Your company’s dataset contains millions of records, and users want to visualize the data in SAP SAC. However, the UI becomes sluggish when attempting to load these large datasets.
How would you optimize the SAP SAC user interface to handle large datasets while maintaining a responsive experience for users?
To optimize the SAP Analytics Cloud (SAC) user interface and ensure a responsive experience when dealing with large datasets, there are several strategies you can implement to enhance performance and improve the user experience. Below are the steps and techniques you can apply:
– We can leverage the data aggregation method where we pre-aggregate the data. Thus, instead of loading millions of records, the volume of data will be reduced using aggregation techniques by various data models & data preparations. This helps reduce the volume of data processing time & becomes more manageable for SAC to handle.
The data roll-ups or summarizations can be done before bringing the data into the SAC, so that only high level of data can be sent in the front end.
Optimize the data models by optimizing the dimensions & use measures for aggregations. Using optimized calculated measures can also be used. These are the ways through which performance can be improved & unnecessary data retrieval can be taken care.
Optimizing the data connections as and when possible. This helps in offloading of the data to the data base layer, so this way SAC can focus more on visualization rather than handling of the larger amount of dataset.
– Apply the right amount of filters to reduce the data loads in our dashboard. We can apply the filters in our models, stories, dashboards etc to limit the amount of data we can load into our SAC system. Dynamic filters & drill downs should be used.
– We should avoid using too many charts & widgets on the same page when we are dealing with larger datasets. Reducing the complexity will help in interpreting the system, and the high-level trends without overwhelming the systems.
– The only required data which is required should be loaded into the system as and when needed. This concept is also known as lazy loading concept. Enabling custom loading indicators & feedbacks for the users can also help with better experience of larger datasets during the loading process.
– For larger dataset, always use the live data connections, specially when the data is stored in external datasets. If we are using the import mode, we should ensure to load only the necessary subsets of data.
– Caching can be enabled for storing the frequently accessed data. This helps with avoiding querying the backend data source repeatedly. Another good feature is to preprocess the data into the staging environment before loading it into the SAC system.
– We can also leverage the capabilities of HANA such as using HANA Calculation Views, Hierarchies & Indexed Views for faster data retrieval.
– The most important aspect is to monitor & troubleshoot the performance of the system timely, by checking the usage & load time of each story or dashboard on timely basis. This helps if further optimizations are required.
Using the above strategies, we can significantly improve the performance & responsiveness of SAC while handling larger amount of dataset.
I am going to post new Real-Time Scenerio Based SAC – Q/A for next 30 days. Check it out on acorporateguy.com – Prepare For Your Next Interview !!
Schedule your SAC Mock Interview Session/How to prepare for Interviews” with ME: https://topmate.io/vartika_gupta11/1375779
Get your “33 Real Time Scenerio Based Ques & Ans” PDF – part 1 now: https://topmate.io/vartika_gupta11/1523015
Get your “33 Real Time Scenerio Based Ques & Ans” PDF – part 2 now: https://topmate.io/vartika_gupta11/1523041
Get your “150 Common Questions only list for SAC Interview Preps” now: https://topmate.io/vartika_gupta11/1089505