Apache Spark vs. SAP Business Warehouse

Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.

Incentivized

SAP BW is best for: 1. Large enterprises 2. Enterprises with 3+ legacy systems with entrenched users (politically difficult to merge) 3. Enterprises with employees who can understand both the technical capabilities of SAP BW and the needs of the business users - ability to speak both languages, otherwise the program could be unwieldy and potentially underutilized (it's not particularly inexpensive) SAP BW is less appropriate for: 1. Small enterprises 2. Enterprises who have well established, same location, CRM and UFS - the integration of data analysis will be easier and less expensive with other solutions 3. HANA

Incentivized

• Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
• Faster in execution times compare to Hadoop and PIG Latin
• Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
• Interoperability between SQL and Scala / Python style of munging data

Incentivized

• It tracks bin locations for parts which can be really helpful.
• SAP Business Warehouse tells you who has a part checked out so you can find it.
• It shows current parts shortages so buyers are flagged to get parts on order based on current demand.

Incentivized

• Memory management. Very weak on that.
• PySpark not as robust as scala with spark.
• spark master HA is needed. Not as HA as it should be.
• Locality should not be a necessity, but does help improvement. But would prefer no locality

Incentivized

• Age of software is showing as it struggles with very large data modules, which were not as prevalent in its early years
• Querying performance at times can be very slow
• Support and development for BEx has been discontinued or hard to find.

Incentivized

Capacity of computing data in cluster and fast speed.

If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used

Incentivized

The overall usability is robust. The tool offers lot of native feature to achieve all the data warehousing functions. Starting from data modelling to reporting and authorisation, the tool provided native features for almost all the areas of analytics. Integration of hybrid modelling with hana studio opened the usage of sql functions with the sap analytics

Incentivized

1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.

Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.

Incentivized

SAP Business Warehouse scores higher in data warehouse functionalities for integration to SAP ERP and other SAP solutions such as SAP CRM, SAP APO, and SAP SRM. Standard SAP data source extractors which are available in SAP ERP can be used immediately for full or delta replication into SAP Business Warehouse. System governance in SAP Business Warehouse is top-notch with change management support for migration between system landscape from the development system to production system.

Incentivized

• Business leaders are able to take data driven decisions
• Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available
• Business is able come up with new product ideas

Incentivized

• Positive - This tool report output is in Excel so it's a good tool if your users are familiar with Excel.
• Positive: this tool has rich BI content so developing extractors for standard processes from SAP ECC can be done in minutes.
• Negative: It lacks lot of features which are available in other newer tools today. For ex. - rich charts, rich filtering, exporting capabilities, user interface.
• Negative: Its not a plug and play tool like Qlikview, Lumira, or Tableau. Even a single report development in this tool takes a lot of time compared to others.

Incentivized