SAP Data Lifecycle Manager

In data management, depending on the data volume and access frequency, we talk about hot, warm, and cold data:

• Hot data: Frequent access, high-value, high query performance
• Warm data: less frequent access, less value, reasonable query performance
• Cold data: Rarely accessed, low-value, low query performance

Hot data store:

The hot data store is managed in the in-memory database tables of SAP HANA. This is the default location for data and provides the best possible performance but at the highest cost.

Warm data store:

• One SAP HANA slave node in the scale-out landscape is reserved for warm data storage and processing
• DLM-specified aging rules to move complete table partitions between hot store and warm store
• No DLM-generated SAP HANA View (pruning/UNION) required, due to single partitioned table
• Data access managed by SAP HANA including partition pruning
• SQL HANA Schema and HDI container scenarios are supported
• No impact to data update/delta handling, as records are moved to unique table partition, based on partitioning criteria

Cold data store:

• DLM uses the HANA Spark Controller to move data bi-directional between HANA in-memory store and Hadoop
• Cold Data are managed by the HANA Spark Controller and are only accessible via HANA SDA
• DLM provides pruning views to optimize data access to cold data
• Cross schema, cross- and local HDI scenarios are supported

Data Lifecycle Manager (DLM) functionality:

• Integration to custom-built or insert-only focused SAP HANA applications like IoT and sensor data (Big Data)
• Modeling aging rules on persistence objects
• Generated pruning node views for optimized access between source tables and target tables
• Modeling relocation rules on remote source table
• Automated and orchestrated data relocation between data stores
• Scheduling data relocation using SAP HANA task chains
• Generated stored procedures to perform mass data relocation - in and out