SAP Community - SAP Analytics Cloud, connectivity

Strategy for troubleshooting network performance issues in SAP Analytics Cloud (SAC)

2025-04-08T10:13:11.959000+02:00

Introduction

In today's data-driven environment, the ability to access and analyze information swiftly and efficiently is a cornerstone of successful business operations. As organizations lean heavily on business intelligence tools to make informed decisions, SAP Analytics Cloud (SAC) emerges as a powerful solution offering an extensive suite for data analysis, planning, and collaboration. Despite its robust capabilities, performance issues can occasionally arise, diminishing user productivity and impairing the decision-making process.

SAC performance is predominantly influenced by three key components: client time (frontend), network time, and SAP Analytics Cloud service time (backend). The network time plays a very important role among these, affecting how efficiently SAC communicates with data sources and delivers analytics results to users. This blog provides practical troubleshooting strategy on how to identify and resolve network performance issues in SAP Analytics Cloud.

Strategy for troubleshooting network performance issues in SAP Analytics Cloud (SAC)

To identify and resolve network performance issues in SAC, it's imperative to employ a range of steps designed to pinpoint specific problems:

Gather Information:
- Identify Symptoms: Clearly describe the specific errors or issues that users are experiencing within SAC, including steps to reproduce it if possible.
- Note Timing: Document when the issues started and whether they are intermittent or persistent.
- Identify Affected Users/Devices: List the users, devices, and locations that are experiencing the issue.
- Perform Tests at Different Time: If the issue is preserved for some time, perform several network tests at different times of the day to take into account the variations of network traffic.
- Test Different Network Configurations: Conduct tests across various network settings (such as different VPNs, Wi-Fi, or wired connections) to observe any variations in SAC performance, aiding identification of configurations that lead to issues.
Check ISP and Network Provider Status
- Investigate the status of your Internet Service Provider (ISP) or network provider for outages or maintenance activities that might be impacting service quality.
Monitor Network Latency and Bandwidth
- In general, SAP Analytics Cloud requires no more bandwidth than is required to browse the internet. All application modules are designed for speed and responsiveness with minimal use of large graphic files.
- Utilize network monitoring tools to assess latency and bandwidth between client systems and SAC servers. High latency or insufficient bandwidth can be a significant cause of degraded performance.
Note: As the bandwidth differences are primarily due to network and infrastructure factors outside of SAP's control, resolving these issues typically requires involvement from the customer's Internet Service Provider (ISP) and internal IT team.
Perform Ping and Traceroutes
- Use ping tests to assess the responsiveness of SAC servers, a simple but effective method for identifying connectivity or server accessibility issues.
- Conduct traceroute diagnostics from client systems to SAC servers to determine any bottlenecks or slow hops along the network path. This can reveal where delays are occurring.
Check Proxy and Protocol Settings and Assess Firewall and Security Settings
- Verify if third-party proxies are forcing HTTP 1.1 instead of HTTP/2. Ensure proper firewall and port configurations for SAC connectivity
- Verify that firewall and security settings do not block or throttle SAC traffic. Misconfigured settings could result in restricted access or slower performance.
- Verify Quality of Service (QoS) policies. Using QoS policies to prioritize SAC traffic can help ensure optimal performance, especially on congested networks. Prioritize traffic based on protocols, ports, or IP addresses associated with SAC.
Address SAML IDP Logon Times
- Evaluate the configuration of the SAML Identity Provider (IDP) to ensure accurate settings and optimized authentication speed. Slow logon times can result from IDP server load issues or misconfigurations that delay user authentication.
Generate Diagnostic Information and Examine Network Logs
- Review network logs for unusual traffic patterns or errors that could indicate disruptions in connectivity or excessive packet loss, which may affect user experience.
- Collect HAR files and Chrome DevTools performance profiles. Gather relevant error messages and screenshots.
Monitor Concurrent Usage
- Analyze SAC usage patterns to determine if peak utilization periods correlate with network performance issues, indicating bandwidth saturation during high-demand times.
Review Updates and Patches
- Ensure all network-related hardware and software are up to date, as outdated equipment or software may have vulnerabilities impacting performance.
Check SAP documentation and SAC technical requirements
- Ensure system meets SAC technical requirements.
- Review SAP notes on common network issues.
Engage SAP Support
- If you've investigated potential network issues and are still experiencing performance problems, engage SAP Support for further assistance.
- Open a support ticket in the SAP for Me portal.

