Discovering Basic Administration Tasks

SAP Analytics Cloud is a software as a service (SaaS) analytics platform designed by SAP. SAP Analytics Cloud is made specifically with the intent of providing all analytics capabilities to all users in one cloud native product.

SAP Analytics Cloud is a single solution for business intelligence and enterprise planning, augmented with the power of predictive analytics and machine learning technology. SAP Analytics Cloud solution helps all types of decision makers by combining business intelligence, enterprise planning, and augmented analytics into a single solution. You do not need to reply on standalone spreadsheets or disparate reporting and planning tools. It helps everyone in your organization make fast, confident decisions for better business outcomes.

As SAP Analytics Cloud is a SaaS solution that is fully managed by SAP, the underlying technologies are transparent to customers. Customers cannot deploy SAP Analytics Cloud on their own in a private cloud.

SAP Analytics Cloud is built on top of SAP Cloud Platform and leverages some of the services offered by the Cloud Platform.

As expected of any platform, SAP Analytics Cloud offers a set of platform services such as: auditing & monitoring, data connectivity, admin, lifecycle management, and many others. In this course, you will learn how to set up security and leverage some of these platform services that are offered.

SAP Analytics Cloud is a true SaaS service previously delivered in SAP data centers (SDC) on SAP’s own Neo platform. In 2018, SAP Analytics Cloud began rolling out on Cloud Foundry in AWS data centers. Most new customer tenants that are provisioned will be Cloud Foundry tenants. Systems hosted by SAP data centers use one digit in their URL, like us1 or jp1. Non-SAP data centers host systems use two digits, such as eu10 or us30.

SAP Analytics Cloud provides customers with global deployment options by partnering with SAP Cloud Platform and public cloud providers to support data center options that meet customer business needs:

• Geo-location requirements.
• Vendor specific preference.
• Local security and privacy compliance laws requirements.

SAC leverages public cloud partners:

• AWS: primary public cloud for SAP Analytics Cloud.
• Azure: primary alternative option to AWS.
• Alibaba: primary public cloud for China market and cyber security compliance.
• AWS Government cloud: FedRamp certified data center to support the compliance needs of the U.S. government.

Since the launch of the product, SAP Analytics Cloud is on fast development cycle and the system received updates approximately every two weeks. As of version 2018.19, a change was made to move to a Quarterly Release Cycle to align with SAP’s global strategy for cloud application releases. This means you can expect a new version once every quarter. The system updates are done by the Cloud Operations team and it's not possible for customers to opt out of having the update done on their system. Currently (Q1 2024), the current version of SAP Analytics Cloud is 2024.2.1.

The quarterly release cycle means that you will be on a consistent version of SAP Analytics Cloud for more time. This extra time can be used to develop use-cases, train users, and deploy content within a consistent version of the solution.

While the majority of the customer tenants are updated on a quarterly basis, some customer tenants may still be on the biweekly update cycle and get an update every two weeks. Customers could potentially have a mix of tenants on quarterly release cycle and biweekly update schedule. As you will see later in the course, this scenario can present challenges for content movement between systems.

While SAC offers a comprehensive analytics solution, SAP has many customers who still leverage on-premise Business Intelligence tools such as SAP BusinessObjects BI or have data in their on-premise systems such as BW, HANA, S/4HANA, and many other third-party sources. Large number SAP customers will run hybrid landscapes where some data and applications are in the cloud while others are on-premise. SAP Analytics Cloud can seamlessly integrates with your data and planning solutions to simplify your analytics landscape. It can connect to data from multiple different sources and visually analyze your information to see the full picture of your business and make better-informed decisions. In this course, you'll learn how to connect SAC to variety of on-premise and cloud sources.

Since 2023, monitoring and performance applications have been redesigned in SAP Analytics Cloud. Former applications have been renamed and redesigned, and some new applications have appeared meanwhile. In summary, you can find an updated list of all performance applications in the following lines:

• System Overview (menu System → Overview) – formerly SAP Analytics Cloud Administration Cockpit.
• Monitor (menu System → Monitor): The usual application for tracking current License usage and traces, which is now also embedded in the new System Overview application.
• SAP Analytics Cloud Usage Tracking Content (Files → My Files → Public → SAP Analytics Cloud Usage Tracking Content): The Usage tracking content story which will be replaced in the future by the new System Overview application.
• Performance (menu System → Performance): Menu containing a set of performance applications.

