.com/r/saplabs/hanaexpress

I therefore converted hxe.ova to docker image hxesave:latest using
https://medium.com/@roberto.fernandez.perez/create-docker-base-image-for-legacy-linux-system-3f5f77acd740

hxesave:latest was my image from hxe.ova

docker run -p 8090:8090 -p 4390:4390 -p 39013:39013 -p 39017:39017 -p 39041-39045:39041-39045 -p 1128-1129:1128-1129 -p 59013-59014:59013-59014 -it --name hxecnt -h hxehost hxesave:latest bash

hxecnt is the container
docker container start hxecnt # To start
docker exec -it hxecnt bash # HDB start HDB stop
docker container stop hxecnt # To stop

AFTER docker exec -it hxecnt bash
hxehost:/ # su - hxeadm
hxeadm@hxehost:/usr/sap/HXE/HDB90> source jncbash.sh # My fav bash settings
hxeadm@hxehost:/usr/sap/HXE/HDB90> cat jncstart.sh

HDB start
HDB stop
HDB start

hxeadm@hxehost:/usr/sap/HXE/HDB90> ./jncstart.sh
start-stop-start needed as some init issue stops nameserver first time

On the 24GB GCP Server Yannick Schaper's PAL example ran fine!
This was a demo of a large example to bring out the miracle of SAP Data Science!

Unknown to me there was pre-existing AFL plugin
SAP HANA AFL (incl.PAL,BFL,OFL)
Version 2.00.054.0000.1611928859

Challenge came after I tried APL

Hands-On Tutorial: Automated Predictive (APL) in SAP HANA Cloud | SAP Blogs

I did not have the APL plugin
Install Optional Packages
https://developers.sap.com/tutorials/hxe-ua-packages-vm.html

Unfortunately apl.tgz the download of APL did not work though installed OK.

I read Installing the Automated Predictive Library (APL) on SAP HANA Express 2.0
https://blogs.sap.com/2018/01/11/installing-the-automated-predictive-library-apl-on-sap-hana-express-2.0/

Downloaded official APL plugin and this worked fine.

There is a SAP NOTE 2871252 - What does AFL, APL, PAL stand for
With the picture that explains it all

AFL PAL APL Explained

Way I have understood
AFL is the library tightly coupled with the Hana Data Base
PAL and APL are the 2 libraries that have the Data Science Algorithms
PAL is part of AFL and APL is to be installed additionally
https://help.sap.com/doc/1d0ebfe5e8dd44d09606814d83308d4b/2.0.07/en-US/hana_ml.html

To summarize
HANA EXPRESS is simply amazing learning vehicle.
Thanks to SAP for sharing as you can have ScriptServer and both PAL & APL to study fast data science in HANA Express; total control

If you have GCP(Google Cloud Platform); you can create and drop VMs as you wish what size and power you wish
Just upload a docker image (saved in tar gz) and you are up and running.
Delete the server when over

90% of the time my 16GB RAM Centos8 is fine

I would encourage ALL developers to try HANA Express
Free HANA in BTP interface very nice but highly restricted
I noticed SAP now has renamed AFL as PAL in BTP

If you want to experience HANA Express in full glory do try
at least VMWARE version in Windows PRO or Linux

Sap Datasphere Data Flow Series – Introduction and sample example

2023-11-09T21:58:07+01:00

Hey Champs,

Let’s understand one of the important feature of datasphere which is dataflow, Before moving on to the topic first we have to understand the three things well i.e “What” “Why” ”How”.

This article is the first in the blog post series and introduces about dataflow and one small example that we’ll use along the entire blog series listed below:

Blog Post #1: Introduction to dataflow and example

Blog Post #2: Sap Datasphere Data Flow Series – Operators (Joins, Projection, Aggregation, Union)

Blog Post #3: Sap Datasphere Data Flow Series – Script Operator Part 1

Blog Post #4: Sap Datasphere Data Flwo series - Script operator Part 2(Still cooking)

Why:

Data flow is an exciting new feature of SAP Datasphere because it provides a visually-based modeling experience for data integration. This makes it easier to combine and load data from various sources, including structured and unstructured data. Just imagine we have BODS facility integrated here and that is awesome.

