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IBM Db2 Big SQL vs Spark SQL comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

IBM Db2 Big SQL
Ranking in Hadoop
10th
Average Rating
0.0
Number of Reviews
0
Ranking in other categories
No ranking in other categories
Spark SQL
Ranking in Hadoop
4th
Average Rating
7.8
Number of Reviews
14
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 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 12.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop
 

Featured Reviews

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SurjitChoudhury - PeerSpot reviewer
Nov 23, 2023
Offers the flexibility to handle large-scale data processing
My experience with the initial setup of Spark SQL was relatively smooth. Understanding the system wasn't overly difficult because the data was structured in databases, and we could use notebooks for coding in Python or Java. Configuring networks and running scripts to load data into the database were routine tasks that didn't pose significant challenges. The flexibility to use different languages for coding and the ability to process data using key-value pairs in Python made the setup adaptable. Once we received the source data, processing it in SparkSQL involved writing scripts to create dimension and fact tables, which became a standard part of our workflow. Setting up Spark SQL was reasonably quick, but sometimes we face performance issues, especially during data loading into the SQL Server data warehouse. Sequencing notebooks for efficient job runs is crucial, and managing complex tasks with multiple notebooks requires careful tracking. Exploring ways to optimize this process could be beneficial. However, once you are familiar with the database architecture and project tools, understanding and adapting to the system become more straightforward.

Quotes from Members

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Pricing and Cost Advice

Information not available
"There is no license or subscription for this solution."
"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."
"We use the open-source version, so we do not have direct support from Apache."
"The solution is bundled with Palantir Foundry at no extra charge."
"The on-premise solution is quite expensive in terms of hardware, setting up the cluster, memory, hardware and resources. It depends on the use case, but in our case with a shared cluster which is quite large, it is quite expensive."
"The solution is open-sourced and free."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
25%
Computer Software Company
17%
Manufacturing Company
6%
University
6%
 

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.
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.
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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Sample Customers

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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: October 2024.
814,649 professionals have used our research since 2012.