Tools

Here are some essential tools you can use as part of the steps above:

SAC Performance Measurement Tool - build-in sac tool to measure your client’s score, as well as the latency and bandwidth to your SAP Analytics Cloud service. Note that the network score may be negatively affected by other applications consuming the bandwidth, various network configurations, or different network issues. For example: Virtual Private Networks (VPN), Firewalls, Corporate bandwidth, Internet bandwidth, Database performance (live data connections), SAML IdP logon time.
SAC Performance Statistics and Analysis Tool - build-in sac tool to identify scenarios that have a high-performance impact and shows network related insights such as Median Network Time Series and Top 10 User with the highest Median Network Time. For more information, see SAP Analytics Cloud Network Statistics.
SAC Performance Analysis Tool - build-in sac tool to analyze single user workflows and break down their total end-to-end runtime for different processing layers such as backend time, network time and frontend time. For more information, see SAP Analytics Cloud Performance Analysis Tool.
Cloud Check Tool (CCT) - tool to analyze the network performance to specific data centers. The CCT addresses the problem that it is hard to analyze if a bad performance is related to the cloud system, the network or the device of the end-user. With the help of the CCT, the network connection from the end-user to the data center can be measured easily. The results can be used for example in support incidents.
Cloud Availability Center - offers a personalized dashboard with at-a-glance visibility into cloud product availability. Accurate, up-to-date information into the status of your cloud products, critical information, maintenance updates, notifications, all in one place. Read more about it here.
Speedtest by Ookla - the global broadband speed test.
TRACERT (Trace Route) - a command-line utility in Windows used to trace the path that an Internet Protocol (IP) packet takes to its destination. The TRACERT diagnostic utility determines the route to a destination by sending Internet Control Message Protocol (ICMP) echo packets to the destination. You can use TRACERT to find out where a packet stopped on the network and whether it was lost. It also displays the amount of time each hop takes. TRACEROUTE - analogue of TRACERT command in Mac.
PING - a command-line utility in Windows used to test the reachability of a host on an IP network.
NETSTAT - a command-line network utility that displays open network sockets, routing tables, and a number of network interface and network protocol statistics. It is available on Unix, Plan 9, Inferno, and Unix-like operating systems including macOS, Linux, Solaris and BSD.

NOTE: All tools in this blog are given as examples, the use of alternative web tools is also allowed. Please note that measurements may vary between tools; variations are normal and reflect real-world network conditions.

SAP Notes

Here are some key SAP notes related to troubleshooting network performance issues in SAP Analytics Cloud:

Conclusion

Troubleshooting network performance issues in SAP Analytics Cloud requires a methodical approach. By following the steps outlined above, you can systematically identify and address these issues, ensuring optimal performance and a seamless user experience. Regular monitoring and proactive maintenance are key to maintaining high performance and preventing future problems.

Call to Action

If you encounter persistent issues or need additional assistance, don't hesitate to reach out to your Technical Quality Manager (TQM), your SAP Account, your Customer Success Partner (CSP), or our central entry channel, the Virtual Project Room (VPR). They can help you leverage support from SAP's Center of Expertise (COE) for Analytics, Planning, and Consolidation.

Introducing the New PaPM Step in Multi Actions in SAP Analytics Cloud

2025-04-18T04:56:12.174000+02:00

Integrating SAP Analytics Cloud (SAC) with SAP Profitability and Performance Management (PaPM) offers significant business value by combining powerful data visualization and planning capabilities with advanced profitability and allocation calculations.

SAC users can execute PaPM calculations via API step within Multi actions today. API step in Multi Actions empowers SAC users to execute remote calculation or program as one step in a multi action sequence. It requires technical background to properly configure the API step.

I am excited to introduce the upcoming release of integrating PaPM step within multi actions, which will be available with our 2025 QRC2 release! This new capability offers easy-to-use configurations, which empowers a non-IT user to create the streamlined workflows and improve efficiency.

What’s more, in the 2025 QRC2 release, SAC is introducing enhanced parameterizations for String type parameter and Datetime type parameter.

