SAP Community - Data and Analytics

Horizon Theme and Templates for Stories in SAP Analytics Cloud

2024-04-05T17:10:07.510000+02:00

A new theme, templates, and sample story are now available in the SAP Analytics Cloud Community Content on GitHub to enable you to apply the Morning Horizon style to your stories in SAP Analytics Cloud!

The Morning Horizon theme is an evolution of the SAP Fiori design language, aimed at providing a more coherent and consistent user experience across SAP's product portfolio. It is designed to be simple, intuitive, and efficient, helping users focus on their tasks with minimal distraction.

Sample Story Based on the Morning Horizon Theme

In the sample story provided as part of the template package, you can see how a story will look like using the Morning Horizon theme, which includes:

Cards with rounded corners and drop shadows that group together related information;
Consistent headers and controls to standardize the experience across elements and pages; and
A clean and minimal palette, with lots of white space, where color is used sparingly to highlight key information and reinforce semantics.

On the first page of the sample page, you can see the appearance of widgets that display data, such as charts and tables, and containers like panels. On the second page are all the filter controls, the comment widget, and other elements with the theme applied.

Morning Horizon – Sample Story

Morning Horizon Theme for Stories in SAP Analytics Cloud

The theme defines all relevant styling aspects of the widgets in SAP Analytics Cloud, so you, as a story designer, can focus more on your data rather than on fine-tuning the colors of your widgets. For some aspects, you will need to apply a CSS class to widgets by adding the CSS class name in the Styling Panel.

A detailed CSS guide for the theme is available on our SAC Dashboard Design Best Practice page here: https://dashboarddesign.frontify.com/. After you have registered with your email address, you will receive an invitation to the guide. The information on the CSS classes themselves can be found under the top level navigation item "Optimized Story".

You can use following CSS classes for content cards with shadow, which can be applied to Panels and Flow Layout Panels.

card

You can use following CSS classes for different Button use cases:

acceptButton
rejectButton
emphasizedButton
liteButton

You can use following CSS classes for different Text use cases:

pageTitle
pageSubTitle
descTitle
descContent
link
negative
critical
positive
information
neutral

Story Templates Based on the Morning Horizon Theme

We provide two templates to enable you to quickly apply the Horizon Theme to your own stories, ensuring a consistent look and feel across your SAP Analytics Cloud content and between it and other SAP applications.

The Report Template gives you the layout for a structured reporting page. The header summarizes high level the key message of the page with a title, short description, and overarching KPIs. Below that, you can place detailed breakdowns on the logical groups that visually group information on cards. On the second page of the Report Template is a layout with the overarching information on the left-hand side.

Report Template

The Exploration Template provides a structure to create a story for an exploration use case. It also starts with overarching information on the top and then provides one detail area, where you can create charts that go more in detail from left to right. You can then link the charts using the Linked Analysis feature (not part of the template) so that you can explore your data in more detail.

Exploration Template

Download Now!

You can download the package with the from GitHub:
https://github.com/SAP-samples/analytics-cloud-datasphere-community-content/tree/main/Technical_Samples/SAC_Fiori_Horizon_Theme

Once you have downloaded the package, you can import it to your SAP Analytics Cloud system using the Transport feature. Once imported, the templates will appear on the Welcome to Stories page.

You can then create a new story based on the template in the Optimized Story Experience. In the editing environment, click on the placeholders and add your model to the story. You can then continue by defining the data that you want to show on each placeholder.

We’ll provide more information in a later blog on how to make larger changes to the default layout.

Best Practices When Applying a Theme to Stories

In SAP Analytics Cloud stories, there are three options that can influence the look and feel of story widgets:

Theme, for styling the entire story;
Widget styling, to define the style for individual widgets via the Styling Panel; and
CSS class, for applying more granular styling to widgets than you can achieve via the Styling Panel or Theme settings.

In the story runtime, these settings are applied to the story widgets with the following priority order:

CSS classes that are defined in story-level CSS file
CSS classes that are defined in theme file
Widget local styling (via the Styling panel for each widget
Widget style setting defined in the theme

Based on these rules, if you would like to apply the theme to your story, you need to consider the following aspects:

For new stories, avoid adding any specific widget styling in the Styling panel; stick with the default settings.
For existing stories, reset all the existing widget styling to the default value and then apply the theme.

Local widget styling MUST be set to the default value so that the theme style settings can be successfully applied to the story, as the theme has a lower priority compared to the widget styling that is locally done via the styling panel.

SAP Datasphere: Using Variable derivation for currency conversion measures within Analytic Model

2024-04-05T20:50:21.666000+02:00

Introduction

As part of this blog, I would like to introduce you to the new variable derivation feature in Analytic model within SAP Datasphere for currency conversion measures, as mentioned in the SAP Help documentation.

Before we start, you may be wondering what is variable derivation and why do we need this?

For example, let say we have a sales report where users would like to see Net Amount in the currency of their choice and it is preferred by them to choose a country to fetch the respective currency using a flexible lookup logic instead of them knowing the currency codes. For building the flexible lookup logic, an independent lookup entity having country-currency relationship can be used. Also, further in the lookup entity, extra features like language-dependent value help for countries can be added later, as well.

To realize the above scenario, 4 main components will be needed.

Country variable - The one filled by the user, optionally with value help.
Currency variable - It's configured to derive the value for itself by feeding a lookup entity with the value of the country variable.
Lookup entity - Any table or view can be used here, but typically this would be a view with input parameter, either graphical or scripted in SQL or SQL script.
Currency conversion measure - The measure that uses the currency variable value and converts a given currency amount, like sales value, to that very currency.

Fig a: User Workflow

The high-level user workflow would be as shown above and it provides the following advantages:

Variable Reusability within Analytic Model: Same currency conversion variable can be used for other currency conversion measures within the same Analytic Model.
Lookup Entity Reusability across Analytic Models: The lookup entity can be used in other Analytic Models as well; in case they have a similar need.
Centralized Governance: If a country changes its currency like back when all countries in the Euro-zone gave up their own currencies, then such a change could be centrally managed in the lookup entity.
Flexibility: Since, lookup entities can be freely developed as graphical or SQL views, the full power of SQL is at your disposal for derivations. Also, chaining of derivations can be performed.

Obviously, a country->currency lookup is very simplistic, but lookups can be lot more complex. Some scenarios are described below:

Use the user identity from the session context to derive their cost center, subsidiary etc.
Perform complex date calculations like start or end of current month or quarter, # of days since start of year etc.

At this point, only source variables and reference date variables can be derived, but SAP has some interesting enhancements in the works that will over time boost the power of variable derivations even more. Our vision is to extend the variable derivation for the other variable types, namely Restricted Measure & Filter variables, as well. And, further, introduce the same concept for Calculated Measures as well.

Please note that here we are just sharing the vision, hence not committing or liable to any new feature release.

In the next sections, we will look at the inner workings of the country->currency lookup example.

Prerequisites

For Currency conversion, please make sure you have currency conversion tables available within your space as mentioned in the SAP Help Documentation.

Overview:

In our scenario, we will be using the derived source variables within Currency Conversion measures in Analytic Model to get the derived value for Target currency and also, for Reference date from two separate lookup entities, as shown below.

Fig b: Currency Conversion Measure

These values will be derived using a source variable in the Analytic Model where we have an option to use the Derive value, as shown below.

Fig c: Source Variable - Derive Value

Variable filled by – Dropdown to select between the option Manual Input and Derive value.
Lookup Entity – Using help icon, a relevant lookup entity can be chosen from the pop-up dialog. Any graphical or SQL view as well as any table in the space can be used. Most likely, the typical case will be a view with input parameters.
Result Column – This is the result or referenced column from the lookup entity. For now, only a single value can be returned.
Lookup entity Parameter Mappings – In the standard case of a lookup entity with input parameters, these can be mapped to other source variables (e.g. Country variable). They could theoretically also be set to a fixed value, but then derivation would always derive the same value, so this will rarely be useful.
Used In - Once the variable is used somewhere, like for example in a currency conversion measure, this usage is listed here.

So, from above, we can categorize the whole process within Analytic Model into below high-level steps.

Step 1: Create Lookup Entities

In this step, we will create two lookup entities - one for translating country to currency and another one for translating a human-readable time representation (today, yesterday, same day 5 years ago etc.) into a proper date.

1.1 First lookup entity, MCT_Lookup_Currency_From_Country, is a simple view containing 2 columns – Country and Currency.It stores data for the country->currency relationship like USD for US.

Fig d: First Lookup Entity - MCT_Lookup_Currency_From_Country

Further, it has an input parameter Country for filtering the data on the Country column, as shown below.

Fig e: First Lookup Entity Country Filter

The source of this view is a simple table MCT Country, as shown below.

Fig f: First Lookup Entity Source

1.2 Second look up entity, Date_Lookup_Entity, has an input parameter Date selection and is having the SQL view Date_Lookup as its source, as shown below.

Fig g: Second Lookup Entity - Date_Lookup_Entity

And this input parameter Date selection is further used to filter the ID column, as shown below.

