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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Apache Spark

    Score8.8 out of 10
    N/AApache Spark is an open-source, distributed cluster-computing framework designed for large-scale data processing, batch transformations, real-time Streaming Analytics, and machine learning workloads. The platform executes distributed memory-centric computations across heterogeneous storage layers using unified APIs in Python, Scala, Java, SQL, and R.N/A

    SAP BW

    Score8.8 out of 10
    N/ASAP Business Warehouse, or SAP BW (formerly SAP NetWeaver Business Warehouse) is SAP's legacy data warehouse solution, now superseded by SAP BW/4HANA, and the SAP Data Warehouse Cloud which was launched in 2019. SAP BW versions up to 7.4 have reached end of maintenance. SAP BW 7.5 support is extended to align with SAP Business Suite with NetWeaver components. For existing customers maintenance is scheduled to continue through 2027, with extended support available through 2030.N/A
    Pricing
    Apache SparkSAP BW
    Editions & Modules
    No answers on this topic
    No answers on this topic
    Offerings
    Pricing Offerings
    Apache SparkSAP BW
    Free Trial
    NoNo
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Community Pulse
    Apache SparkSAP BW
    Considered Both Products
    Apache
    Chose Apache Spark
    Databricks uses Spark as a foundation, and is also a great platform. It does bring several add-ons, which we did not feel needed by the time we evaluated - and haven't needed since then. One interesting plus in our opinion was the engineering support, which is great depending …
    Incentivized
    SAP
    No answer on this topic
    Key User Insights
    Would buy again
    100%
    Would buy again
    11 Answers
    100%
    Would buy again
    8 Answers
    Delivers good value for the price
    100%
    Delivers good value for the price
    11 Answers
    100%
    Delivers good value for the price
    7 Answers
    Happy with the feature set
    100%
    Happy with the feature set
    11 Answers
    100%
    Happy with the feature set
    8 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    8 Answers
    No answers on this topic
    Implementation went as expected
    100%
    Implementation went as expected
    11 Answers
    100%
    Implementation went as expected
    8 Answers
    Features
    Apache SparkSAP BW
    Access Control and Security
    Comparison of Access Control and Security features of Apache Spark and SAP Business Warehouse
    Feature
    Apache Spark
    -
    Ratings
    SAP Business Warehouse
    9.5
    7 Ratings
    6% above category average
    Multi-User Support (named login)00 Ratings9.47 Ratings
    Multiple Access Permission Levels (Create, Read, Delete)00 Ratings9.77 Ratings
    Single Sign-On (SSO)00 Ratings9.37 Ratings
    Location-Based Data Governance00 Ratings9.87 Ratings
    Data Modeling
    Comparison of Data Modeling features of Apache Spark and SAP Business Warehouse
    Feature
    Apache Spark
    -
    Ratings
    SAP Business Warehouse
    9.3
    6 Ratings
    4% below category average
    Data model creation00 Ratings9.36 Ratings
    Data Exploration
    Comparison of Data Exploration features of Apache Spark and SAP Business Warehouse
    Feature
    Apache Spark
    -
    Ratings
    SAP Business Warehouse
    7.7
    7 Ratings
    2% below category average
    Visualization00 Ratings7.77 Ratings
    Data Warehouse
    Comparison of Data Warehouse features of Apache Spark and SAP Business Warehouse
    Feature
    Apache Spark
    -
    Ratings
    SAP Business Warehouse
    8.0
    7 Ratings
    7% below category average
    High-Volume Data Processing00 Ratings8.57 Ratings
    Data Warehouse Management00 Ratings9.07 Ratings
    Administrative Automation00 Ratings7.27 Ratings
    Self-Optimization00 Ratings7.27 Ratings
    Best Alternatives
    Apache SparkSAP BW
    Small Businesses
    No answers on this topic
    No answers on this topic
    Medium-sized Companies
    No answers on this topic
    Cloudera Enterprise Data Hub
    Score9 out of 10
    Enterprises
    No answers on this topic
    Oracle Exadata
    Score9.8 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Apache SparkSAP BW
    Likelihood to Recommend
    9.0
    (24 ratings)
    8.3
    (18 ratings)
    Likelihood to Renew
    10.0
    (1 ratings)
    -
    (0 ratings)
    Usability
    8.0
    (4 ratings)
    9.0
    (4 ratings)
    Support Rating
    8.7
    (4 ratings)
    -
    (0 ratings)
    User Testimonials
    Apache SparkSAP BW
    Likelihood to Recommend
    Apache
    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
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    SAP
    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
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    Pros
    Apache
    • 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
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    SAP
    • 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
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    Cons
    Apache
    • 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
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    SAP
    • 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
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    Likelihood to Renew
    Apache
    Capacity of computing data in cluster and fast speed.
    Read full review
    SAP
    No answers on this topic
    Usability
    Apache
    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
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    SAP
    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
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    Support Rating
    Apache
    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.
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    SAP
    No answers on this topic
    Alternatives Considered
    Apache
    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
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    SAP
    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
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    Return on Investment
    Apache
    • 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
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    SAP
    • 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
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