The PaPM Step in Multi Actions

A new dedicated step type in multi actions for PaPM integration

The PaPM integration step is now in a more prominent place in multi actions, making it more accessible for users.

Easy-to-use PaPM step configuration

Adding a PaPM step to your multi action is a straightforward process. As a user, your knowledge about the PaPM process is sufficient to easily configure the PaPM step in multi actions. You don’t need to bother about the complexity of the technical details of the HTTP APIs.

Here are the key steps involved:

Enter a Name: Provide a name for the step to easily identify it within your multi action.
Choose PaPM Connection: Select the appropriate PaPM connection to link your SAC tenant with PaPM system. Note that the HTTP API connection created for the API step is not applicable in this context.
Select Action: Decide whether you want to use an existing process or create a new one.
Configure Activity: Depending on your choice of action, select the relevant PaPM environment, process, template, and activity.
Set Parameters: Define the parameters for the PaPM step. You can choose pre-defined multi actions parameters or set fixed values.
Choose Selections: Specify the selections for the PaPM step to tailor it to your needs. You can choose pre-defined multi actions parameters or set fixed values.
Follow-Up Options: Decide if you want to delete the PaPM process after it runs or set the activity to complete if successful.

Once the PaPM integration step is added to the multi action, you can continue to add more steps, and reorder steps as required. This flexibility allows you to create comprehensive workflows that leverage the full capabilities of SAP Analytics Cloud and PaPM.

Enhanced parameterization strengthens the integration with PaPM

The current Member type parameter and Number type parameter are applicable to the new PaPM step. These parameters can be used across all existing steps and the new PaPM step.

Additionally, the QRC2 2025 release introduces a new String type parameter and a new Datetime type parameter. These new parameters can be used in the PaPM step and the API step.

The String type parameter offers various options to easily choose the right values during multi actions execution.

The available values of the String type parameter can be bound with a PaPM parameter ensuring only the values allowed by the PaPM parameter can be chosen during multi actions execution.

The available values of the String type parameter can be predefined in the multi actions (for example, yes or no, true or false).

The available values of the String type parameter can also be a free string, allowing the full flexibility of any string during multi actions execution.

The Datetime type parameter is also introduced, providing a date picker and datetime picker for value selection. The date and datetime picker ensure accurate and efficient input, improving user experience and interaction with the product.

Execute Multi Action
This multi action can be executed in story, analytics application, and scheduled via Calendar as normal multi action.

Now, planner can execute this multi action via one button / schedule job simply. The complexity of planning process is hidden from end-user.

Example Use Case

Imagine a scenario where a company wants to analyze its profitability across different product lines and regions. By integrating PaPM step within a multi action in SAP Analytics Cloud, the company can automate the process of running profitability calculations in PaPM, gathering the calculation from PaPM to SAC and running further calculations in SAC.

Here's how the multi action works:

Profitability Calculation: The PaPM step is triggered to run profitability calculations based on the collected data. This step leverages the Data Export Service and PaPM's advanced algorithms to pull data from SAC to PaPM and perform detailed calculations.
Data Collection: Once the calculations are complete, the multi action proceeds to import the calculated profitability data via Data Import step.
Data aggregation and consolidation: the profitability across different product lines and regions are aggregated and consolidated via the Data Action step.

By automating these steps within a multi action, the company can save time, reduce errors, and ensure that profitability analysis is conducted efficiently and effectively.

Conclusion

The integration of PaPM steps within multi actions in SAP Analytics Cloud marks a significant advancement in simplifying and streamlining the workflow. This new feature not only enhances the efficiency and accuracy of workflows but also provides users with the flexibility to tailor processes to their specific needs.

As businesses continue to navigate the complexities of the modern market, tools like these will be invaluable in driving success and growth

General Availability of Google Big Query Live Data Integration in SAP Analytics Cloud

2025-04-24T17:16:43.263000+02:00

We are thrilled to announce the upcoming General Availability (GA) of live data integration from Google BigQuery (GBQ) into SAP Analytics Cloud (SAC). This significant milestone empowers our customers to leverage their actuals data directly from GBQ data lakes, while seamlessly incorporating it into SAC as if the data were locally replicated. This enhancement introduces data federation capabilities to SAC data models, allowing usage of GBQ data within SAC data models as if it were local, enabling enhanced analytics and decision-making.