Fig h: Second Lookup Entity Date Filter

Further, source SQL view Date_Lookup uses SQL Date and Time functions like CURRENT_DATE etc. to create a view with the date and time related data, as shown below.

Fig i: Second Lookup Entity SQL View

Moreover, this SQL view is created on the text table Date_Texts which has below simple structure and text data inserted into it using Data editor, as shown below.

Fig j: Second Lookup Entity Source table

Step 2: Create Source variables for User Input

In this step, we will create the variables to prompt the users for a country as well as for a readable date placeholder (like Today, Yesterday, Start of Month etc.).

2.1 Once the lookup entities are defined, please go to the respective Analytic Model and define the source variable, COUNTRY, as shown below.

Fig k: Source variable Country

2.2 Then, create another source variable, DATE_SELECTION, as shown below.

Fig l: Source variable Date

Step 3 – Create Source Variable for Variable Derivation

Now, we come to the core of this blog - the variable derivation. Here, we create two source variables and configure them such that they derive their values by help of the lookup entity and the prompt value provided by the end-user

3.1 Next, create derived source variables, CURRENCY_REF_DATE, as shown below.

Fig m: Derived Variable - CURRENCY_REF_DATE

Here, for the lookup entity use the view Date_Lookup_Entity, created in step 1, and result column as Date.

Then map the input parameter, Date selection, of the lookup entity to the source variable, DATE_SELECTION, created in the previous step, as shown below.

Fig n: Derived Variable Lookup Entity Parameter Mapping

Note that we could also create a new variable on the fly rather than reusing the variable DATE_SELECTION that we create earlier.

3.2 Next, create the source variable, TARGET_CURRENCY_FROM_LOOKUP, to derive value for target currency, as shown below.

Fig o: Derived Variable - TARGET_CURRENCY_FROM_LOOKUP

Here, for lookup entity use the view MCT_Lookup_Currency_From_Country, created in step 1, and result column as Currency.

Then map the input parameter, Country, of the lookup entity to the source variable, COUNTRY, created in the previous step, as shown below.

Fig p: Derived Variable Lookup Entity Parameter Mapping

Step 4 – Create Currency Conversion Measure

By now, it's time to make use of our derived variable in a currency conversion measure.

4.1 First create a Currency Conversion measure, as shown below.

Fig q: Currency Conversion Measure

5.2 Next, in the measure definition for Target Currency and Reference Date choose the derived source variables created in previous step, as shown below.

Fig r: Choose Derived Variables

Once done save and deploy the Analytic Model.

Step 5 – Analytic Model Preview

So, we're done building things and now, let's test things.

5.1 As a final step, please go to the Analytic Model Preview and enter the value for Input Parameters Country and Date selection as US and TODAY, respectively.

Fig s: Variable Screen

This will pass these values to the lookup entity and fetch the Target Currency and Reference Date to be used within the currency conversion measure to provide us the desired result.

Fig t: Derived Variable Lookup Entity Parameter Mapping

Summary

In this blog, I showed you how to use the currency conversion measures with the derived source variable.

Please make use of this cool new feature and let us know your valuable feedback.

Best wishes,
Jai Gupta

Find more information and related blog posts on the topic page for SAP Datasphere .

SAP Master Data Rated #1 for User Satisfaction & Market Presence by G2

2024-04-05T23:17:18.174000+02:00

We are thrilled to share that SAP Master Data Governance (MDG) has once again been named a Leader in the Spring 2024 G2 Grid Report for Master Data Management (MDM).

Satisfied Users

Critical to our legacy of success, SAP MDG stands at the top in terms of Market Presence in the G2 MDM category. This presence attests to not only the wide adoption of our solution, but also to our unwavering commitment to provide high-quality data governance capabilities across numerous data domains, use cases, implementation styles, and industries across the globe.

A true testament is customers' satisfaction with SAP MDG, the 100% Satisfaction score awarded us the top spot in the G2 MDM Grid. In addition, 97% of users on G2 have rated SAP 4 or 5 stars – an overall approval rating covering support, usability, functionality, administration, setup, and business relationship - that also speaks to the quality and reliability of the SAP MDG solution and services.

Steering in the Right Direction

And we continue to innovate, with 96% of G2 users echoing their belief that SAP MDG's product direction is going in the right direction. Our roadmap for MDG is driven by our customers and leading market trends in data management. We believe, SAP can provide much more value than other MDM tool providers, because we understand how master data is used in business context. We can provide master data management capabilities that truly support all domains, all industries, and all approaches - embedded into the business application landscape and end-to-end processes. And, we have proven this with SAP MDG, and we will continue to deliver on this unique promise.

Selecting SAP Master Data Governance means opting for a Leader in Master Data Management – a choice founded on superior market presence, high user satisfaction, and a clear vision of the future. Join the vast network of SAP MDG users that have established a trusted foundation of business-critical master data to support operational excellence, ensure accurate analytics and AI, and accelerate digital transformation.

Check out these resources to learn more:

Sneak Peek in to SAP Analytics Cloud release for Q2 2024

2024-04-16T11:05:20.990000+02:00

As we enter Q2 of 2024 we bring you some exciting and eagerly anticipated new features in SAP Analytics Cloud. We will first take a sneak peek in to our Top 5 features in QRC2 2024. A full list of what is coming can be found on our Roadmap Explorer or the Product Plan.

Unified Story Presentation Mode

Digital Boardroom was always a popular feature in SAP Analytics Cloud. The Presentation Mode aims to bring the main features that are available with the traditional Digital Boardroom directly into the Optimized Experience. These include:

Import of Story Pages: it allows a Designer to re-use content as their own. There is an option to re-import the page when we detect there has been an update
Building a Presentation Structure: it can be flat (by default) or configure a tree structure with Topics and Subtopics
Customizing the Presentation Mode Experience: option to configure the Toolbar, Default Viewer, and Navigation Panel State
Present Mode: brand new Present Mode with a simplified toolbar (if configured) and side panel navigation

Lite Viewer for Present Mode is supported for Flat Presentations. NOTE the same limitations for Lite Viewer still apply and will cause a re-direct to the Full Viewer.

Learn more about Unified Story Presentation Mode in this blog post by @Tunir

Integrating KPI tiles in SAP Mobile Start

An exciting new feature coming this quarter is the ability to consume Analytical KPIs when on the move. Now, you can see your SAP Analytics Cloud business KPIs in SAP Mobile Start.

The KPIs are shown on the respective tile or widget, and you can easily navigate to the details (analytical story) by tapping on them. Based on the configuration, analytical stories can be opened within the native SAP Analytics Cloud app or within the embedded web view of SAP Mobile Start.

For further details check out this great blog by @ThiloBerndt

Rank with others – view top-N and bottom-N in comparison to other data points

An eagerly anticipated feature from our Customers is the ability to Rank with Others for Charts. In this quarter we will support an option for the Story Designer and Story Viewer to define a rank with others. It will be supported for:

Charts with Flat Dimension(s)
Charts with Measures with Base Aggregation
Define a Rank with Others on Multiple Axes

Others can be placed last (Top N) or first (Bottom N):

It can be renamed
We will NOT allow you to expand the others node or interact with it (i.e. Linked Analysis, Filter, Exclude, and so on)

Data analyzer – export to PDF with the option to fit columns to page width

We continue expanding the feature and functions of Data analyzer by adding the ability to export data as a PDF file using the new option in the dialog to fit the columns to the page width.

Plan Process Management - notifications management

An important new feature in notification management within planning is the ability for the system administrator to further customize how and when users will receive notifications. They can now provide granular control over which calendar events will initiate email or in-application notifications.

Highlights include:

Configurable for all key calendar actions/events for both user-driven and automated tasks
Default notification settings configurable by role (i.e., Owner, Assignee/Review, Viewer)
Control delivery method (i.e., In-App and/or Email)
Quick selection to enable/disable All notifications

There are plenty more exciting deliverables coming your way this quarter and we will be taking a deeper dive into these items in our What’s New video and blog series so stay tuned. For a full list of all features coming in QRC2 2024 for SAP Analytics Cloud check out the Roadmap Explorer and Q2 2024 Product Plan.

Want to experience new and existing features for yourself? Take the leap and start your journey towards making data-driven decisions with confidence by signing up for a free trial, today.#sap #sapanalytics

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.

What’s New in SAP Datasphere Version 2024.8 — Apr 11, 2024

2024-04-16T19:00:58.606000+02:00

SAP has delivered the version 2024.8 of SAP Datasphere. Please note if I’m not mistaking that version/wave 2024.7 has been skipped.

There are 4 Administration, 3 Data Integration, 2 Data Marketplace, 1 Data Modeling and 1 General enhancements in version 2024.8 of SAP Datasphere.

Administration

Photo by Guido Klinge on Unsplash

1 You can now control the duration of the token and refresh token lifetimes for your OAuth clients. Create OAuth2.0 Clients to Authenticate Against SAP Datasphere

Token Lifetime: Minimum of 60 seconds to a maximum of one day. Default 60 minutes.