What:

Data flow allows you to create and manage data pipelines using a graphical interface. These pipelines can be used to perform a variety of tasks, such as extracting data from different sources, transforming it, and loading it into target destinations.

How:

Well this answer will not be small and simple, to understand this lets watch the full movie with me.

User Interface :

Champs lets see what all tools and option Sap Datasphere is providing us.

DataFlow User Interface

Now lets cover a simple scenario of getting data from a source as excel file and loading to a target table.

Wait.. Do we need to create target table again ?. No not at all, thanks to Sap for this wonderful feature where we just have to click create and deploy target table and automatically target table will be created.

USE CASE 1:

We got a requirement where while getting the order information from a different source table we want to add the leading zeros to the item column, so how to do it ?

let’s jump in for data flow to add the leading zeros :-

First we will create the source table using one excel file. After that we need to drag and drop the Sales order table into our play area :

Adding Source Table : Sales Order Table

Well, Now lets write one small python script to add the leading zeros. “What ? Datasphere supports python ??”. Yes, that’s correct datasphere support many python function and its still evolving.

Drag and drop script operator as shown in the below image and write the following code.

Adding Script Operator

Once we finished writing the script we have to then click on add table to create the target table.

Adding Target Table

Now once we have added the target table. Click on Target table and in the right side we will get details panel. Time being select the mode as append. And then in the top left corner just clock deploy option.

I will discuss all the rest mode types in details in further blogs.

Append: Write the data obtained from the data flow as new records appended to the end of the target table.

Now go to target table and click on data preview and its done, as we can see all the items have leading zeros.

Running the Dataflow

That's not the end, Stay tuned for more blogs which I will be adding to the Data Flow Series.

Sap Datasphere Data Flow Series – Operators (Joins, Projection, Aggregation, Union)

2023-11-17T17:49:34+01:00

Hey Champs,

Hope you enjoyed the last blog on Sap Datasphere Data Flow Series – Introduction and sample example, well its just the trailer. Today lets jump into different types of operator in Datasphere in details.

Operators in SAP Data Sphere are used to manipulate and transform data. They can be used to perform a wide variety of tasks, such as filtering, sorting, aggregating, and joining data. Operators can be used in both data flows and data jobs.

Here are some of the most common operators in SAP DataSphere:

Projection Operator

Join Operator

Union Operator

Aggregation Operator

Script Operator (We will cover in a separate blog)

Join Operator & Projection Operator in Data Flow:

In SAP Data Sphere, the Join Operator is used to combine data from two or more sources based on a common column between them

There are several types of joins available in SAP Data Sphere, each with its own purpose:

Inner Join: This is the most common type of join, and it combines data from two sources where the values in a related column match. Only rows with matching values are included in the result dataset.

Left Join: This join includes all rows from the left source, regardless of whether there is a matching row in the right source. If there is no matching row, the values for the right source's columns are displayed as null.

Right Join: This join is similar to the left join, but it includes all rows from the right source instead of the left source. If there is no matching row, the values for the left source's columns are displayed as null.

To use the Join Operator in SAP Data Sphere, you can drag and drop it onto the data flow canvas, connect it to the desired data sources, and configure the join type and join condition. The join condition specifies the related column between the sources and the values that must match for rows to be included in the result dataset.

Projection Operator:

It allows us select the required fields into the output. For example we have 100 columns in a table and we want to see only 50 columns so in that case we can use a projection operator and select our required fields.

Lets see one example where we are join two dataset named Order Table and Order Item table.
We can combine Order table and Order Item table using Order id as join condition between two dataset.

Thoda Gyan:
The size limit for files being processed by the join operator is 10 GB.

Steps :

From the left side repository panel drag the Order header table and Order Item table to the play area as source.

Drag and drop the join operator from operator panel to the play area.

