SAP leaves no stone unturned to please its end-users and offers extended help. SAP BW4HANA is one such high-end offering. Technically, it’s an SAP HANA-powered packages data warehouse offered in both on-premises and cloud-based format.
The use of this data warehouse allows organizations to consolidate the entire database to gain deeper insights into it.
Also Read: SAP DWC: The Future of Data Warehousing
SAP BW4HANA implementation is not where an organization must stop and consider the SAP strategy complete. Regular statistical analysis of SAP BW4HANA infrastructure is crucial to assess runtime data, events, and key processes. In this blog, we’ll explore the importance of analyzing SAP BW4HANA and the need for statistical analysis.
Why SAP BW4HANA?
Out of all the data warehousing solutions, SAP BW4HANA stands out as its only solution handling analysis with full perfection in the transactional and analytical processing ecosystem.
Additionally, it’s capable to reap the expanded benefits of SAP HANA in-memory RDBMS in full swing. It grants enterprises the ability to integrate live and historical data together leading to in-the-moment analysis and data-driven decision-making.
Also Read: Your Modern Data warehouse for Elevated Needs – Know about SAP BW/4HANA
SAP HANA gains an edge over traditional data warehousing solutions by endowing a more flexible and real-time approach. It supports smart data streaming in the case of IoT and smart data integration. The facility of pre-packaged EDW that one enjoys with SAP BW4HANA is absent in other data warehousing solutions.
Statistical Analysis of BW4HANA – When, When, and How
Statistical analysis is the process of making sense of the collected data to find out the trends and patterns. It’s a subset of data analytics used to interpret research studies and determine the utility of software/tools.
In the case of SAP BW4HANA, statistical analysis is used to record the runtime data for SAP BW4HANA processes and events. The process involves calculating the total usage time by computing the accurate event runtime. The value of event runtime can be analyzed by subtracting the event start-time and event end-time. Additionally, statistical analysis is required to record the BW objects monitoring values from the Data Warehouse.
As BW4HANA is a type of data warehouse, the general data infrastructure statistical analysis process will be applicable. In general, the process is based on core data services technology or CDS. Based upon the areas, it makes the analytical queries accessible.
Also Read: SAP BW/4HANA – The Intelligent Enterprise Data Warehouse
CDS views analytical queries are used as default proposals for analysis featuring crucial information and are capable to execute in the BI client.
Additionally, one is allowed to define the Transient Providers-based queries, extracted from cube views. To make this possible, one must select the Search for TransientProvider field in the Query Wizard.
The use of CDS technology keeps the need for installation and activation of technical content away during the statistical analysis. Also, there is no need to load the data. Data is delivered in its real-time state.
Using the CDS technology, one can perform statistical analysis of data warehouse areas like data loading, process chains, data volume, and query runtime.
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Data Loading Statistical Process
To perform the statistical analysis process for data loading, one is allowed to use the RSPM request statistics and RSPM DTP load statistics.
RSPM Request Statistics involves using the RSPM request statistics with CDS view query “Rv_C_RspmRequestQuery” and cube view “Rv_C_RspmRequest”. RSPMREQUEST source table for the statistical analysis for data loading is also used.
Analytical Query 2CRVCREQQRY is the query used for returning the information request for a BW target object.
The next approach for statistical analysis of data loading is RSPM DTP Load Statistics which involves using query CDS view “Rv_C_RspmDtpLoadQuery” and cube view “Rv_C_RspmDtpLoad”. The reference source tables used for this approach are RSPMREQUEST, RSPMXREF, RSPMPROCESS, and RSBKDTP. The analytical query used in this approach is 2CRVCDTPLOADQ which returns the query information during the execution of the DTP.
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Data Volume Statistics Process
Data volume statistical analysis is a tedious process and allows one to combine the statistics for the combined SAP HANA /cold store data volume, SAP HANA online data volume statistics, and cold store data volume statistics. The resulting analysis of combined data volume acts as an entry point for data volume analyses.
They provide a detailed analysis of the whole data volume at a particular time. For extra detailed information, one can gather the statistical analysis value of SAP HANA online data volume and cold Store data volume.
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Process Chain Statistical Process
Process chain The statistical process can be started by combining the process chain status statistics and status and runtime information statistics.
To gather the statistics for the process chain status, one has to use process chain status statistics with query CDS view “Rv_C_PcmPcQuery” and cube view “Rv_C_PcmPcCube”. The analytical query used here is 2CRVCPCMPCQ and explains the present-date status of all the system-inherited process chains.
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Query Runtime Statistical Process
One has to use query CDS view “Rv_C_OlapStatAQuery” and cube view “Rv_C_OlapStatACube” to execute the statistical analyses process for query runtime.
With the help of extracted statistical value, one can figure out how much time is required for the execution of particular user actions. The analytic query used in this case is 2CRVCOLAPSTATAQ and the source of the reference tables used are RSDDSTAT_OLAP (view), RSDDSTATHEADER, RSDDSTATINFO, and RSDDSATEVDATA.
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Analytic Queries Authorization
A key part of statistical analysis is authorizing the analytic queries. It can be done using the S_RS_COMP object. Using this object, one can gain control over a user’s actions related to query processing. In case of prohibiting certain actions like query execution, one has to restrict the authorization value of S_RS_COMP as per the need of the hour.
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Full Extraction
Full extraction is the next stage of statistical analysis of the data warehouse and is performed using the full-extraction supportive code views. The CDS views are one of them and are flexible enough to be used seamlessly in operational data provisioning.
Using these cube views, one can easily craft a historical analysis model via snapshot loading and generating the time series.
To perform these two tasks, one has to access the ODP-Myself source system. In this system, taking the help of ODP context ABAP Core Data Services makes the creation of DataSource for operational data providers is possible.
Additionally, loading the snapshots into the DataStore object is possible. It allows the analytics performer possible to figure out whether or not a full view supports full extraction.
Statistics Views and Queries Time Details
Prior to the launch of SAP BW/4HANA 2.0, the time details of views and queries were exclusively stated in the UTC format, with no utilization of local user time format. However, SAP BW/4HANA 2.0 now provides the time details for query CDS and cube views in the local time format, allowing users to access information in their preferred time zone.
Additionally, the CDS view query fields are offered in local user times. Previously UTC format-based fields are now available in local user times. This time difference can create havoc in the statistical analysis of SAP BW4HANA.
Hence, one must maintain consistency in the time details. To make this happen, make sure that Rv_C_RspmDtpLoadQuery (the load stat) and Rv_C_RspmRequestQuery (the requested stat) certain fields are delivered in both UTC as well as local time for the user.