Refresh Token Lifetime: Minimum of 60 seconds to a maximum of 180 days. Default 30 days.

Note: @Thibault_dockx has posted a question: Unable to connect to Datasphere using the CLI There might be an issue regarding this enhancement.

2 In Tenant Configuration, the block size of compute blocks and storage blocks have been reduced to offer you more granular control over your tenant configuration. Performance classes have been added to allow you to choose the vCPU allocation to your tenant. Configure the Size of Your SAP Datasphere Tenant

Available Performance Classes, determines the number of vCPUs allocated to your tenant.

Memory
Compute
High-Memory
High-Compute

3 You can now assign yourself to a scoped role. Create a Scoped Role to Assign Privileges to Users in Spaces, Assign Users to a Role and Control User Access to Your Space

It’s handy and practical feature as long as you have DW Administrator role.

4 In Tenant Configuration, you may now increase the size of data lake storage in increments of 1 TB instead of 5 TB. Configure the Size of Your SAP Datasphere Tenant.

You can specify from 0 TB (minimum) to 90 TB (maximum), by increments of 1 TB.

Data Integration

Photo by Gozha Net on Unsplash

1 You can now use Confluent Kafka and the corresponding connection type to add targets to replication flows. Using Confluent Kafka As the Target, Integrating Data via Connections and Confluent Connections

SAP Datasphere — Integrating Data via Connections

2 In Views monitor, you can now see all views that have been created in the Data Builder without additional actions from your side. Views are displayed into 3 tabs to allow a better navigation:

All Views: It displays all the deployed views that have been created in the Data Builder for the current space.
Scheduled (default): It displays the views for which a schedule is defined to persist the data.
Persisted: It displays the views already persisted.

Persisting and Monitoring Views

3 Replication flows can now be scheduled and included in task chains if all their objects have load type Initial Only. Schedule a Data Integration Task (Simple Schedule) and Creating a Task Chain

You can schedule or un-schedule data integration tasks such:

Remote Data Replication
Data Persistence
Data Flow
Replication Flow — *if all objects in the flow have load type Initial Only
Task Chains

Data Marketplace

Photo by Elaine Casap on Unsplash

1 Use the Catena-X Access Policy context to create a private offering within the Catena-X network. Context Management App

More information about Catena-X automotive network.

2 You can now manage Data Marketplace licenses, releases, and contexts using the command line (CLI):

Data Modeling

Photo by Randy Fath on Unsplash

1 The improved Select Object dialog provides new filtering options to help you quickly find your required object when creating associations and doing other tasks that require you to select an object from your space. This new dialog is available when creating an association, a text association, applying a data access control, and adding related entities to an E/R model diagram.

General

Photo by Michael Bourgault on Unsplash

1 As from wave 2024.08 all SAP Datasphere guides can be commented directly from the Help Portal: the Feedback button becomes visible in the action bar once you are logged in. Open Documentation Initiative for SAP Datasphere: Provide Feedback and Help us Improve the Documentation

If you would like to contribute Open Documentation for SAP Datasphere and provide feedback directly from Help Portal. Try:

SAP Datasphere — Feedback

References

What’s New in SAP Datasphere. April 11, 2024. Version 2024.8
What’s New in SAP Datasphere Version 2024.8 — Apr 11, 2024 at medium.com/@tncykarc

Use CRUD Opreations with SAP Datasphere Command-Line-Interface

2024-04-25T20:05:59.755000+02:00

Using the Command Line Interface (CLI), it is possible to implement processes and interactions without the user interface in the Datasphere and to automate them using Node.js scripts, for example. An important feature for the usefulness of the CLI in the context of the Datasphere is the provision of CRUD operations, which are considered basic operations in the handling of data elements. CRUD is an acronym and stands for C - creating data elements, R - reading data elements, U - updating data elements and D - deleting data elements. The handling of the individual elements is shown below. It is possible to edit a list of objects:

Object Name	Command
Local Tables	local-tables
Remote Tables	remote-tables
ER Models	er-models
Analytic Models	analytic-models
Task Chains	task-chains
Data Flows	data-flows
Replication Flows	replication-flows
Transformation Flows	transformation-flows
Data Access Controls	data-access-controls
Business Entities	business-entities
Fact Models	fact-models
Consumption Models	consumption-models
Intelligent Lookups	intelligent-lookups
Contexts	contexts
Types	types

The local tables serve as an example for illustrating CRUD operations in this blog. The following command serves as the basis for handling CRUD operations:

datasphere objects [object-type][commands]

After the command has been executed, you are forwarded to a dialog. The space ID must always be specified here and, depending on the command, a JSON file to create an object, the technical name of an object to delete it or both to update a view.

Create
The objects listed above can be created using the following command:

datasphere objects local-tables create --space "Space ID" --technical-name "viewName" --file-path "path.json"

Make sure that a JSON file is used as the basis for the objects. The following template can be used for local tables:

{
    "definitions": {
      "Template": {
        "kind": "entity",
        "@ObjectModel.modelingPattern": {
          "#": "DATA_STRUCTURE"
        },
        "@ObjectModel.supportedCapabilities": [
          {
            "#": "DATA_STRUCTURE"
          }
        ],
        "@EndUserText.label": "Template",
        "elements": {
          "Column1": {
            "type": "cds.Decimal",
            "precision": 38,
            "scale": 19,
            "@EndUserText.label": "Column1"
          },
          "Column2": {
            "type": "cds.String",
            "length": 4,
            "@EndUserText.label": "Column2"
          }
          ,
          "Column3": {
            "type": "cds.Integer",
            "@EndUserText.label": "Column3"
          }
          ,
          "Column4": {
            "type": "cds.Date",
            "@EndUserText.label": "Column4"
          }
          }
        }
      }
    }

Read
The following command can be used to read the objects listed above. A JSON file is returned here. The JSON file is filled with the semantic information of the object and, depending on the object, is similar to the template provided for local tables.

datasphere objects local-tables read --space "Space ID" --technical-name "viewName"

Update
The objects listed above can be changed using the following command. Updates can be made via changes to the JSON file. It is recommended to execute a read command first and to change the JSON file that is obtained by the read command.

datasphere objects local-tables update --space "Space ID" --technical-name "viewName" --file-path "path.json"

Delete
The objects listed above can be deleted using the following command. By specifying the space and the technical name, the objects are uniquely identified and can therefore be deleted.

datasphere objects local-tables delete --space "Space ID" --technical-name "viewName"

For further information, please check SAP Help.

Example 1 - Creating and Deleting a Local Table
A simple use case is described here, in which a local table is created and deleted again. Both take place in a space called "CLI". In the first step, a JSON file is required. In this example, the previous template is used as the file. Once you have prepared the file for your requirements, you can execute the Create command. As can be seen in Figure 1, no parameters are specified in the command, which is why a dialog takes place via the CLI. Here you can enter the space ID and the path of the JSON file. If everything has worked, you should get a successful response. The table should then be generated as shown in Figure 2.

Figure 1 - Create Command

Figure 2 - Space on Datasphere with Table created via CLI

To delete the table, you need the delete command listed above. Again, no parameters are specified here, which is why you have to specify the space ID and the technical name of the object in a dialog. By specifying the correct object, it is deleted from the space.

Figure 3 - Delete Table via CLI Command

Example 2 - Read an object and create it in a new space
This scenario represents a useful process for exchanging objects in a space-to-space sharing via the CLI. In addition, such logic could be partially automated using scripting languages so that all or selected objects can be exchanged by specifying the source space and the target space. However, only a simplified use case is shown here in the blog, which is intended to represent the basic function.
Unlike above, in this example the commands are executed with the addition of parameters. First, an object must be selected, which is written to a selected directory via the CLI in the following step. Once the object has been successfully written to the directory, it can be created on the other space in the next step. As the technical name only has to be unique within a space, this step also works without adapting the JSON before the upload. Once the JSON file has been uploaded with the correct parameters, the table should be available in both Spaces, as you can see in figure 5.Figure 4 - Read and Create in a space-to-space sharing process

Figure 5 - Table in both spaces

Both use cases are relatively simple in this scenario, but efficient. As already mentioned, the use cases can be expanded very widely and offer huge potential. If you have any questions about CLI or if you have an exciting use case, please feel free to get in touch.

What’s New in SAP Datasphere Version 2024.9 — Apr 23, 2024

2024-04-29T18:42:25.804000+02:00

SAP has released version 2024.9 of SAP Datasphere. Enhancements in administration, data cataloging, data integration, and data modeling.

There are 1 administration, 2 data cataloging, 4 data integration, and 5 data modeling enhancements in version 2024.9 of SAP Datasphere.

Administration

Photo by Justin Hu on Unsplash

1 You can now add a visual tenant type indicator to distinguish which system you are using. For example, between a test or production system. When enabled, a colored information bar is visible to all users of the tenant, and the browser favicon is updated with the matching color. Display Your System Information

You can add a title and choose a color for the system information. It’s a very simple, but very useful enhancement. Users should know what system they are using. It’s critical during the SIT, UAT phase of the project.