With this new integration, our customers who use GBQ as their data lake can now leave their actuals data in GBQ, keeping their data where it naturally resides, reducing the need for additional data replication and storage management. This is especially useful for customers who have XP&A scenarios and for business users who want to use federated data, without the assistant of a data engineer.

Expanded Data Access

You don’t use GBQ? Perhaps you use something like Impala, Oracle, Snowflake or Azure data lakes instead. GBQ is just the first step. We are committed to expanding these federation capabilities to support even more data sources and are currently working on covering all of the most popular data sources in the near future.

Technical Notice

As we roll out this new feature, there are a few important technical considerations to keep in mind:

HANA Cloud Requirement

- Your SAC tenant must be running on HANA Cloud, not HANA 2. Some customer tenants already have this configuration, but mass migration has not yet occurred. We will provide additional guidance on migration as needed. You can check the status of your tenant by looking in the about dialog. If it is a HANA Cloud based tenant, it will display the HANA Cloud version in this dialog. This information will not be present on HANA 2 tenants. More information on the migration is available here.

Architectural Work

- To facilitate the rapid expansion of our federated data source list, we will be conducting some architectural work on our federation infrastructure. While we do not anticipate any significant issues, there is a low probability that existing federated models created during the first quarter after the initial GA release may be impacted.

Note

If you create a data model with federated data access to GBQ sources prior to QRC 2025 Q3, please be aware of this potential. By QRC3, we expect to mitigate all risks associated with these architectural changes.

Feature Activation

Given the potential for minor disruptions, this feature will not be enabled by default. Until QRC3 2025 Q3, customers will need to raise a support ticket to have their HANA Cloud-based tenant enabled for federated access to GBQ. Please note that this measure is not to block anyone from using this feature. If you have a tenant that can support this feature, you can get it enabled. We simply want to make sure that you are aware that we can’t rule out breaking any models created during the first quarter after release. Please contact our support team for assistance. (see KBA KB0946801)

Embrace the Future of Data Integration

We are excited about this step forward in data integration and look forward to bringing even more capabilities to our customers. Stay tuned for more updates and enhancements as we continue to improve SAP Analytics Cloud.

Consuming complex Datasphere models

2025-05-02T15:34:21.742000+02:00

Introduction

SAP Datasphere is a part of the new SAP Business Data Cloud offering, which enables a business data fabric architecture that uniquely harmonizes mission-critical data across the organization.

One prime feature of Datasphere is the semantically rich data model, that allows for deep structures of dimensions, hierarchies and transactional data.
The data models created in Datasphere can then be consumed for reporting by various tools, both SAP and non-SAP tools and used as the basis of reporting.

And while the "Single source of truth" is the cornerstone of enterprise reporting, the individual tools may interpret the data models differently.

Purpose

My main objective in this blog is to show how different tools interprets an Analytical Model and what you may want to consider when building models.

I will focus on the Analytical Model, rather than consumption models / perspective in this blog, because the end-result is the same, and the Analytical Model is the object that most people will first try to consume for reporting.

Basic concepts

Before we dive in, I want to outline a few basic concepts that is important to understand the mechanisms at play. If you are already familiar with the concepts, you can skip this section.

Annotations

With the introduction of CDS views, SAP introduced Annotations, which is a tool to semantically enri

ch a table or view with instructions for the analytical engine to interpret the data.
While there are many, many different types of annotations, Datasphere primarily uses Consumption and Semantics.

Take the below, very simple table

Material	Quantity	Sales Unit
MAT1	100	KG
MAT2	50	TON

We can add the following annotations to the table:

Column	CDS Annotation
Material	none
Quantity	@Semantics.quantity.unitOfMeasure: 'Sales Unit'
Sales Unit	@Semantics.unitOfMeasure: true

Which does nothing to the table definition itself, but we've told the engine that 'Quantity' and 'Unit' belong together.

Associations

One of the, at least to me, best things that came with CDS views was 'associations'.

In short, an association between two tables allows the engine to fine-tune the query, based on what you need, without you having to join tables together.

Based on the associations and annotations, the analytical engine can dynamically navigate the underlaying objects to fit the demands of the user at runtime.

You can read more about associations in the SAP documentation or one of the many blog posts.

Semantic usage

When modelling objects in Datasphere, you will have the option to mark a table or view with a semantic usage, which provides some higher level annotations to the object.