Now drag the projection operator in front of the Order header table, now connect the Order header table and projection.

Now connect a line from projection and Order item table to the join operator.

Click on projection and change the Name to Customer Name. Now click on the join operator and select inner join as shown below.

Adding a projection

Now click on add target table, Add in the right side details pane click on “Create and deploy target table” . And from the general tab select the mode as append and save and activate. Now in the top left corner click run as show in the below image.

Running the Data Flow

.Aggregation Operator:

Aggregation refers to the function whereby key figure values on detail level are automatically summed up at runtime and shown or planned on aggregated level.

SUM: Calculates the total sum of values for a particular column.

AVG: Calculates the average value for a particular column.

MIN: Determines the minimum value for a particular column.

MAX: Determines the maximum value for a particular column.

COUNT: Counts the total number of non-null values for a particular column.

These aggregate functions can be applied in various scenarios within Data Sphere, including:

Analyzing sales data: By aggregating sales figures across different time periods, product categories, or customer segments, you can identify trends, patterns, and outliers in sales performance.

Calculating performance metrics: Aggregated functions can be used to assess employee performance, track website traffic, or evaluate the impact of marketing campaigns.

Identifying trends in sensor data: By aggregating sensor readings over time, you can detect patterns and anomalies in environmental conditions, equipment performance, or user behavior.

Generating summary reports for companies: Aggregated data can be used to create informative reports that highlight key findings and trends.

Steps:

Using the previous flow after Order item table, drag a projection to the play area and remove the item column from the projection. Keep those that you want to aggregate and those that you want to group the aggregations by.

Click the Aggregation tool, drag it onto the diagram canvas, and release it where you want to create the aggregation.

Click on aggregation and click the column named Item and then select the aggregation type as Sum.

Join the flow between projection, aggregation and join.

Adding the Aggregation Node

5.Now save and deploy and run the dataflow. Then click on the target table and do data preview.

Data Preview of Aggregation Node

Union in Data Flow:

Union in Sap DataSphere Data Flow is used to combine two datasets. Lets take one example to combine two dataset. In my example I am taking Order Header Table and Sales Order Table. Just to show the union functionality. I will only take order number from both the dataset.

Steps:

Drag and drop Order Table Header into the play area as source and add a projection and connect the source table and projection. Now enable the Order Id column.

Drag and drop Sales Order Table into the play area as source and add a projection and connect the source table and projection. Now enable the Order Id column.

Drag and drop the union node into the play area.

Now connect the flow of the projection to the union node

Click on the union node and map the order id of both the table. And go to the unmapped section and delete the columns which are not mapped.

Now save and deploy it and click on run to see the data preview..

Before lets look into the ingredients for the dish :

Order Header Table

Order Item Table

Now we can data preview and see the complete data.

Adding the Union Operator

Conclusion:

This blog introduced the different types of operator available in datasphere data model that we’ll use along the entire blog series, I have explained each and every topic in detail. We can do a lot more using all these operators. I have skipped the script Operator and will cover it soon with another blog.

Thanks for reading! I hope you find this post helpful. For any questions or feedback just leave a comment below this post. Feel free to also check out the other blog posts in the series and follow me to learn as well as master sap analytics. Let me know if you find something can be improved or added.

Best wishes,

Kunal Mohanty

Sap Datasphere Data Flow Series – Script Operator Part 1

2023-11-25T10:15:33+01:00

Hey Champs,

Hope you enjoyed the last blog on Sap Datasphere Data Flow Series – Operators (Joins, Projection, Aggregation, Union), well its just the trailer. Today lets jump into script operator of Datasphere in details.

The Script Operator seamlessly integrates the functionalities of popular Python libraries Pandas and NumPy with SAP Data Warehouse Cloud, enabling the creation of Data Flows and tailored information views. This versatile operator caters to a diverse range of tasks, including data cleansing, data transformation, and more.