Data Cataloging

Photo by Phill Brown on Unsplash

1 You can add an SAP Datasphere, SAP BW bridge system to the catalog. With this addition, metadata for the following objects from SAP BW Cloud systems are monitored and extracted to the catalog.

ADSO (Advanced DataStore Object)
AREA — (InfoArea)
HCPR (Composite Provider)
IOBJ (InfoObject)
LSYS (Source System)
RSDS (DataSource)
TRCS (InfoSource)
TRFN (Transformation)

Once extracted, you can publish these objects as assets to the catalog. Connecting to Data Sources

In addition to SAP Datasphere and SAP Analytics Cloud, you can now connect to SAP BW Bridger and add objects to the catalog. The catalog does not automatically extract new or updated metadata from the source system. To synchronize the catalog with the source system, choose Synchronize System.

2 Assets from SAP Datasphere, SAP BW bridge source systems appear in the impact and lineage diagram. Evaluating Catalog Assets

Since the SAP BW Bridge is now one of the systems for Catalog Assets, you can see the SAP BW Bridge in the visual impact and lineage analysis diagram.

Data Integration

Photo by Daniel Eledut on Unsplash

1 For the connection types SAP ABAP, SAP BW, SAP ECC, and SAP S/4HANA On-Premise, we’ve added RFC Serialization to the advanced connection properties. The property is available with Data Provisioning Agent version 2.3.7.2 or higher. You can use it to specify whether to use fast column-based serialization or row-based serialization when transferring data. The default is column-based serialization.

Remote Function Call (RFC) is one of the connection options for all SAP ABAP, SAP BW, SAP ECC, and SAP S/4HANA on-premises systems. Now you can take advantage of the serialization feature of RFC. Check out RFC: New Fast Serialization (New) and How to Profit from FAST RFC Serialization by @thomas_weiss

2 When the source system is not available, you can disable real-time data replication from the Remote Tables monitor or from the table editor of the Data Builder. The data access of your remote table is switched from Replicated (Real-time) to Replicated (Snapshot), and the replicated data are kept to allow further consumption or actions on the object such as deleting or changing the properties of the connection. Disabling Real-Time Data Replication

It’s as simple as selecting one or more remote tables in the Remote Tables Monitor and clicking Data Replication Disable Real-Time Data Replication, which changes the data access from Replicated (Real-Time) to Replicated (Snapshot).

3 For local tables as the source of a replication flow, you can now also use local tables that are enabled for delta capturing. However this is still only possible for load type Initial Only ( not Initial and Delta). Add a Source

When creating replication flows, you can select Datasphere itself as the source connection, the source container is automatically defined as the space you are in. The load type is automatically set to Initial Only because Initial and Delta is not currently supported!

4 To ensure better resilience, the SQL procedure to persist view data is always executed asynchronously.

If the persistent view data is asynchronous, you need to look at all views in the Data Integration Monitor.

Data Modeling

Photo by Artem Zhukov on Unsplash

1 When you create a data flow and the data source come from Open Connectors, you can define filter conditions to apply on the data. Add a Source

Now not only for CDI, OData, and ABAP CDS but also for Open Connectors sources you can add filters. Check out Open Connectors Connections.

2 When an association in the underlying views or tables of an analytic model is changed or deleted, you can still save the analytic model, but you get an error message and the status of the analytic model changes to design time error.

Hmm, is it really an enhancement? Before this enhancement, what was the state of the analytical models when the underlying view association was changed or deleted?

3 You can now create and update modeling objects of any type from the command line (CLI) using the following commands: Manage Modeling Objects via the Command Line.

datasphere objects <object-type> create — space <id> — file-path <file>.json
datasphere objects <object-type> update — space <id> — technical-name <name> — file-path <file>.json

The syntax of datasphere objects as you see more options to handle:

datasphere objects <object-type> list
— space id
[ — technical-names <name>[,…]]
[ — output <file>.json]
[ — select “<property[,…]>”]
[ — filter “<property operator value>[ and|or …]”]
[ — top <n>]
[ — skip <n>]

4 If your SAP S/4HANA Cloud connection supports using replication flows to load data to local tables, the Import Entities dialog will now default to this data access setting. Importing Entities with Semantics from SAP S/4HANA

With Import Entities, you can automatically create more Business Builder and Data Builder entities for CDS views by leveraging their rich semantics to import higher-level objects and track and include associations to dimensions, hierarchies, and text entities.

5 You can now create non-cumulative measures for analytic models. This type of measure is used for cases where measure values describe a state that is not changing every day. For example, you can use non-cumulative measures to calculate the stock in a warehouse, or the bank balance of an account. Create a Non-Cumulative Measure

Another simple nice extension. Once you create the measure of the analytical model, select Non-Cumulative Measure and provide the base calculation.

References

What’s New in SAP Datasphere. April 23, 2024. Version 2024.9
What’s New in SAP Datasphere Version 2024.9 — Apr 23, 2024 at medium.com/@tncykarc

Plumbing in the Data – the rise of the Data Engineer

2024-05-01T17:38:54.793000+02:00

Before we dive into the role of the data engineer, let's acknowledge that it can be a little confusing when you hear about the various ‘data’ roles that currently exist. So what exactly is a data engineer compared to a data modeler, or a data analyst?

Let's start with perhaps the easiest role to describe: Data Analyst. A Data Analyst uses tools to work with data to answer business questions and derive insights. Data Analysts focus on the usage of data and less on how to collect it and prepare it. (though some data analysts would argue they do it all!)

Data engineering is the building of systems to enable to collection and usage of data. This data is usually made available to data analysts or data scientists who need large data sets to power analytics or train AI models. They need access to large and diverse data sets that come from a multitude of systems, often in different formats and from different technologies.

The role of the Data Engineer to make that data available at the right time, in the right format, and at the right level of quality, making sure data compliance rules are followed.

Data is growth is a big concern for companies who collect vast amounts of data. Storage costs and security are at least two concerns. A Data Engineer will design and implement a storage architecture that provides fast access to data at the least cost that is secure.

As well as the provisioning of data, data engineers are often responsible for data modeling. So what is data modeling?

Data modeling is the structuring of the acquired raw data into meaningful business-relevant shapes so that it can be easily consumed by analytical applications. A data model represents a consistent view of business data and is built to serve the unknown requirements of the business in the future. A data model is fueled by the data pipelines that have been built by the data engineer.

In summary, a Data Engineer prepares data for consumption, so they need to provision data then model it into shape ready for consumption.

Sometimes, the task of data modeling is shared with the data analyst who works closely with the business to answer business questions. A Data Analyst and a Data Engineer work closely together to ensure a complete flow from data generation to analytics.

So now you understand the role of the Data Engineer, how does SAP help you develop skills in that role? And how do you prove to future employers that you have developed those skills?

SAP have you covered on both counts.

SAP recently launched the new role-based certification Data Engineer - SAP HANA. This certification proves you have developed skills in data provisioning and data modeling, using the SAP HANA platform.

To prepare for certification, SAP Learning provides you with two free-of-charge, self-paced courses:

Provisioning Data to SAP HANA and Developing Data Models with SAP HANA

So, if data-plumbing appeals to you, jump in and tap into the growing demand for this skill set.

Understanding Data Modeling Tools in SAP

2024-05-02T16:25:43.459000+02:00

Choosing the right SAP data modeling solution can be a challenging task, as there are multiple options available, including cloud-based or on-premise solutions with or without data visualization capabilities etc..so to select the best tool that can meet the project requirements and specifications, several factors need to be considered including, reviewing the raw data source and understanding how it will be used, assessing the need for data warehousing capabilities, evaluating the team's skill sets, and deciding whether a cloud or on-premise solution is preferred.

During the last few years, I've worked with almost all the SAP Data Modeling tools that are available, and In this blog article, I would like to briefly highlight the strength of each tool and where would be the best fit for it 🤗.

Understanding Data Modeling

so what it's data modeling in SAP and why it's important? 🤔

when we talk about Data modeling we are defining the process of creating diagrams that show how data flows in and out of a database. These diagrams define the data formats, structures, and functions required to support the data flow. Once the database is built, the data model becomes documentation for its existence and design. Data models provide a framework of relationships between data elements within a database. They enable different applications to share the same data and create a simple and logical database that eliminates redundancy, reduces storage requirements, and enables efficient retrieval.

SAP HANA Calculation Views

SAP HANA calculation views are the key data modeling object on HANA, it's a graphical object that describes all the data processing steps from raw data to the final, consumable output.

Common data processing steps include filtering data, combining data, aggregating data, calculating new values, converting values, and ranking data. Calculation views are built in layers so that each calculation view can consume other calculation views to form a stack of super-models. This approach promotes the reuse of calculation views at the lower levels.

Strengths:

In-Memory Processing: Data processing occurs directly in memory, ensuring high performance.
Graphical Interface: Intuitive tools make it easy to create and visualize models.
Custom Logic: Ability to integrate custom SQL for tailored data processing.
Advanced Analytics: Supports spatial, graph, text analytics, and predictive/machine learning.

