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Optimally setting up in-memory databases with the right modelling
CHALLENGE
The insurance group has converted its data warehouse from SAP BW on HANA to a HANA in-memory database, which enables significantly faster and more complex queries. Now the company wants to use the new modelling options to optimise existing data models: The goal: to make the most of the advantages of in-memory technology and thus lay the foundation for a performance boost.
APPROACH
What can in-memory capabilities do, both in the virtual and persistent layers of data? How can the technology be used for new application areas, such as annotated board reporting? The modelling concept developed by DMA initially focuses on complex data models; in a second step, the departments themselves take on the necessary adjustments for their respective needs. In order to streamline the conversion process, the current processes are migrated first, followed by the historical data – and both are finally merged into significantly fewer and less complicated data models than before.
BENEFITS
More performance, less effort: Thanks to the consistent use of in-memory functions, it is not only possible to significantly reduce the amount of redundant data. After the changeover, both administration and internal modelling efforts are massively reduced. Distributed data can be retrieved with high performance in more complex data models and provide a consistent picture in the analysis.
Finances
Optimally setting up in-memory databases with the right modelling
CHALLENGE
The insurance group has converted its data warehouse from SAP BW on HANA to a HANA in-memory database, which enables significantly faster and more complex queries. Now the company wants to use the new modelling options to optimise existing data models: The goal: to make the most of the advantages of in-memory technology and thus lay the foundation for a performance boost.
APPROACH
What can in-memory capabilities do, both in the virtual and persistent layers of data? How can the technology be used for new application areas, such as annotated board reporting? The modelling concept developed by DMA initially focuses on complex data models; in a second step, the departments themselves take on the necessary adjustments for their respective needs. In order to streamline the conversion process, the current processes are migrated first, followed by the historical data – and both are finally merged into significantly fewer and less complicated data models than before.
BENEFITS
More performance, less effort: Thanks to the consistent use of in-memory functions, it is not only possible to significantly reduce the amount of redundant data. After the changeover, both administration and internal modelling efforts are massively reduced. Distributed data can be retrieved with high performance in more complex data models and provide a consistent picture in the analysis.
Finances
Optimally setting up in-memory databases with the right modelling
CHALLENGE
The insurance group has converted its data warehouse from SAP BW on HANA to a HANA in-memory database, which enables significantly faster and more complex queries. Now the company wants to use the new modelling options to optimise existing data models: The goal: to make the most of the advantages of in-memory technology and thus lay the foundation for a performance boost.
APPROACH
What can in-memory capabilities do, both in the virtual and persistent layers of data? How can the technology be used for new application areas, such as annotated board reporting? The modelling concept developed by DMA initially focuses on complex data models; in a second step, the departments themselves take on the necessary adjustments for their respective needs. In order to streamline the conversion process, the current processes are migrated first, followed by the historical data – and both are finally merged into significantly fewer and less complicated data models than before.
BENEFITS
More performance, less effort: Thanks to the consistent use of in-memory functions, it is not only possible to significantly reduce the amount of redundant data. After the changeover, both administration and internal modelling efforts are massively reduced. Distributed data can be retrieved with high performance in more complex data models and provide a consistent picture in the analysis.