Datasphere allows for the following options

Fact - Contains one or more measures and attributes. A fact typically has associations pointing to one or more dimensions and is consumed by analytic models (see Create a Fact to Contain Measurable Data).
Dimension - Contains attributes containing master data like a product list or store directory, and supporting hierarchies (see Create a Dimension to Categorize Data).
Hierarchy - Contains attributes defining a parent-child hierarchy (see Create an External Hierarchy for Drill-Down).
Hierarchy with Directory - Contains one or more parent-child hierarchies (see Create a Hierarchy with Directory).
Text - Contains attributes used to provide textual content in one or more languages (see Create a Text Entity for Attribute Translation).
Relational Dataset - [default] Contains columns with no specific analytical purpose.

Creating a complex data model

When I use the word "complex", I don't mean big but deep. A data model can have a hundred dimensions, and still be pretty simple.

In this example, I've created a straight forward data model, which the following objects:

Description	Semantic Usage
Employee	Fact
Department	Dimension
Education	Dimension
Job Function	Dimension
Job Level	Dimension

The main object in this data model is the employee, which has several attributes: (1) They belong to a department, have an (2) education, and a (3) job function.
In addition, each job function has another property: (4) job level, which indicates if it is a management position.

You can see the relationship in a graphical representation here:

When I say "complex" data model, this is what I mean: Some dimensions have no direct link to the main object - as you might have in a star schema - but can still be consumed.

In a real world scenario, you can imagine an almost infinite amount of levels and data links.

Imagine, for instance that we extended the "Education" dimension with the following extra fields: (1) Level (Bachelor, master, PhD, ect.) which also needs it one text, (2) area of study (such as engineering, finance, accounting, etc.), (3) completion date, (4) school of graduation, which may be a whole other dimension all by itself.

You can imagine how quickly we can add layers to a data model, which might technically be "correct" in a data modelling sense, but can present other problems, as we can see shortly.

When we create our Analytical Model, we have the option to select the associations we want from our "Fact" object.

Take note of a few things here:

Some of the fields are marked with a cube, which marks them as a Dimension (See the red squares).
All the dimensions have a "T" on their line, meaning that the dimension contains both key and text
Notice the "Date" dimension has a little stair / hierarchy icon as well, meaning that the association also comes with a hierarchy.

As you saw above, our "Job Function" dimension have attributes of it's own, so we can choose that and see that it has two attributes, but only one of them is a dimension:

The final analytical model looks like this:

Consuming the Analytical Model

As promised, we shall now see how the Analytical model can be consumed with all the semantic enrichment. I will go through the following tools:

SAP Analytics Cloud via a Live Connection
Microsoft Excel via an SAP Add-In
3rd Party tools via OData

Consumption in general

I have skipped over a lot of different topics, so I would absolutely recommend the official documentation on consumption to see in more detail.

SAP Analytics Cloud via a Live Connection

To be able to use this connection, you must have a user in both Datasphere and SAC, and have a working live connection (see Live Data Connections to SAP Datasphere in the SAP Analytics Cloud documentation).

When you create a story in SAC, or just edit an existing one, you have the option to add a data source:

From here, you can see a subfolder called Datasphere. Select the correct Datasphere and Space:

And select your Model:

Choose Analytical Model

You can now consume the model including all associations and metadata.
While there are a lot to explore, consider these two points:

The attribute “Management” is available as a property under Job Function and can be used in modelling
While the actual data only contains 1, 2, 3 in the “Department” column, because we have created the association to texts in Datasphere, we can use the texts with no extra work.

Additionally, take note of the "Date" column, where we have both attributes and the hierarchy:

Which is absolutely fantastic!
No objects are transferred to SAC, it just acts as a tunnel between Datasphere and the end-user.

However, there is one problem. As you may have noticed, Job Level is not visible as a property of "Job Function", whereas "Management" is. "Job Level" appears as it's own dimension.

SAC displays nested dimensions as their own entity, rather than related to "Job Function". Which may be perfectly alright, but might cause confusion for the people building the reports, that might not know the underlaying data model.
Imagine you have some sales data with a ship-to and ship-from columns. Both of these might have an "Address" dimension, and may cause confusion.