Syntax for script operator:

-----------------------------------------------------------------------

def transformation(data):

# Fill in your scripts here with data as a source

# and save the values into data as output

return data

------------------------------------------------------------------------

Currently included libraries are restricted to Pandas, NumPy and several built-in module operations. But still not all functions are supported we can check the official documentation for the supported things.

Sap Official Documentation

Limitations of Sap Datasphere Script Operator:

We have a dedicated python editor where we can write our python code. But the only limitation is that we don’t have a syntax validator. And that’s where it hurts a lot, hope will get in future soon.

Due to the maximum batch size limit of 100,000 entries, the given Pandas function cannot be used to effectively remove duplicates. Using SQL-Script is the recommended approach for this task to ensure accurate duplicate detection, even across batches.

Limitation due to batch size

Importance of DataSphere script operator in real world:

The Script Operator in SAP DataSphere offers several valuable use cases and is crucial for real-world data manipulation and analysis tasks. Here are some key usage and advantage of the Script Operator:

Data Cleaning and Preprocessing: The Script Operator works very well in cleaning and preparing raw data for analysis. It helps the users to handle tasks like removing invalid characters, imputing missing values, standardizing data formats, and detecting outliers. This ensures data quality and reliability for analytical processes.

Complex Data Transformations: The Script Operator extends the capabilities of SAP DataSphere by enabling complex data transformations which are not feasible with built-in operators. It provides user the flexibility to perform advanced data manipulation techniques like feature extraction from text data, data enrichment through external sources, and custom aggregation functions.

Integration with Python Libraries: The Script Operator seamlessly integrates with popular Python libraries like Pandas, NumPy, and built-in modules. This allows users to leverage the extensive functionalities of these libraries for data manipulation, analysis, and exploration.

Handling Unstructured Data: The Script Operator effectively handles unstructured data formats like text, JSON, and XML. Users can write Python code to parse, extract, and transform unstructured data, making it usable for downstream analysis.

Custom Data Validation and Checks: The Script Operator facilitates custom data validation and checks. Users can write Python code to implement custom business rules, data quality checks, and anomaly detection mechanisms, ensuring data integrity and consistency.

Note : We will try to learn from use case about how we can use the python operators and get familiar with the syntax. Not all use cases will be useful. Try to get the use of the operators and how to write the code

Use Case 1:

We will always get a scenario where we want to count the number of employee for each department or number of items in a order. So in this case we will try to take a similar example where I will use script operator to find the count of different payment mode. Using this we will see how efficient is script operator and what flexibility it gives us while writing python code.

Let’s have a look at our data:

This is our order table data and using this I want to count the payment modes.

Order Table Sample Data

So we will expect some output like this.

Expected Output Script Operator

Let’s have a look at the code and understand it step by step:

Python Script to group Data

This code defines a function called transform that takes a Pandas DataFrame as input and returns a new DataFrame with two columns: 'Payment Mode' and 'Count of Payment mode'. The function performs the following steps:

Convert 'Payment Mode' column to categorical data type: The Payment Mode column is converted to a categorical data type using the pd.Categorical() function. This is useful for tasks like data analysis and machine learning, as it allows for more efficient data manipulation and representation.

Group the data by the 'Payment Mode' column: The data is grouped by the 'Payment Mode' column using the DataFrame.groupby() method. This creates a grouping object that allows us to iterate over the data grouped by the payment mode.

Create a new DataFrame to store the desired output: An empty DataFrame is created with two columns: 'Payment Mode' and 'Count of Payment mode'. This DataFrame will store the final output of the function.

Now lets get our hand dirty by entering to datasphere and using the dataflow.

Add a table as source into the play area.

Add a projection and keep only payment mode and remove other columns.

Add a script operator and write the above script and connect everything.

Add a target table and connect the script operator to the target table. Now save and deploy it

Dataflow Script Operator

Conclusion :

This blog introduced the script operator available in datasphere data flow that we’ll use along the entire blog series, I have explained each and every part of the script operator in detail. Script allow is a very handy thing to do a lot of manipulation and analysis of data. The things which we can do using sql script, the same thing also we can do using script operator but since pyhon is used as a language so its gives us the flexibility to use lots of inbuilt function to do a more operation on data. I will continue the second part of this script operator to show what things are possible easily using script operator which we can't do using sql script.