In this new lesson, we'll review the following applications for analyzing performance in SAP Analytics Cloud from the Performance menu (System → Performance):

• Measurement Tool (formerly Performance Benchmark)
• Analysis Tool(formerly E2E Performance Analysis Tool)
• Statistics and Analysis
• Data Action Statistics and Analysis
• Data Management Job Statistics and Analysis
• Private Versions Statistics and Analysis
• Error Statistics and Analysis

Measurement Tool (formerly Performance Benchmark)

With wave 2022 QRC2, a new Performance Benchmark feature has been deployed in SAP Analytics Cloud. In wave 2023 QRC1, this content is renamed now to Measurement Tool. You can start the landing page of the Measurement Tool application from the System → Performance → Measurement Tool menu.

The overall performance of SAP Analytics Cloud is mainly determined by the three components:

• Client Time (Frontend)
• Network Time (between the Frontend and the Backend)
• SAP Analytics Cloud Service Time (Backend)

Practically, the Measurement Tool feature will help you to benchmark your client hardware and measure the client latency and bandwidth to the SAP Analytics Cloud service.

You can choose two options for performance in Measurement Tool:

• Run a Client Test
• Run a Network Test

Performance Test: Run a Client Test

The client benchmark tool runs several scripts on the local machine and calculates a normalized score. This score can be categorized according to the current best practices for SAP Analytics Cloud:

To improve your client score while keeping your existing hardware configuration, watch out for CPU intensive applications and processes that may have a negative impact on your SAP Analytics Cloud performance. For example:

• Business video conferencing services
• Virus scanning
• Synchronizing shared network drives

Various tests with different CPU generations showed that SAP Analytics Cloud performance is CPU sensitive.

Performance Test: Run a Network Test

The network tests are measuring the client latency and bandwidth to the SAP Analytics Cloud Service.

• Network Latency (in milliseconds): It describes the elapsed time between the request and the response to the SAP Analytics Cloud server. A good latency is a ping rate within a low milliseconds range.
• Download Bandwidth / Upload Bandwidth (in megabytes per second): It measures the amount of data transferred to or from the client in megabits per second (Mbps). The network score may be negatively affected by other applications consuming the bandwidth, various network configurations, or different network issues. For example: Internet server provider connection, Virtual Private Networks, Firewalls.

Use data access control to restrict access to individual values in the model to specific users.

Security at the level of individual dimensions adds two extra Read and Write columns to the data table for the dimension where it has been activated. You can use these to control access (based on teams or individual user IDs) to specific cells or values. To enable dimension security, switch on Data Access Control in the Dimension Settings or via the model preferences.

Once the Data Access Control is enabled for a dimension, Read and Write columns are available to define which user or team should have Read or Write access to that dimension member. For the Version dimension, a Delete column is added as well as Read and Write columns to control which users can delete each public version. If the dimension has hierarchical members, the data access settings will be inherited by the lower members of the hierarchy. For example, if you grant Read and Write access to United States, users will be able to see data for individual states as well.

The following example illustrates how the data permissions restrict what users can do with the model.

• Account: Access control enabled
• Organization: Access control enabled
• Version
• Date

Version Security

Adding version security to a model lets you restrict read, write, and delete access to public versions, to prevent other users or teams from changing them. Users who have read-only permission for public versions can still copy data to a private version that they can edit. Users who don't have write permissions can't publish into a public version. With delete permissions for a public version, a user can read, publish to, and delete a public version.

Similar to using Data Access Control (DAC) for other dimensions, you use DAC for Version dimensions to restrict access.

• Only users with the Update privilege (defined in Security Roles) can set DAC for a version dimension.
• Version security applies only to planning-enabled models.
• The default read/write/delete permission is "none". You must explicitly enable read/write/delete access to users or teams, including yourself.
• The Version dimension was named the Category dimension in older versions of the application.

To restrict read and write access to a Version dimension:

1. In the Modeler, open or create a model, and select the Version dimension.
2. In the Dimension Settings panel, switch Data Access Control on, and then select OK.

   The three additional columns Read, Write, and Delete appear.

3. Select a cell under Read, and then select users and teams who you want to grant read access to.
4. Do the same for the Write and Delete cells, to grant write and delete access.

You can see details of your choices in the Preview panel.

You can display a subcategory of your SAP Analytics Cloud content by choosing a folder.

The following folders are available on the left side of the Files page:

The following folders can be accessed from the –> Browse menu.

Custom widgets can be bookmarked in the optimized story experience, and the bookmark's property values and data bindings result for the widget can be utilized. The custom widget developer must set the supportsBookmark property to true in the widget's JSON file.

As a Schedule Manager or Schedule Administrator, you are notified via email whenever the status of your scheduled publication changes from Open to Successful, Partially successful, Failed, or Canceled. You can view details of the status by clicking the Open Task button on the email.

In addition, you can now use the Include Formatting option while scheduling a publication with the CSV file type and keep the numeric formatting precisely as it appears in the table or chart. The value will be a string in the CSV file.