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 #4 with an average rating of 8.3. 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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111 Comparison Views

Spark SQL

Read 14 Spark SQL reviews

1,046 Views
777 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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Hadoop

December 2024

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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

4th

Average Rating

7.8

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of December 2024, in the Hadoop category, the mindshare of IBM Db2 Big SQL is 1.3%, down from 1.7% compared to the previous year. The mindshare of Spark SQL is 9.9%, down from 12.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

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Lucas Dreyer

Data Engineer at BBD

Processing solution used for data engineering and transformation with the ability to process large datasets

It takes a bit of time to get used to using this solution versus Panda as it has a steep learning curve. You need quite a high level of skill with SQL in general to use this solution. If SQL is not someone's primary language, they might find it difficult to get used to. This solution could be improved if there was a bridge between Panda and Spark SQL such as translating from Panda operations to SQL and then working with those queries that are generated. In a future release, it would be useful to have a real time dashboard versus batch updates to Power BI.

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Top Industries

By visitors reading reviews

No data available

Financial Services Firm

27%

Computer Software Company

15%

Manufacturing Company

Retailer

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 59% of the time

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Compared 11% of the time

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Netezza Analytics vs Spark SQL

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Product Reports

Buyer's Guide

Hadoop

December 2024

Download IBM Db2 Big SQL product report

Buyer's Guide

Spark SQL

November 2024

Download Spark SQL product report

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IBM

Apache

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.

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.

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: December 2024.

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