Thanks for reading! I hope you find this post helpful. For any questions or feedback just leave a comment below this post. Feel free to also check out the other blog posts in the series and follow me to learn as well as master sap analytics. Let me know if you find something can be improved or added.

Best wishes,

Kunal Mohanty

Data Flows - The Python Script Operator and why you should avoid it

2024-04-16T12:48:44.641000+02:00

Introduction

When using SAP Datasphere to transform data for persistence, the Data Flow provides the necessary functionality. We recently compared various basic transformation tasks using different modeling approaches. Therefore, we tried four different approaches to implement a certain logic:

Modelling with the Standard Operators in the Data Flow
Modelling with a Graphical View as a source to be consumed in the Data Flow
Modelling with a SQL View as a source to be consumed in the Data Flow
Modelling with the Script Operator in the Data Flow.

The goal was to give a recommendation about what approach might be the best for various scenarios in case of runtime, maintenance, other categories and if every scenario can even be modelled with every approach. We implemented the following scenarios:

String to Date Conversion
Join Data
Concatenate Columns
Aggregate Data
Transpose Data and Aggregate
Regex
Unnesting Data
Generate a Hash
Generate a Rank Column
Calculate a moving Average

Setup

To have a comparable setup, we performed this action with an identical dataset, which contains the following columns:

Region
Country
Item Type
Sales Channel
Order Priority
Order Date
Order ID
Ship Date
Unit Sold
Unit Price

We uploaded this dataset (a CSV file) into a table. The table then contained 10 million records. The reason for that is that we wanted to get a feeling how Data Flows and Datasphere handles big amounts of data.

Results and Interpretation

The outcome of our tests is now displayed in the table below. Note that the runtimes are displayed in MM:SS format, with seconds rounded to minutes if the runtime exceeds a few minutes.

Scenario	Python (Script Operator)	Standard Operator	Graphical View	SQL View
String to Date	45:00	00:45	00:58	00:49
Join	NA	01:00	00:53	00:50
Concatenate	36:00	00:52	00:51	00:36
Aggregation	23:00	00:39	00:25	00:37
Transpose and Aggregation	24:00	00:50	00:28	00:24
Regex	36:00	00:59	01:00	00:50
Unnesting Data	14:00	NA	NA	00:38
Hash	234:00	NA	01:00	01:00
Rank	40:00	NA	00:58	01:00
Moving Averages	23:00	NA	NA	00:21

For better comparison, the chart below provides an overview in logarithmic scale.

One of the first findings is that between the Standard Operator, the Graphical View and the SQL View there is not a huge difference. Given the amount of data, the performance is overall quite pleasant.

Additionally, some requirements or tasks are not feasible with the Standard Operator or the Graphical View, but an SQL View supports a wide range of possibilities.

The elephant in the room is obviously the performance of the Script Operator. The one thing which should enhance your possibilities as a developer with a currently very popular programming language does not perform in any acceptable way compared to the other options. After we did our tests, we contacted SAP support to verify one of our scenarios. We thought we missed something in our modelling approach or probably this is even a bug. Maybe we missed the “Make it fast” setting. But after we posted our incident, we got some insight from SAP Support why this is slow. Spoiler alert: We did not miss the “Make it fast” setting. The explanation for this is quite simple. When you use the Standard Operators (without the Script Operator), the Graphical View or the SQL View everything can be performed directly on the database. However, when you use the Script Operator all the data which is processed in the Script Operator needs to be transferred to a separate SAP DI cluster which will perform the Python operation and afterwards the result needs to be transferred back. In our case that is 10 million records which is almost about 1GB of data. We tried to illustrate the process based on the feedback from SAP in the picture below on a high level.