Best Fit: Developing flexible data marts with custom logic requirements.

SAP BW/4HANA

SAP BW/4HANA is an on-premise data warehouse. The main purpose of a data warehouse is to extract data from any data source, integrate the data, store the cleansed data, and provide modeling tools so that data models can be created ready for use by any analytical tool. This is exactly what SAP BW/4HANA can do.

Strengths:

Ready-Made Models: Comprehensive data models are provided for virtual and physical storage.
Data Warehousing Features: Supports dimensional modeling, hierarchies, and efficient data storage.
Graphical Tool: User-friendly interface for efficient model visualization.
On-Premise Deployment: Ideal for industries requiring high security and data control.

Best Fit: Establishing on-premise data warehouses with strong data integration needs.

ABAP CDS Views

ABAP CDS Views enable developers to create data models that are semantically rich. These models are then exposed to UI clients by the application services. CDS Views is the central pillar of S/4HANA development and is used as the core technology in most of SAP's programming models.

Strengths:

High Performance: Data processing is pushed down to the database level, ensuring efficiency.
Ready-Made Models for SAP S/4HANA: Simplifies operational reporting on S/4HANA.
Flexible Development: Allows for code-based customization leveraging SQL skills.

Best Fit: Operational reporting on SAP S/4HANA with ready-made content.

SAP Analytics Cloud

SAP Analytics Cloud is an end-to-end cloud solution that brings together business intelligence, augmented analytics, predictive analytics, and enterprise planning in a single system. It is a solution aimed at business users for self-service analytics.

Strengths:

Integrated Modeling: Modeling seamlessly integrated with reporting tools for streamlined analytics.
Cloud-Based: No need for software installation; data modeling is conducted in the cloud.
Self-Service Analytics: Empowers business users to create their own models.

Best Fit: Organizations seeking self-service analytics with integrated modeling and reporting.

SAP Datasphere

which is so far My Absolute favorite tool 😁! It is a public cloud software as a service (SaaS) data warehouse that supports the business data warehouse architecture. This tool effortlessly harmonizes data across the enterprise, making it a must-have for any business. It includes services for data modeling, data integration, cataloging, and data warehousing across SAP and non-SAP data, making it a one-stop-shop for all data management needs

Strengths:

Comprehensive Data Warehouse Features: Supports dimensional modeling, data history management, and hierarchy modeling.
Scalability: Easily scale resources in the cloud, supporting new innovations.
Cloud Service: Simplifies software management through cloud consumption.

Best Fit: Cloud-based data warehousing with advanced features and scalability.

SAP BusinessObjects BI Semantic Layer (Universes)

An organized collection of objects that called a Universe, which consists of dimensions, measures, and attributes grouped by business topics. The skill of a Universe developer is to create objects that resemble business terms by mapping them to the columns of the database tables. In some cases, SQL coding may be required to generate the value of the objects.

Strengths:

User-Friendly Interface: Graphical tool simplifies data modeling tasks.
SQL-Based Development: Easy to understand for database developers.
Mature Tool: Offers a wide range of features for diverse modeling needs.

Best Fit: Supporting SAP BusinessObjects reporting tools with Universes.

for more information, I would recommend this Learning journey from SAP which will give you an overview of all the SAP data modeling solutions 😊

Integrating SAP FSM with Microsoft PowerBI: Step-by-Step Guide

2024-05-03T07:56:33.723000+02:00

Introduction

In today’s data-driven world, integrating different platforms to enhance business intelligence and data analysis capabilities is crucial.

This article provides a guide on integrating SAP Field Service Management (FSM) with PowerBI. Notably, there is no standard connector between these two platforms, necessitating a workaround for effective data utilization. Here, we'll explore how to overcome the authentication challenges using OAuth2 and subsequently fetch data into PowerBI.

1. Introduction to the Challenge

Integrating SAP FSM with PowerBI poses a significant challenge primarily due to the authentication process. SAP FSM requires OAuth2 authentication, which involves token generation not natively supported in PowerBI. This guide aims to address and provide a solution to this issue.

There are many connectors available through PowerBI, like SalesForce, IBM, S4, but no connection to FSM.

2. Token Retrieval

Step 1: Understanding OAuth2 Authentication

OAuth2 is an authorization framework that enables applications to obtain limited access to user accounts on an HTTP service. In the context of SAP FSM, it requires setting up an OAuth2 client in the SAP FSM system to generate credentials.

Please refer to the following SAP documentation on how to create these.

You are able to set-up the rights and company assigned to the Client ID you are creating.

Once the Client is created, save the clientSecret in a secured place. This will be the only time the key is shown, so make sure you store it.

Step 2: Scripting the Token Retrieval Function

Open a fresh blank PowerBI report. Go to Transform Data, and add a new "Blank Query".
Then navigate to the Advanced Editor

Paste this code to create the token retrieval function.

()=>
let
 // Concatenates the ClientID & Client Secret and converts to base64
 authKey = "Basic " & Binary.ToText(Text.ToBinary("CLIENTID:CLIENTSECRET"),0),
 url = "https://eu.fsm.cloud.sap",
 // Uses the FSM POST OAuth2/token method to obtain a bearer token
 GetJson = Web.Contents(url,
     [RelativePath="/api/oauth2/v2/token",
         Headers = [#"Authorization"=authKey,
                    #"Content-Type"="application/x-www-form-urlencoded;charset=UTF-8"],
         Content = Text.ToBinary("grant_type=client_credentials") 
     ]
 ),
 FormatAsJson = Json.Document(GetJson),
 // Gets token from the Json response
 AccessToken = FormatAsJson[access_token],
 AccessTokenHeader = "bearer " & AccessToken
in
    AccessTokenHeader

Don't forget to replace the CLIENTID:CLIENTSECRET part with the values previously created.

Now, when you run this function, it should retrieve a bearer token.
If you encounter this warning : edit credentials, and select "Anonymous"

You should be able now to retrieve the token, and the hardest part is complete.

Rename this function "GET Token"

3. Fetching Data from SAP FSM

Creating the Data Fetch Function

Like before, create a new blank query, and paste this code

(query as text, DTOs as text, companyName as text, tenantName as text) =>
let
Source = Json.Document(Web.Contents("https://eu.fsm.cloud.sap", 
[RelativePath="/api/query/v1?dtos=" & DTOs  & "&query="
& query & "&account=" & tenantName  & "&company=" & companyName & "&useExternalIds=true",
    Headers=[Authorization="" & #"GET Token"(), #"Content-Type"="application/json",#"x-client-ID"="cpi", #"x-client-version"="1.0"]])),
    data = Source[data],
    data1 = data
in
    data1

This function is calling SAP FSM Query API (documentation here) using the previouslys created function GET Token.
4 parameters are waited :

query: this is the query you want to make on FSM
DTOs: this is the list of DTOs for the query
companyName: this is the company you want to query on FSM
companyTenant: this is the tenant of your FSM account

When saving, this should now look like this:

As you can see, this function can now be called as you wish. This will be the next step: invoke this query and create our first query. You can rename this "FSM Query".

4. Utilizing the Function

Here’s how you can use the above function to fetch and load data into PowerBI :

Enter the 4 parameters, and click on Invoke.

This will create a new query on the left side, with the result from FSM.

And by playing with PowerBI standard functionnality, you'll be able to expand, rename columns, add conditionnal logic; etc.

That's it! You now have all data in your PowerBI, and are able to query as much as you want the system.

Disclaimer

It's important to note that embedding credentials directly in your PowerBI files is not secure.
The client ID and client secret are visible in plain text, which poses a security risk.

SAP recommends using SAP Analytics Cloud for business intelligence to ensure better security and integration.

SAP Datasphere News in April

2024-05-03T10:45:11.774000+02:00

April was for real 💯 with regards to great new features for SAP Datasphere. There are also lots of excellent new blogs in the community and in case you missed it, our What’s New in SAP Datasphere session covering Q1 just ran in early April.

What’s New in SAP Datasphere Q1 2024 Webinar

We just ran our Q1 What’s New in SAP Datasphere session on April 10th 2024. No worries, in case you missed it. The session covering the features and functions delivered in Q1 2024 was recorded and is available on YouTube.
Watch the recording 👇

My top features in April

➡️ Confluent Kafka as Target for Replication Flows

After delivering the native Apache Kafka connection already back in November 2023, you can now use Confluent Kafka and the corresponding connection type to add targets to replication flows.
For more information: Using Confluent Kafka As the Target and Integrating Data via Connections

➡️ Cataloging offers ability to add an SAP Datasphere, SAP BW bridge as a Source System

You can add an SAP Datasphere, SAP BW bridge system to the catalog and have the metadata objects monitored and extracted to the catalog. Once extracted, you can publish these objects as assets to the catalog and use them in the impact and lineage diagram.
For more information: Connecting to Data Sources and Evaluating Catalog Assets

➡️ Measure Type Non-Cumulative Measure for Analytic Models

You can now create non-cumulative measures for analytic models. This type of measure is used for cases where measure values describe a state that is not changing every day. For example, you can use non-cumulative measures to calculate the stock in a warehouse, the bank balance of an account or certain values in HR use cases.
For more information: Create a Non-Cumulative Measure .

