Apache Spark vs IBM Streams comparison

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2,468 views|1,915 comparisons
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Executive Summary

We performed a comparison between Apache Spark and IBM Streams based on real PeerSpot user reviews.

Find out what your peers are saying about Cloudera, Apache, Amazon and others in Hadoop.
To learn more, read our detailed Hadoop Report (Updated: March 2024).
765,386 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pricing and Cost Advice
  • "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."
  • "Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
  • "We are using the free version of the solution."
  • "Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."
  • "Apache Spark is an expensive solution."
  • "Spark is an open-source solution, so there are no licensing costs."
  • "On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
  • "It is an open-source solution, it is free of charge."
  • More Apache Spark Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:The product’s most valuable features are lazy evaluation and workload distribution.
    Top Answer:They provide an open-source license for the on-premise version. However, we have to pay for the cloud version including data centers and virtual machines.
    Top Answer:They could improve the issues related to programming language for the platform.
    Top Answer:The solution’s licenses pricing is different from one region to another region. I rate the solution’s pricing a seven out of ten.
    Top Answer:the limited number of connectors. This shall be overcome with work-arounds or eventually buying additional connectors to complete the solution.
    Top Answer:We use the solution for data pipeline by modernizing the traditional ETL jobs done through advanced streaming. Another use case is building the g2g streaming platform, which facilitates data exchange… more »
    Ranking
    2nd
    out of 22 in Hadoop
    Views
    2,468
    Comparisons
    1,915
    Reviews
    20
    Average Words per Review
    387
    Rating
    8.6
    15th
    out of 38 in Streaming Analytics
    Views
    678
    Comparisons
    598
    Reviews
    1
    Average Words per Review
    447
    Rating
    7.0
    Comparisons
    Also Known As
    IBM InfoSphere Streams
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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

    IBM Streams is an advanced analytic platform that allows user-developed applications to quickly ingest, analyze and correlate information as it arrives from thousands of data stream sources. The solution can handle very high data throughput rates, up to millions of events or messages per second. Streams helps you analyze data in motion, simplify development of streaming applications, and extend the value of existing systems.
    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
    Globo TV, All England Lawn Tennis Club, CenterPoint Energy, Consolidated Communications Holdings, Darwin Ecosystem, Emory University Hospital, ICICI Securities, Irish Centre for Fetal and Neonatal Translational Research (INFANT), Living Roads, Mobileum, Optibus, Southern Ontario Smart Computing Innovation Platform (SOSCIP), University of Alberta, University of Montana, University of Ontario Institute of Technology, Wimbledon 2015
    Top Industries
    REVIEWERS
    Computer Software Company30%
    Financial Services Firm15%
    University9%
    Marketing Services Firm6%
    VISITORS READING REVIEWS
    Financial Services Firm25%
    Computer Software Company13%
    Manufacturing Company7%
    Comms Service Provider6%
    VISITORS READING REVIEWS
    Financial Services Firm24%
    Computer Software Company15%
    Comms Service Provider6%
    Government5%
    Company Size
    REVIEWERS
    Small Business40%
    Midsize Enterprise19%
    Large Enterprise40%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise12%
    Large Enterprise71%
    VISITORS READING REVIEWS
    Small Business18%
    Midsize Enterprise9%
    Large Enterprise73%
    Buyer's Guide
    Hadoop
    March 2024
    Find out what your peers are saying about Cloudera, Apache, Amazon and others in Hadoop. Updated: March 2024.
    765,386 professionals have used our research since 2012.

    Apache Spark is ranked 2nd in Hadoop with 58 reviews while IBM Streams is ranked 15th in Streaming Analytics with 5 reviews. Apache Spark is rated 8.4, while IBM Streams is rated 8.2. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". On the other hand, the top reviewer of IBM Streams writes "A solution for data pipelines but has connector limitations". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop, whereas IBM Streams is most compared with Confluent, Azure Stream Analytics, Apache Flink, Google Cloud Dataflow and Apache NiFi.

    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.