Also, the recommendation by the support was that the Script Operator should only be used if the requirement cannot be implemented with one of the other options. However, we think that how the Script Operator is advertised by SAP this can be an unpleasant surprise. Currently we see the Script Operator to be used very carefully, because in the end it might be a bottle neck in processing data during a transformation. Now one could argue that 10 million records is not something which is transferred on a regular basis in data warehouses, but we think this statement is not correct. In current SAP BW Warehouses, we regularly see the amount of data which is growing. Transferring at least 1 million records daily is not uncommon. Initially we were very excited to used Python, but currently we would generally advise against its use unless absolutely necessary. Even then, be prepared for potential performance issues during the runtime of your Data Flows.

Conclusion

To reiterate, the primary takeaway is the recommendation to avoid using the Script Operator in a Dataflow. Due to our test and the incident we submitted to SAP, we gained insights into how the data is processed in the background. We also searched to find if SAP provides this information already somewhere within the Datasphere documentation but could not find it. This might be helpful to gain a better understanding. It might be slightly misleading how the Script Operator is advertised. It's important to be aware of its limitations, making SQL the preferred option for now.

SAP Community - SAP Analytics Hub

SAP: Future of Analytics webinar Sept 8th. Your questions answered

What is the difference between SAP Analytics Catalog and SAP Analytics Hub

SAP Analytics Cloud vs S/4 Hana Embedded Analytics

Is it Possible to connect Smart Predict to SAP HANA tables and SQL Views?

Does SAP Analytics Cloud for planning replace SAP BPC?

Is data replicated to SAP Analytics Cloud when using a Live local connection to SAP S/4HANA?

How does the SAP S/4HANA embedded analytics architecture look like?

Commenting in SAP Analytics Cloud

Commenting in SAP Analytics Cloud

SAC Commenting workflows :

Story Commenting

Supported:

Not Supported:

DataPoint Commenting

Supported:

Not Supported:

Comment Widget

System Configurations :

More Links

Support and Empowerment for your SAP Analytics projects

How can value maps support you with your SAP Analytics project?

SAP Analytics Cloud, Analytics Catalog, a new opportunity to centralize and easily access all types of analytical content

Preliminary remark: To simplify reading, SAP Analytics Cloud, Analytics Catalog will be called SAC Analytics Catalog throughout the document.

Analytics Catalog

Future

SAP Analytics Cloud: Technical Guide

Unleash the value of your data with Analytics at SAP TechEd in 2021

Table of contents

1. Why should I attend SAP TechEd?

Go back to Table of Contents

2. Analytics at SAP TechEd in 2021

Go back to Table of Contents

2.1. Channel 1 - Data Warehousing & Analytics

Go back to Table of Contents

2.2. Discover Enterprise-Ready Data Warehousing

Go back to Table of Contents

2.3. Making Strategic Decisions with SAP Analytics

Go back to Table of Contents

2.4. Applying Analytics End to End

Go back to Table of Contents

3. How to register and further information

Go back to Table of Contents

SAP Analytics Cloud - Removing Concurrent Session licenses and other updates around managing licenses with roles and teams

Introduction to EU Taxonomy

SAP Analytics Cloud Performance Benchmark Tools

Introduction

How-To

Conclusion

SAP Analytics Cloud: Workspaces

General

Creating Workspaces

Working with Workspaces

Roles

Development

Move

Transport

Deletion

Final Thoughts

Data Catalog with SAP - Quo Vadis?

Discover the SAP Cloud ALM Analytics API

Customer receipts automation through payment service provider integration

Business case:

Key Drivers for the automation of the process: -

Key Rationale:

Components of Integration –

PI -

Value Mapping –

File Formats -

Business Impact: -

S/4 Hana Road map (SAP Digital payments Add-on solution): -

Summary: -

Related topics –

Stop Working, Start flowing with SAP SAC Workflow Management: Input Task

SAP Analytics Cloud - Changes with managing licenses (Q3 or Q4 2023)

Improvements in SAP Analytics Cloud, from early November 2023:

Important steps needed to prepare

What named-user license enforcement means

Scope

Resolving exceeded license limits