➡️ Add a Visual Tenant Type Indicator

When you are working with multiple tenants you can now add a visual tenant type indicator to distinguish which system you are using. For example, between a test or production system. When enabled, a colored information bar is visible to all users of the tenant, and the browser favicon is updated with the matching color.
For more information: Display Your System Information

Blogs for our Japanese Community

My top blogs in April

Excellent blog from @AlexLePape outlining the integration of SAP Datasphere with the Customer Data Platform (CDP) through Direct Data Access
➡️ Unlocking the Power of Customer Data – SAP Customer Data Platform and SAP Analytics Cloud.

@ClaudiaFiess started a blog series about SAP extractors and SAP Datasphere with many great details on the different scenario options:
➡️ Exploring Integration Options in SAP Datasphere with the focus on using SAP extractors
➡️ Exploring Integration Options in SAP Datasphere with the focus on using SAP extractors - Part II

Another favorite in April is @jaigupta excellent deep dive into currency conversion and the derivation of variables
➡️ Using Variable derivation for currency conversion measures within Analytic Model

The last blogs I’d like to highlight is about the command line interface. @Yves_Kipfer checks on the new feature of CRUD operations
➡️ Use CRUD Operations with SAP Datasphere Command-Line-Interface

More blogs to check out

Find more information and related blog posts on the topic page for SAP Datasphere. You will find further product information on our Community with various subpages about Getting Started, Business Content, the SAP BW Bridge as well as content for Best Practices & Troubleshooting and the FAQ for SAP Datasphere.

Find out how to unleash the power of your business data with SAP’s free learning content on SAP Datasphere. It’s designed to help you enrich your data projects, simplify the data landscape, and make the most out of your investment. Check out even more role-based learning resources and opportunities to get certified in one place on SAP Learning site.

SAP Data Analyst Certification

2024-05-08T05:28:23.062000+02:00

Certificates won’t get you a job in data analytics.

But you know what might?

The real world skills you gain in the process of getting that certification.

I fell into the trap of certification collecting earlier on in my data career, thinking the more the merrier!

But when it came time for interviews, no one ever asked me about any specific certifications I had on my resume.

Instead, they asked about the tools I learned, projects I built, and how I tied my learnings to my everyday job.

One certification that teaches you data analyst skills in the context of real world tools and business use cases is SAP’s Data Analyst Certification.

Its courses teach you everything you need to work in SAP Analytics Cloud–one of the most popular platforms for analytics and planning.

The best part: if you’re a woman, veteran, career changer, or in a minority group, you’re eligible to take this cert completely free.

Start learning today at 👉 https://learning.sap.com/certifications/sap-certified-associate-data-analyst-sap-analytics-cloud?campaigncode=CRM-PM24-DAN-2677421&source=socialad-glo-linkedin-influencer-megan

Unveiling SAP Analytics Cloud Performance Analysis Insights for Optimal Performance

2024-05-08T17:46:12.449000+02:00

In a dynamic landscape dominated by data-driven decision-making, monitoring your performance is crucial. And if you learn how to strengthen data management practices with respect to your performance, your business is going to thrive and avoid pitfalls. When it comes to SAP Analytics Cloud (SAC), ensuring peak performance isn't just a matter of preference—it's a strategic necessity. In this blog, we delve into the significance of SAC performance analysis, exploring its benefits and why it's a critical aspect of maximizing the potential of your analytics platform.

Understanding SAC Performance Analysis

Performance analysis in SAC encompasses an array of metrics, including stability, speed, scalability, and responsiveness. Essentially, it's about how efficiently the platform handles workloads, from loading dashboards to executing queries.

You can access it by, Choose System > Performance

And there are some tools that can help you with the whole performance analysis paradigm. Let’s discuss them.

Measurement Tool

The Measurement Tool within SAC serves as a compass for gauging your system's performance. By assessing factors like client time, network latency, and SAC responsiveness, it provides valuable insights into areas that demand improvement. The normalized scores generated by the Measurement tool offer a Measurement against best practices, guiding optimization efforts effectively.

The Measurement Tool tab consists of two Performance tests which are Client test and Network test.

(The above test is done in our internal system where Client score is 83 and Network latency is 280ms.)

The client Measurement tool calculates a normalized score that can be compared against best practices.
Network tests are essential for measuring client latency and bandwidth to the SAP Analytics Cloud Service.
Both CPU-intensive applications and network performance significantly impact load times for Stories and Analytic Applications.
It is crucial to be mindful of these factors to ensure optimal system performance.

Performance Analysis Tool

SAC's Performance Analysis Tool revolutionizes the way administrators monitor system performance. Gone are the days of grappling with complex tools and manual data interpretation. This tool automates the collection and storage of performance information, empowering administrators to identify and resolve issues swiftly. With features like Page Load Time and Widget Drilldown, administrators can pinpoint bottlenecks and optimize performance at a granular level.

To begin using the tool, you can narrow down your search criteria by specifying a combination of Date, Resource, and/or User.

we have a selected a Resource and chosen a story

We have used the below story for demonstrating Performance analysis tool

Upon selecting the Story that we want to analyze; we have presented with the initial view of Performance analysis tool.
The tool calculates the time at which pages, widgets, and model but no specific methods are there to control load time.

Page Load Time

It's mainly used to dig deeper into the story by looking at widgets, models, and pages. It gives a list of items sorted by how long they take to load, starting with the slowest.
It displays time for Pages in a deeper level like page preparation time. Maximum/Minimum backend time etc.

Widget Drilldown

It is used to drill down the widget wise measure’s which is used to calculate Widget time, Network time and Backend time like shown in the below image

When clicking any particular widget or action it pops up a certain window that displays the front-end time, connected Model and Backend Request.

If any particular widget is selected it changes the below graph which calculates Median backend time, Median Network time, Median Frontend Time.

Runtime Distribution

Below image shows the runtime distribution. It displays time for widget, backend, network, frontend.

Unveiling Insights with Statistics and Analysis

The Statistics and Analysis tool in SAC bridges the gap between technical backend operations and user-facing frontend experiences. By providing comprehensive statistical analysis, it offers a holistic view of system performance. From backend query execution times to frontend load times, this tool equips administrators with the insights needed to fine-tune performance across the board.

The Overview Page displays KPIs for Number of Logons, Frontend and Backend Kpi’s as well as Top 10 Users by Logon Time and Top 10 Stories by Load Time.

In SAP Analytics Cloud, a model can be used in multiple stories. Sometimes, the model itself works fine, but when combined with a specific story, it can cause performance issues. We identify these combinations that have the most impact on the backend system. This helps us understand which specific combinations of models and stories might be causing slowdowns or other problems in the system.

We use this charts to see how quickly query get answers and how many times they have run over time. We also check where the time goes in the process of running this query. This helps us understand how well the query are doing and if they're getting faster or slower over time

The Importance of SAC Performance Analysis

In today's data-centric landscape, SAC performance analysis isn't just a nicety—it's a necessity. Here's why:

Optimized Efficiency: By identifying and rectifying performance bottlenecks, SAC performance analysis streamlines operations, ensuring optimal efficiency.
Enhanced User Experience: A responsive and stable SAC platform translates to a seamless user experience, fostering user adoption and satisfaction.
Cost Savings: Proactive performance analysis helps mitigate the risk of system downtime and inefficiencies, ultimately resulting in cost savings for organizations.
Data-Driven Decision Making: With SAC performing at its peak, organizations can trust the accuracy and timeliness of the insights derived, facilitating informed decision-making.
Competitive Advantage: Leveraging SAC performance analysis to continuously optimize the platform provides organizations with a competitive edge, enabling them to stay ahead in today's dynamic business landscape.

In conclusion, SAC performance analysis isn't just a technical endeavor—it's a strategic imperative. By harnessing the power of performance analysis tools, organizations can unlock the full potential of SAC, driving innovation, efficiency, and success.

So, embark on the journey of SAC performance analysis today, and witness firsthand the transformative impact it can have on your organization's analytical capabilities and bottom line.

What’s New in SAP Datasphere Version 2024.10 — May 7, 2024

2024-05-09T15:55:33.407000+02:00

SAP has released version 2024.10 of SAP Datasphere. Enhancements in administration, data integration, data marketplace and data modeling.

There are 3 data integration, 2 data modeling, 1 administration, and 1 data marketplace enhancements in version 2024.10 of SAP Datasphere.

Administration

Photo by Patrick Tomasso on Unsplash

1 For resilience purposes, we are now limiting the maximum CPU resources for workload generated by spaces, user group users and agent users to 80%. The remaining 20% of CPU resources are reserved to ensure that the system can respond under heavy load.

You can now configure the total amount of threads that each space can consume up to a maximum of 100% of this 80% of the threads available in your tenant.

