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Over the last few years, the data volume in SAP systems has multiplied. And while the amount of information to be stored continues to grow exponentially, performance is becoming increasingly important. If you use customer-specific logic in a SAP HANA Data Warehouse, SAP BW on HANA (from version 7.5) or SAP BW/4HANA system, SQLScript can provide help.
You can use SQLScript in transformations, customer exits and BAdIs as well as HANA Analytic Views to benefit from enormous performance increases. In real life projects we have experienced cases with up to 15x faster runtime. Since the calculations are executed directly on the database level ("code to data" approach), you don't have to wait until the data is loaded. You can concentrate directly on implementing the knowledge gained and thus gain a competitive advantage.
Using SQLScript in your business logic allows you to fully exploit the potential of your HANA database. The calculations are executed directly in the database without loading the data into the application layer first. This way you can benefit from up to 15 times better runtime!
Don't wait any longer for the data to be loaded, make decisions! Because of the enormous speed advantage, you can focus on data analysis, gaining new insights and getting results instead of waiting for data to be processed.
Don't waste time waiting, but use the latest insights before competitors do. Take advantage of SQLScript to consolidate and expand your market position and increase existing competitive advantages.
The launch of the HANA database led to a paradigm shift in data processing. In order to fully exploit the advantages of the new database, SAP relies on the "Code Pushdown" concept. This involves transferring calculations involving large amounts of data to the database level. This prevents unnecessary data transfers from the database to the ABAP server. Instead, calculations are performed directly in the SAP HANA database, where the data is stored. The code thus is brought "to data" (so-called "code to data" approach). This saves a lot of time, especially with data-intensive calculations.
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