Microsoft Excel via an SAP Add-In

The SAP Analytics Cloud add-in (which is not the same as Analysis for Office), can simply be downloaded from the Microsoft Store.

After installing, you can add the model to Excel, very similar to SAC:

And will interpret the metadata in the exact same way SAC does:

3rd Party tools via an OData Service

Non-SAP products, such as Power BI or Tableau, can consume the data from Datasphere through an OData service.

Setup

You can find other guides to help you set up a connection for your service of choice, but the setup in Datasphere is the same for all tools.
I highly recommend @gustavokath blogpost on the topic, which has all the information you need to set up your client.

The OData request

Datasphere allows you to build and preview an OData request from the Analytical Model builder:

Which allows you to play and and configure the request and will generate a URL for you:

If you preview the data, you can see what kind of response it will generate.
However, there are a lot of things to note here, so let's take them one at a time.

1. Datasphere completely flattens the data before it sends it:

The dimensions are served as both the key, and the text.
Note that the "Dimension" is served as the technical name of the dimension.
All Text fields added this way will have the suffix "_T", but uses the technical name of the field in the annotation:

Which can make the output difficult to read.

2. All data is served as either string or number (decimal, integer ect).

This may not seem as such a big deal, until you consider the dates:

And while it can seem like a non-issue, the data itself contains no hint as to the format.
You can request the metadata and see the type of the field is Edm.Date, which is a nice standard format:

But that only happens because we've specified the column as a Date:

But you can imagine different sources serving data in different formats, which can make consumption and modelling a nightmare, unless you enforce strict data harmonization in Datasphere - which you should obviously do - but mistakes happen.

3. Nested dimensions are flattened with the business name, not technical name.

There is, however, one absolutely brain-melting thing that I personally foresee causing endless amounts of confusion.

The nested dimension M_LEVEL, seen above as "Job Level" is a part of the data, but it is represented by the description of the parent dimension:

Just looking at this output, you have a ice cube's chance in hell to deduce which fields belong together.
Again, you can query the metadata and see the relationship, but you know as well as I do, that at some point, the data is going to end up in the hands of a super-user who has a very specific use case, and will be absolutely floored.

And yes - you are correct. If I had named my dimensions and fields better, a lot of the confusion could be avoided. Absolutely. That is kind of the point 🙂

Conclusion

The ability to consume Analytical Models build in Datasphere is an absolute godsend.
It allows you to build central complex, semantically rich data model and allow your users to build their own reports, with whatever tools - to an extent - they prefer.

However, you as the data engineer have a responsibility to find the sweet spot between a model that contains 'enough', without it becoming too complicated.

Sometimes, two models might be better than one.

Creating a Sunburst Chart in SAP Analytics Cloud Using R and Plotly

2025-06-03T17:50:01.457000+02:00

A Sunburst chart is a powerful visualization technique for representing hierarchical data. It starts from a central root node and radiates outward to represent the hierarchy across multiple levels. In SAP Analytics Cloud (SAC), you can create this type of chart using R scripting and Plotly, providing a dynamic and interactive experience.

This guide walks you through the steps to build a Sunburst chart inside an SAC story using R.

What Is a Sunburst Chart?

A Sunburst chart visualizes multilevel categorical data in a circular layout. Each level of the hierarchy is represented by one ring or circle, with the innermost circle being the root node. Plotly in R provides excellent support for building dynamic Sunburst visualizations.

Step 1: Enable R Integration in SAC

Before using R scripts in SAC:

Open the Main Menu.
Navigate to System > Administration > External Systems.
Ensure the R connection is enabled.

If not, refer to the official SAP guide for integration:
https://help.sap.com/docs/SAP_ANALYTICS_CLOUD/00f68c2e08b941f081002fd3691d86a7/8d589100aea341a58a5c0916bbef022b.html

Step 2: Insert an R Visualization in Your Story

Create or open a story.
Go to the Insert section, click the +, then choose R Visualization.
A tile will be added to your story.
Click the tile and go to the Builder Panel on the right.
Click + Add Input Data and select the required dimensions and measures (e.g., Version, Plants, Product, M1).

Step 3: Add and Execute the R Script

Click Add Script, and you will see four options: Editor, Console, Environment, and Preview.

