Data Volume Management (DVM)
Data Volume Management (DVM) is an application within Solution Manager (SolMan) which is used to review, analyze and predict the data growth within your SAP Landscape. DVM is a tool which will aid in summarizing, deleting and archiving. Extractors pass the statistical information into SolMan, which can then be analyzed using the dashboards, graphs and queries with drill down capabilities in the SolMan Fiori Tiles views.
Data is growing at an astronomical rate especially with the newer technology of Social Media and Internet of Things (IoT) being captured in SAP systems.
Data Aging is a methodology, supported by DVM, utilized for moving operationally less relevant data within a HANA database to gain more working memory. Data Aging moves data from the current area (main memory) to the historical area (secondary storage) of your HANA database and thus making sure only operationally relevant data is loaded into the current area. Note: that Direct table access within ABAP programs will require adjustments.
SAP’s service and support portfolio for Data Volume Management (DVM) consists of:
– DVM Guided Self-Service: Which is a tool based approach within SolMan.
– DVM Continuous Quality Check (CQC): SAP Consulting service report-based approach.
– DVM Enterprise Support Value Map: SAP Enterprise Support services approach.
– DVM Workcenter in SAP Solution Manager: Leverage pre-built SolMan Fiori Tile transactions.
– DVM Engineering Service (ESRV): SAP Consulting services approach
The benefits of DVM
- Used to predict database growth in the future based on past metrics.
- Identify which of the SAP tables can be scheduled for archiving as well as identify the archive objects also identify which records can be deleted
- Flexible queries can be used for analyzing by period, size, standard or customized tables.
- DVM Planning dashboard used to predict data storage savings before and after archiving
- KPI measures can be assigned to quantify achievements
Our real life experience
We implemented a DVM solution which allowed our telecommunication client to work on their storage and archiving strategy. Using the prediction analytics, they were able to purchase cheaper storage and initiate a streamlined archiving strategy. Customizing table records were scheduled for deletion and then tablespace reorganization to regain the prime disk storage. They were able to target the specific tables records for archiving to cheaper SAN to remain in compliance. In-directly this also led to improved performance of long running reports.
We implemented DVM for our Simplified Finance on HANA client for their analysis of Data Aging and Archiving projects