Requirements and Challenges due to Data Growth
A key requirement of SAP BW/4HANA is to store data - a lot of data!
Data is collected from all source systems in the organization. Many of those systems generate huge amounts of data on a daily basis. SAP BW/4HANA can even collect data in real-time using streaming mechanisms, and so the flow of data never stops.
From a technical perspective, SAP HANA - the database that powers SAP BW/4HANA - could cope with huge data growth. We would simply scale-up the hardware by adding more memory. Once the server reaches its memory capacity, we would then add more servers. This is called scale-out. But this is a very expensive solution to the data growth problem.
Apart from these increasing costs of storing more and more data in SAP BW/4HANA memory, we are in danger of clogging up the system with data that is rarely used. So let's take a step back and think about the best solution.

Data has a life-cycle. When data is new, it is usually very important to the business and used in decision-making. But as data ages, its value usually decreases as it is used less and less. We should move the less valuable data, from the highest performance / highest cost storage - which is memory - to the lower performing / cheaper storage options - such as disk.
What is needed are tools to manage the data life-cycle so that we can move data from memory to disk when the time is right. Such tools are included in SAP BW/4HANA.
Multi-Temperature Data Management Concept

SAP has defined a multi-temperature data storage concept. Data is classified as HOT, WARM, or COLD that aligns to the frequency of data access and the requirement for performance. These criteria include the type of data involved, how useful it is for business purposes, the importance of these processes, frequency of access, performance and security requirements. More facts that influence the setup of the SAP Multi-Temperature strategy for storing data include:
Budget restrictions that limit spend on hardware
Technical restrictions regarding the capacity of the SAP HANA database
Storage of historical data (due to data growth)
Rules for storing data, such as the requirement to save all data for at least some years for legal reasons
A large portion of data managed in a large enterprise data warehouse such as SAP BW/4HANA, is processed frequently and needs fast access. This kind of data can be considered as HOT.
In addition, there is often also a large volume of data that is not accessed frequently and perhaps might not require fast access. For this reason, this type of data can be defined as WARM. Data that is no longer needed but must still be kept, perhaps for legal reasons, can be classified as COLD.
Classifying Data by Access Frequency into Three Different Temperatures

SAP BW/4HANA data is classified by access frequency and use case into HOT, WARM, and COLD.
In the context of the SAP BW/4HANA reference architecture for data warehousing (Layered Scalable Architecture for BW/4HANA), the various areas in a data warehouse and the different architectural layers of the EDW architecture can be assigned to these multi-temperature data categories.
Let's learn more about these multi-temperature data categories:
- HOT
All layers related to mission-critical, day-to-day business analysis and planning
Hot data is accessed frequently for reporting and planning purposes, or by regular SAP BW/4HANA processes such as lookups during data loading. Examples of SAP BW/4HANA objects that typically handle hot data, include the following:
Data Mart DataStore Objects (advanced)
- Standard DataStore Objects (advanced)
No functional restrictions: read and write actions are allowed on this data.
- WARM
All layers related to data acquisition
Warm data is accessed less frequently. Performance is not the top priority for this data. . and does not have to be permanently stored in the main memory. Examples of SAP BW/4HANA objects that handle warm data include the following:
Objects in the Corporate Memory, typically Staging DataStore Objects (advanced).
Objects in the Open Operational DataStore layer, typically Staging DataStore Objects (advanced).
No functional restrictions: read and write actions are allowed on this data.
- COLD
All layers related to the retention of historical data
Cold data is accessed very rarely or not at all.
Functional restrictions: this data is used mainly for read only. It can only be made available for reporting by enabling a setting in the query. By default this data is not accessed by queries. Writing to the data is possible in exceptional cases only such as corrections. If reading this type of data is required, expectations regarding performance must be set accordingly.