Also, statements are now queued when the CPU usage reaches 90% (instead of 80%) of the database capacity. This value, which you cannot change, applies to all spaces, including spaces created before this version. Set a Priority and Statement Limits for a Space

It’s all about how you manage resources in Space Management > Workload Management. Good luck!

The ADMISSION CONTROL QUEUE CPU THRESHOLD parameter is set to 90% and cannot be changed.

The TOTAL STATEMENT MEMORY LIMIT parameter is set to 80% by default. You can change it by entering the maximum number (or percentage) of GBs of memory that concurrently executing statements can consume in the space.

Data Integration

Photo by Manuel Mnvx on Unsplash

1 For transformation flows, if the source table is not a delta capture table, you can now switch delta capture on for the target table. The system adds the delta capture columns Change Date and Change Type to the target table. Add or Create a Target Table

It’s a handy addition to the transformation flows. Yes, if the source table is not a delta, then the default value of the Delta Capture property for the target table is Off. Now you can enable delta capture and add delta capture columns Change Date and Change Type to the target table.

2 If email notification is set up for a task chain, you’ll now receive email notifications if an error occurs during initialization or preparation to run the task chain, before the task chain run actually starts. Creating a Task Chain

You can now set up email notifications when task chains are completed. These options are available:

Send email notification only if the run failed.
Send email notification only when the run completes successfully.
Send email notification when the run completes.

SAP Datasphere — Task Chain Email Notification

3 Make your SAP Analytics Cloud stories available to the world by translating metadata from SAP Datasphere’s Translation tool. With the Translation privilege, you can access the dashboard and translate metadata such as business names and column names for dimensions and analytic models, and hierarchy dimension labels for stories to a wide range of languages. You can translate manually or via an XLIFF file, but also manage already existing translations, and update them. Translating Metadata for SAP Analytics Cloud

It’s another handy feature for multilingual environments when using SAP Datasphere with SAP Analytics Cloud stories and reports. Use the translation tool in Datasphere and translate object metadata, and your SAP Analytics Cloud story can be viewed in the language of your choice.

Data Marketplace

Photo by Raul Gonzalez Escobar on Unsplash

1 You can now duplicate existing data products. Duplicating a Data Product

It’s simple. My Data Products > Duplicate Product and make necessary changes and Save.

Data Modeling

Photo by Josh Olalde on Unsplash

1 We have improved the user experience:

You can now copy existing measures to create a new measure.
For measures, attributes and associated dimensions there is now a Select All/Unselect All checkbox.

Thanks again for handy features!

2 The Data Preview shows only the first 1,000 rows to increase performance. You can use filters to display the data relevant to you.

Okay, that works.

References

What’s New in SAP Datasphere May 7, 2024. Version 2024.10
What’s New in SAP Datasphere Version 2024.10— May 7, 2024 at medium.com/@tncykarc

S/4HANA Public Cloud Integration with SAP Datasphere

2024-05-14T05:54:48.426000+02:00

Create a Connection between SAP S/4HANA Public Cloud and SAP Datasphere

1. Introduction

In the fast-paced digital landscape, businesses seek synergy between technology solutions to thrive. SAP S/4HANA Public Cloud offers robust ERP capabilities, while Datasphere empowers data-driven insights. Together, they form a dynamic partnership, driving innovation and efficiency.

In this blog, we unravel the connection between SAP S/4HANA Public Cloud and Datasphere. From integration strategies to real-world applications, discover how this convergence revolutionizes operational excellence and strategic decision-making.

Join us on a journey towards unlocking the full potential of this transformative partnership, where data-driven insights fuel unprecedented success in the digital era.

To make the same steps you must have enough permissions on the S4 side and on the Datasphere side.

2. Basic Logic and Setup

We will setup the Connection to work with the S4 data in the Datasphere. To make sure that you have enough permissions to do that connection check the following steps:

On the Datasphere side you must have the permissions to setup a Connection with a System.

On the S4 side make sure that you have access to these apps.

If you have all these requirements, then you are to go with the blog. 🙂

3. Setup the Communication Settings in the S4

To build up the Connection between the S4 and Datasphere we must enable on the S4 side the communication between the S4 and other Systems. The first app that we we need to open is the App “Communication Arrangements.”

After that you can click on “New” to create a new Communication Arrangement. Then a pop up will appear. There you can search for the scenario “SAP Datasphere - ABAP CDS Extraction - WebSocket Integration”

After that you will jump in the Communication Arrangement. Then you can create a new communication System. This will be needed to declare which system will be take the data from the S4. For that click on new and enter a Name for your system and then click create.

Now you are in the Communication System. There you must enter the URL of your Datasphere tenant but make sure to remove the https:// of the URL. Your URL should be something like this: “your-datasphere-tenant.location.hcs.cloud.sap”

Then you can scroll down until you reach the options for the Inbound Communication User. There you can click on the + and press the button to create a new User.

You now can create your own user. Enter there a Name and Description for the User and then you can enter your own password, or you can propose own. After you have done it you can click on Create.

After you have created your Inbound Communication User you will be send back to the Communication System App. You will see that you User has been selected and you can confirm it by clicking “OK”.

Now you can scroll down to the Outbound Communication User. There you can click on the + to create a new User. In the Pop up you can select the Authentication Method of the User. There you can select “None” and then you can create the User.

Now you can save your communication System.

Now you are there where you have started. Select on the Authorization Group ID the Value “SAP_DSP_ALL” to have access to all Views from the S4.

Click on save and then you have setup everything on the S4 side.

4. Make the Connection to S4 in Datasphere

Now that we are back in the Datasphere we can create the connection to the S4. Go to the Connections and click on “Create” there you select the Connection “SAP ABAP.”

Then you can enter your credentials:

Protocol: Web Socket RFC

SAP Logon Connection Type: Application Server

Application Server: your-s4-tenant-api.s4hana.cloud.sap

Port: 443

Client: 100

Username: YourUsernamefromtheInboundConnection

Password: YourPasswordfromtheInboundConnection

Then you can click on “Next Step”

Enter a Name for the Connection and click on “Create Connection”.

The connection is created. If you want to check, if the connection has worked. You can select the Connection and click on the Validate button. There your connection will be validated.

If it has worked, you will see this message:

Now you can work with the S4 Views in the Datasphere. 🙂

Want to learn more about SAP Master Data Governance at SAP Sapphire 2024?

2024-05-15T18:34:26.774000+02:00

Your company wants accurate, complete, and trusted data to master your digital business processes and business models. The SAP Master Data Governance application provides ready-to-run, domain-specific master data governance, enabling you to decentrally own and consolidate and to centrally create, change, and distribute master data across your enterprise system landscape. Tight integration with other SAP solutions supports the reuse of data models, business logic, and validation frameworks with open integration to third-party products and services to support your entire technology stack. It supports cleansing, standardization, duplicate detection, and best-record calculation. It then lets you synchronize clean and standardized data across cloud and on-premise applications.

And here is what we have for you to learn more about SAP MDG at the 2024 SAP Sapphire in Orlando, Florida.

BTP115 Looking forward: SAP Master Data Governance

Learn how the SAP Master Data Governance application can support your transformation journey. Join a discussion on the technology shift to cloud-ready master data management, with support for process automation, agility through federation, data provider integration, and reimagined hierarchy management.

June 5th @ 2:00 PM

BTP160 Build a comprehensive data strategy that prioritizes governance

Data governance is a foundational element in driving innovation. Learn how the SAP Master Data Governance application, SAP Datasphere solution, and Collibra software maximize the value of your analytics and AI initiatives. Hear SAP’s strategy in support of master data management and the creation of a business data fabric for access to trusted data.

June 5th @ 11:00 AM

BTP170 How Kyndryl drove data modernization and trust through transformation

Hear IT infrastructure services company Kyndryl Inc's "Customer Zero Story" as it drove business value by transforming a disparate data and application landscape into a unified, governed source of truth. Hear how Kyndryl delivered innovation through data modernization with the help of the SAP Master Data Governance application and SAP BTP.

June 4th @ 3:00 PM

ERP158 Avantor: Transforming finance with SAP S/4HANA for central finance

Avantor Inc., a biotech, chemicals, and pharmaceuticals firm, embarked on a finance transformation with the SAP S/4HANA solution for central finance and SAP Master Data Governance application. Hear how it streamlined processes and launched new segment reporting, including group reporting, a secondary database, and the SAP Analytics Cloud solution.

June 5th @ 10:30 AM

To see a full list of SAP Business Technology Platform (BTP) sessions, solution demos, and demo stations, click here.

Enjoy your SAP Sapphire 2024 experience!

#SAPSapphire #SAP #SAPMDG #MDG #MDM #MasterData #MasterDataManagement #datagovernance

Kyndryl Shines a Spotlight on Master Data at 2024 Sapphire Orlando

2024-05-15T18:43:22.818000+02:00

Kyndryl (NYSE: KD) is the world’s largest IT infrastructure services provider, serving thousands of enterprise customers in more than 60 countries. The company designs, builds, manages, and modernizes the complex, mission-critical information systems that the world depends on every day.

