IBM Db2 Big SQL vs Spark SQL comparison

IBM and Apache are both solutions in the Hadoop category. IBM is ranked #10, while Apache is ranked #5 with an average rating of 8.5. IBM holds a 1.3% mindshare in H, compared to Apache’s 9.9% mindshare. Additionally, 100% of IBM users are willing to recommend the solution, compared to 85% of Apache users who would recommend it.

IBM Db2 Big SQL

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94 Comparison Views

Spark SQL

Read 14 Spark SQL reviews

918 Views
706 Comparison Views

85% willing to recommend

IBM Db2 Big SQL

Spark SQL

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Executive Summary

We performed a comparison between IBM Db2 Big SQL and Spark SQL based on real PeerSpot user reviews.

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Buyer's Guide

Hadoop

January 2025

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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

IBM Db2 Big SQL

Ranking in Hadoop

10th

Average Rating

0.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Spark SQL

Ranking in Hadoop

5th

Average Rating

7.8

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of February 2025, in the Hadoop category, the mindshare of IBM Db2 Big SQL is 1.3%, down from 1.6% compared to the previous year. The mindshare of Spark SQL is 9.9%, down from 11.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

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Sahil Taneja

Principal Consultant/Manager at Tenzing

Easy to use and do not require a learning curve

Spark SQL can improve the documentation they have provided. It can be a bit unclear at times. They could improve the documentation a bit more so that we can understand it more easily. Moreover, they could improve SparkUI to have more advanced versions of the performance and the queries and all.

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Top Industries

By visitors reading reviews

No data available

Financial Services Firm

26%

Computer Software Company

15%

Retailer

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

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What do you like most about Spark SQL?

Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline.

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What is your experience regarding pricing and costs for Spark SQL?

We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small.

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What needs improvement with Spark SQL?

In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL. There could be additional features that I haven't explored but the current solution for working ...

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Comparisons

No data available

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Compared 53% of the time

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Compared 12% of the time

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Compared 10% of the time

Netezza Analytics vs Spark SQL

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IBM Analytics Engine vs Spark SQL

Compared 7% of the time

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Product Reports

Buyer's Guide

Hadoop

January 2025

Download IBM Db2 Big SQL product report

Buyer's Guide

Spark SQL

January 2025

Download Spark SQL product report

Overview

IBM Db2® Big SQL is an enterprise-grade, hybrid ANSI-compliant SQL-on-Hadoop engine, delivering massively parallel processing (MPP) and advanced data query. Db2 Big SQL offers a single database connection or query for disparate sources such as Hadoop HDFS and WebHDFS, RDMS, NoSQL databases and object stores. Benefit from low latency, high performance, security, SQL compatibility and federation capabilities to do ad hoc and complex queries.

IBM

Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.

Apache

Sample Customers

Information Not Available

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: January 2025.

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