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Apache Spark vs SAP HANA comparison

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Apache Spark Logo
9,844 views|7,892 comparisons
SAP HANA Logo
11,594 views|7,662 comparisons
Featured Review
Find out what your peers are saying about Apache, Cloudera, Amazon and others in Hadoop. Updated: January 2022.
566,121 professionals have used our research since 2012.
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The processing time is very much improved over the data warehouse solution that we were using.""The features we find most valuable are the machine learning, data learning, and Spark Analytics.""The main feature that we find valuable is that it is very fast.""The solution has been very stable.""I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library.""The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly.""Apache Spark can do large volume interactive data analysis.""AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."

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"Using this solution has given us better details for reporting and analytics.""The in-memory computing and the efficient response time are very good features.""All features are valuable.""We've had good experiences with technical support.""This is a feature-rich product and I like all of them.""The performance in terms of processing time is unmatched due to the in-memory processing capability.""The solution operates well.""The solution is extremely stable. That's the most important aspect of the solution, for our organization. There is no downtime, and the performance is very good."

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Cons
"The logging for the observability platform could be better.""Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available.""The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate.""We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data.""I would like to see integration with data science platforms to optimize the processing capability for these tasks.""Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing.""We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time.""Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."

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"Uses a large amount of RAM and is costly.""It would be nice to know when SAP plans to stop its maintenance of a previous version of SAP ECC ERP because, at this point, anyone utilizing SAP will have no choice but to go on S/4HANA Database.""I'd just like to see some more improvements done on the training, both on the functional training and technical training sides as a part of the complete solution.""The user interface and CRM need to be more user-friendly.""The product is very demanding on memory requirements.""The worst thing about SAP HANA is the price; it's very expensive. The licensing cost, implementation cost, hosting cost, and appliance cost are all high.""The documentation can be improved in the future.""SAP HANA is a very proprietary tool and there's not as much support available for it as there is for an SQL Server (which is more popular)."

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Pricing and Cost Advice
  • "Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
  • "Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."
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  • "It is expensive, which isn't a problem for us because SAP HANA is processing the data so fast."
  • "SAP HANA is an expensive product."
  • "It is expensive."
  • "Setup and licensing require planning and proper budgeting, as it is not cheap."
  • "The price of the solution could be reduced, it is expensive."
  • "The price of this product is good."
  • "We pay $200 on a monthly basis."
  • "The price is high and could be a bit cheaper."
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    Questions from the Community
    Top Answer: 
    I don't think using Apache Spark without Hadoop has any major drawbacks or issues. I have used Apache Spark quite successfully with AWS S3 on many projects which are batch based. Yes for very high… more »
    Top Answer: 
    The solution has been very stable.
    Top Answer: 
    We use the open-source version. It is free to use. However, you do need to have servers. We have three or four. they can be on-premises or in the cloud.
    Top Answer: 
    The solution is easy to scale.
    Top Answer: 
    The pricing is a bit on the high side. The use of hardware does not incur additional costs.
    Top Answer: 
    Since we use BW, we are required to use an SLT tool to carry out the data for generating the reports. But, when it comes to in-memory database in respect of the realtime reporting, I do not see why… more »
    Ranking
    1st
    out of 22 in Hadoop
    Views
    9,844
    Comparisons
    7,892
    Reviews
    8
    Average Words per Review
    457
    Rating
    8.8
    Views
    11,594
    Comparisons
    7,662
    Reviews
    24
    Average Words per Review
    478
    Rating
    8.0
    Comparisons
    Also Known As
    SAP High-Performance Analytic Appliance, HANA
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    Overview

    Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

    The SAP HANA® platform helps you reimagine business by combining a robust database with services for creating innovative applications. It enables real-time business by converging trans-actions and analytics on one in-memory platform. Running on premise or in the cloud, SAP HANA untangles IT complexity, bringing huge savings in data management and empowering decision makers everywhere with new insight and predictive power.

    Offer
    Learn more about Apache Spark
    Learn more about SAP HANA
    Sample Customers
    NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
    Unilever, NHS 24, adidas Group, CHIO Aachen, Hamburg Port Authority (HPA), Bangkok Airways Public Company Limited
    Top Industries
    REVIEWERS
    Financial Services Firm40%
    Computer Software Company20%
    Marketing Services Firm10%
    Non Profit10%
    VISITORS READING REVIEWS
    Computer Software Company23%
    Comms Service Provider21%
    Financial Services Firm11%
    Media Company8%
    REVIEWERS
    Energy/Utilities Company22%
    Logistics Company7%
    Consumer Goods Company7%
    Pharma/Biotech Company7%
    VISITORS READING REVIEWS
    Computer Software Company31%
    Comms Service Provider22%
    Manufacturing Company8%
    Government5%
    Company Size
    REVIEWERS
    Small Business40%
    Midsize Enterprise20%
    Large Enterprise40%
    REVIEWERS
    Small Business33%
    Midsize Enterprise12%
    Large Enterprise55%
    Find out what your peers are saying about Apache, Cloudera, Amazon and others in Hadoop. Updated: January 2022.
    566,121 professionals have used our research since 2012.

    Apache Spark is ranked 1st in Hadoop with 9 reviews while SAP HANA is ranked 1st in Embedded Database Software with 25 reviews. Apache Spark is rated 8.4, while SAP HANA is rated 8.0. The top reviewer of Apache Spark writes "Provides fast aggregations, AI libraries, and a lot of connectors". On the other hand, the top reviewer of SAP HANA writes "Very robust solution with good data access". Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Lambda, AWS Batch and Cloudera Distribution for Hadoop, whereas SAP HANA is most compared with SQL Server, Oracle Database, MySQL, IBM Db2 Database and Oracle Database In-Memory.

    We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.