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Apache Spark vs IBM Streams comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Apache Spark
Average Rating
8.4
Reviews Sentiment
7.3
Number of Reviews
67
Ranking in other categories
Hadoop (1st), Compute Service (3rd), Java Frameworks (2nd)
IBM Streams
Average Rating
8.2
Reviews Sentiment
7.2
Number of Reviews
5
Ranking in other categories
Streaming Analytics (22nd)
 

Mindshare comparison

Apache Spark and IBM Streams aren’t in the same category and serve different purposes. Apache Spark is designed for Hadoop and holds a mindshare of 19.3%, up 19.4% compared to last year.
IBM Streams, on the other hand, focuses on Streaming Analytics, holds 1.0% mindshare, up 0.8% since last year.
Hadoop Market Share Distribution
ProductMarket Share (%)
Apache Spark19.3%
Cloudera Distribution for Hadoop22.1%
HPE Ezmeral Data Fabric14.2%
Other44.39999999999999%
Hadoop
Streaming Analytics Market Share Distribution
ProductMarket Share (%)
IBM Streams1.0%
Apache Flink14.6%
Databricks13.1%
Other71.3%
Streaming Analytics
 

Featured Reviews

Omar Khaled - PeerSpot reviewer
Empowering data consolidation and fast decision-making with efficient big data processing
I can improve the organization's functions by taking less time to make decisions. To make the right decision, you need the right data, and a solution can provide this by hiring talent and employees who can consolidate data from different sources and organize it. Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming. To make the right decision, you should have both accurate and fast data. Apache Spark itself is similar to the Python programming language. Python is a language with many libraries for mathematics and machine learning. Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code. Within it, there are many APIs, including SQL APIs, allowing you to write SQL code within a Python function in Apache Spark. You can also use Apache Spark Structured Streaming and machine learning APIs.
Ahmed_Emad - PeerSpot reviewer
A solution for data pipelines but has connector limitations
We have used Kafka for seven years. IBM streams gives you many OOTB features that can boost the time-to-market, especially when it comes to reporting and monitoring for example. Confluent is recognized as one of the leaders in this space and the main reason for this is related to the complete vision of the platform also the large number of connectors. This gives the edge and competitive advatnage.
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Top Industries

By visitors reading reviews
Financial Services Firm
26%
Computer Software Company
11%
Manufacturing Company
7%
Comms Service Provider
7%
Financial Services Firm
25%
Computer Software Company
20%
Government
11%
Comms Service Provider
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business27
Midsize Enterprise15
Large Enterprise32
No data available
 

Questions from the Community

What do you like most about Apache Spark?
We use Spark to process data from different data sources.
What is your experience regarding pricing and costs for Apache Spark?
Apache Spark is open-source, so it doesn't incur any charges.
What needs improvement with Apache Spark?
Regarding Apache Spark, I have only used Apache Spark Structured Streaming, not the machine learning components. I am uncertain about specific improvements needed today. However, after five years, ...
What is your experience regarding pricing and costs for IBM Streams?
The solution’s licenses pricing is different from one region to another region. I rate the solution’s pricing a seven out of ten.
What needs improvement with IBM Streams?
the limited number of connectors. This shall be overcome with work-arounds or eventually buying additional connectors to complete the solution.
What is your primary use case for IBM Streams?
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 exchan...
 

Comparisons

 

Also Known As

No data available
IBM InfoSphere Streams
 

Overview

 

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
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