Paste the following R script into the Editor section. (Note: Adjust dimension/measure names as per your model)

library(data.table)
library(plotly)

as.sunburstDF <- function(DF, valueCol = NULL){
  require(data.table)
  colNamesDF <- names(DF)
  
  if(is.data.table(DF)){
    DT <- copy(DF)
  } else {
    DT <- data.table(DF, stringsAsFactors = FALSE)
  }
  
  DT[, root := names(DF)[1]]
  colNamesDT <- names(DT)
  
  if(is.null(valueCol)){
    setcolorder(DT, c("root", colNamesDF))
  } else {
    setnames(DT, valueCol, "values", skip_absent=TRUE)
    setcolorder(DT, c("root", setdiff(colNamesDF, valueCol), "values"))
  }

  hierarchyCols <- setdiff(colNamesDT, "values")
  hierarchyList <- list()

  for(i in seq_along(hierarchyCols)){
    currentCols <- colNamesDT[1:i]
    if(is.null(valueCol)){
      currentDT <- unique(DT[, ..currentCols][, values := .N, by = currentCols], by = currentCols)
    } else {
      currentDT <- DT[, lapply(.SD, sum, na.rm = TRUE), by=currentCols, .SDcols = "values"]
    }
    setnames(currentDT, length(currentCols), "labels")
    hierarchyList[[i]] <- currentDT
  }

  hierarchyDT <- rbindlist(hierarchyList, use.names = TRUE, fill = TRUE)
  parentCols <- setdiff(names(hierarchyDT), c("labels", "values", valueCol))

  hierarchyDT[, parents := apply(.SD, 1, function(x){
    fifelse(all(is.na(x)), yes = NA_character_, no = paste(x[!is.na(x)], sep = ":", collapse = " - "))
  }), .SDcols = parentCols]

  hierarchyDT[, ids := apply(.SD, 1, function(x){
    paste(x[!is.na(x)], collapse = " - ")
  }), .SDcols = c("parents", "labels")]

  hierarchyDT[, c(parentCols) := NULL]
  return(hierarchyDT)
}

# Example data setup
data11 = Test_Model
data22 = subset(data11, select = c("Version", "Plants", "Product", "M1"))
colnames(data22) <- c("Version", "Plants", "Product", "M1")

DF <- as.data.table(data22)
setcolorder(DF, c("Version", "Plants", "Product", "M1"))
sunburstDF <- as.sunburstDF(DF, valueCol = "M1")

plot_ly(
  data = sunburstDF,
  ids = ~ids,
  labels = ~labels,
  parents = ~parents,
  values = ~values,
  type = 'sunburst',
  branchvalues = 'total',
  marker = list(
    colors = c('white', "e6ccb2", "79b2d2", "a1c65d", "fac723", "f29222", "e95e50", "ff92b1")
  )
)

Click Execute, and the preview pane will display the generated Sunburst chart. Hit Apply to render it on the story canvas.

Conclusion

Using R and Plotly inside SAP Analytics Cloud, you can build highly interactive and insightful Sunburst charts to visualize hierarchical datasets. This approach not only expands SAC's native visualization capabilities but also gives you flexibility to tailor visuals to your data story.

Explore more possibilities with R integration in SAC and turn your dashboards into truly dynamic visual experiences.

SAC New Features to try after migrating from NEO to Cloud Foundry

2025-06-04T05:03:10.528000+02:00

Last update: 2025/06/04

As the landscape of data analytics continues to evolve, staying ahead of the curve is crucial for leveraging the full potential of your tools and resources. If you have been utilizing SAP Analytics Cloud (SAC) on the SAP data center (Neo), it's time to explore the transformative benefits of migrating to the non-SAP data center (Cloud Foundry). This blog is designed to summarize the exciting new features and enhancements that Cloud Foundry offers.

I will continue to update this blog as new features are released each quarter. Stay tuned and let’s start exploring!

Story

1. Scheduling and Publication

The Scheduling and Publication features in SAP Analytics Cloud (SAC) enable automated, customizable report distribution in various formats with targeted access control, enhancing collaboration and ensuring timely delivery of insights to relevant stakeholders. With this, you would be able to schedule a story with recurrence and distribute the same as a PDF over email to a number of different SAC and Non-SAC recipients. Want more, you can even include a customized message in the email per the schedule and as well attach a link to the view of the story in online mode which can be used to check the latest online copy of the story.