Kyndryl became an independent company from IBM in November 2021 and had a 2-year timeframe (TSA exit) to separate. The legacy systems Kyndryl inherited were heavily customized and expensive to manage. Kyndryl required IT systems to move towards a lean, modern company. The decision that simplicity would be the foundation of their separation strategy, resulted in a greenfield implementation of SAP S/4HANA, leveraging SAP Business Technology Platform (BTP). The planned result is a simplified, streamlined, and optimized SAP business platform.

One crucial aspect of Kyndryl’s transformation was emphasizing the importance of master data in the journey to SAP S/4HANA. Clean, reliable, and well-governed master data was essential for a successful and efficient transition to SAP S/4HANA, and this is one area where Kyndryl sets itself apart. While master data management (MDM) is often overlooked for its critical role in business transformation, MDM can propel organizations to achieve their strategic goals more effectively and efficiently.

Data was scattered and difficult to find throughout the organization, with more than 68 disparate information/data warehouses, multiple master data applications and visualization tools, and no central governance or single source of truth. Decentralized and reactive services allowed for limited data visibility.

They chose SAP Master Data Governance (MDG), in conjunction with SAP BTP technologies, to provide the business with accurate, consistent, and timely business-critical master data. This lays the foundation for actionable business intelligence and analytics, accurate reporting, and streamlined business processes, enabling efficient decision-making and driving business growth.

SAP MDG provided a single definition and source of their master data and removed local standards and practices which deviated from the central depiction of the master data. They leveraged SAP Integration Suite to enable distribution of their master data to both SAP and non-SAP solutions across the company.

Kyndryl’s SAP platform provides consistent client experience, improves delivery efficiencies, supports automation objective, and reduces technical duplication across the enterprise aligning with an enterprise platform strategy for enterprise functions.

Want to learn more?

Read the full story here, Kyndryl’s 2024 SAP Innovation Awards entry.
Learn about Kyndryl’s partnership with SAP.
Check out their session at Sapphire Orlando on June 4th at 3 PM, BTP170: How Kyndryl drove data modernization and trust through transformation.

#SAPSapphire #SAP #SAPMDG #MDG #MDM #MasterData #MasterDataManagement #datagovernance

SAP Analytics Business Intelligence Statement of Direction – May 2024 update

2024-05-16T09:39:43.796000+02:00

The SAP Analytics Business Intelligence Statement of Direction provides guidance to our SAP BusinessObjects customers on the product directions of SAP Analytics Cloud and SAP BusinessObjects solutions. We published a new version this month with some important updates which can be found here.

SAP Analytics Cloud

SAP Analytics Cloud is SAP’s strategic analytics & planning offering and is changing the way our customers run their business, by allowing them to analyze their data in context and to make better and faster decisions. The integration of analytics and planning augmented by AI in one native cloud solution allows to move from insight to action, brings analytics closer to the point of decision, and provides trusted insights from customers’ most valuable data.

SAP Analytics Cloud’s vision is to provide trustworthy analytics consumable by any user from any context. The closed loop from insight to action allows customers to identify challenges, analyze options and execute decisions from one place.

Our knowledge of customer data and integration to business process enable customers to rapidly deploy analytics to address their most important business problems.

Going forward, we have four main investment areas: Enterprise Analytics, eXtended Planning & Analysis, SAP Integration and Generative AI.

SAP BusinessObjects BI

With more than 30 years of innovations, SAP BusinessObjects Business Intelligence platform (SAP BusinessObjects BI) equips thousands of companies which reliably manage enterprise reporting use cases at scale. With upcoming versions following 4.3, available on premise and as managed cloud, your investments are safe beyond 2030.

New maintenance strategy

In alignment with SAP standard maintenance strategy, we will switch from yearly support packages to minor releases every 2 years, with Mainstream Support for 3 years followed by Customer Specific Maintenance. This new model will address the need for more stability while keeping regular updates.

We already plan for a next release, BI 2027, which should be available in Q4 2026, and supported until at least 2029, and we will revisit our roadmap on a regular basis.

Plan your upgrade to BI 4.3

Version 4.2 will reach end of priority one support at the end of 2024.

As many did already, we strongly recommend all customers to upgrade to version 4.3 to benefit from the latest features, up-to-date platforms, connectivity and security standards, and to enjoy a modern user experience for the BI Launchpad and Web Intelligence.

SAP BusinessObjects BI 2025 and beyond

We plan to deliver SAP BusinessObjects BI 2025 in Q4 2024. With this new release, we will continue the modernization started in version 4.3 and streamline the suite by focusing on the core components while providing alternatives for unsupported ones.

Going forward, our strategy is based on 3 pillars: reach more business users, increase power users’ productivity and reduce total cost of ownership. To maximize the value for our customers, we will focus ongoing investments on the most-widely adopted SAP BusinessObjects BI solutions: SAP BusinessObjects Web Intelligence, single source .unx universes, SAP Crystal Reports, and the SAP BusinessObjects BI platform.

SAP Analysis for Microsoft Office and SAP Lumira Designer will be maintained and supported, but no new feature will be delivered.

We will provide alternatives

With SAP BusinessObjects BI 2025, we will not deliver SAP Lumira Discovery, SAP Crystal Reports for Enterprise, SAP BusinessObjects Universe Design Tool and .unv universes, multi-source universes and associated connectivity, SAP BusinessObjects Analysis edition for OLAP, SAP BusinessObjects Live Office and SAP BusinessObjects Mobile, but we are recommending alternatives for all use cases.

We will stop supporting AIX and Solaris after version 4.3 and will focus on Windows and Linux.

Move your analytics to the cloud

For companies willing to move analytics workloads to the cloud, we offer 2 complementary options: SAP BusinessObjects BI operated in a private cloud and SAP Analytics Cloud.

SAP BusinessObjects, private cloud edition is an opportunity to shift from a maintenance contract to a subscription model while leveraging your existing investments in SAP BusinessObjects BI solutions.

SAP Analytics Cloud is recommended for new applications, use cases and users.

Want to learn more?

Enjoy right now the full SAP Analytics Business Intelligence Statement of Direction.

SAP Datasphere Multilingual Support - Master Data Translation

2024-05-16T10:22:57.119000+02:00

In this blog we will provide you the overview of Master data translation. We will walk you through each case and how it can be visualized in the Analytical Model Preview as well.

This article is the first in the blog post series on multilingual capabilities in SAP Datasphere.

Blog Post #1: SAP Datasphere Multilingual Support - Master Data Translations (this blog)

Blog Post #2: Translating Metadata using the Translation Dashboard in SAP Datasphere

Introduction

Master data text is the descriptive information associated to the respective master data. For example, customers, products, vendors, materials and their hierarchies can have their independent description in their respective text table. In general, the master data or fact data entity has only IDs. In the analytical representation, if the textual data gets fetched it adds more value and additional context to the better understanding.

In SAC stories, displaying the product description rather than their id's gives a better understanding to the visuals.

Master Data Translation within SAP Datasphere can be accomplished through various methods.

Semantic mapping from an attribute within entity
Text Association
Dimension Association
Hierarchy Association

Figure1: Dependent entities of the Analytical Model

Figure2: Text/Dimension Associations in the entity – SALES (fact)

1.Semantic mapping from an attribute within entity

Here Product Id attribute fetching the corresponding description from the attribute Description of the same fact. When an attribute is marked as semantic type Text, it can be referenced in another attribute for Text/Association. Description with Semantic type Text is referenced at Product ID for Text/Association as mentioned in Figure2.

Figure3: Analytical Model Preview - Fetching the description from the same entity.

2. Text Association

Customer ID attribute has the Text association to table Description.

Figure4: SALES – Description Text Association

Based on the Data Access Language the description will be retrieved. Here I have German (Deutsch), and the texts are fetched for the same.

Figure5: Data Access Language Settings

To get the descriptions, we need to enable the ID and Description or Description from Presentations.

Figure6: Analytical Model Preview - Retrieving the description via Direct text association.

3. Dimension Association

Purchase Date is associated to the Time Dimension – Day.

Figure7: Purchase Date – Time Dimension - Day Association

Figure8: Time Dimension – Day, Here the associated Text tables are highlighted.

Since the day Dimension has a text association for months, the description for months is fetched accordingly (refer Figure10) based on the data access language settings.

Select the Hierarchy like below and enable the display of ID and Description from Presentations settings.

Figure9: Hierarchy Selection

Figure10: Analytical Model Preview - Retrieving the description via Dimension text association.

4. Hierarchy Association

SAP datasphere has released the external hierarchy with directory support. Here the language and texts for Hierarchy label and the Node can be retrieved as well. This blog has already addressed the same here - An Introduction to Hierarchy with Directory in SAP Datasphere

Summary

Text tables serve as the core of master data translation. While importing master data, ensure that if you are using the ABAP language codes like E or D, as compared to i18n language codes like de or en, the string length for the language column is set to 1. And this ensures the effective translation of master data if it has ABAP language codes. A cleaner data without any text duplications is recommended for the effective translation.