IBM Analytics Engine 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 8.7. IBM holds a 2.4% mindshare in H, compared to Apache’s 10.6% mindshare. Additionally, 100% of IBM users are willing to recommend the solution, compared to 85% of Apache users who would recommend it.

IBM Analytics Engine

Read 1 IBM Analytics Engine review

153 Views
66 Comparison Views

100% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

696 Views
483 Comparison Views

85% willing to recommend

IBM Analytics Engine

Spark SQL

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Executive Summary

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

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

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Buyer's Guide

Hadoop

June 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 Analytics Engine

Ranking in Hadoop

10th

Average Rating

8.0

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 June 2025, in the Hadoop category, the mindshare of IBM Analytics Engine is 2.4%, up from 0.9% compared to the previous year. The mindshare of Spark SQL is 10.6%, down from 11.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

Saket Pandey

Product Manager at a hospitality company with 51-200 employees

Good solution for small and medium-sized businesses and highly stable

I would advise instead of only going through other reviews; it would be great if you could schedule a talk with the IBM team that would be helping you implement this solution. They would deep dive into the process and protocols you are currently set up in, and then they will provide you an optimal solution and optimal price. So I believe talking with the support team was really amazing. They even helped us in some other parts as well. It is a good solution for small and medium-sized businesses. Overall, I would rate the solution an eight out of ten because of the support team. They were able to resolve issues, which helped us deploy higher-grade solutions correctly and quickly. We were able to ensure that our processes were working correctly, and we saved about 15-16% of a week's time by using this solution. In terms of return on investment, we saved about $7,000 a month.

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SurjitChoudhury

Data engineer at Cocos pt

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.

Read full review

Quotes from Members

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

Pros

"The best part was that we could make minor changes in the way we were bifurcating the data, even at a very small scale. The accuracy of conversion was also very high."

"The solution is easy to understand if you have basic knowledge of SQL commands."

"I find the Thrift connection valuable."

"The speed of getting data."

"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."

"The performance is one of the most important features. It has an API to process the data in a functional manner."

"This solution is useful to leverage within a distributed ecosystem."

"The stability was fine. It behaved as expected."

"Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that."

More Spark SQL pros

Cons

"One area for improvement would be the initial setup stage, which took longer than expected."

"There are many inconsistencies in syntax for the different querying tasks."

"In the next update, we'd like to see better performance for small points of data. It is possible but there are better tools that are faster and cheaper."

"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."

"This solution could be improved by adding monitoring and integration for the EMR."

"It would be useful if Spark SQL integrated with some data visualization tools."

"There should be better integration with other solutions."

"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."

"Anything to improve the GUI would be helpful."

More Spark SQL cons

Pricing and Cost Advice

Information not available

"We use the open-source version, so we do not have direct support from Apache."

"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."

"The solution is open-sourced and free."

"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 bundled with Palantir Foundry at no extra charge."

"There is no license or subscription for this solution."

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Top Industries

By visitors reading reviews

No data available

Financial Services Firm

18%

Computer Software Company

14%

Retailer

10%

Healthcare Company

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.

See all answers

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.

See all answers

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 ...

See all answers

Comparisons

HPE Ezmeral Data Fabric vs IBM Analytics Engine

Compared 50% of the time

More IBM Analytics Engine Competitors

Apache Spark vs Spark SQL

Compared 47% of the time

SAP HANA vs Spark SQL

Compared 13% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 12% of the time

HPE Ezmeral Data Fabric vs Spark SQL

Compared 10% of the time

Amazon EMR vs Spark SQL

Compared 9% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

Hadoop

June 2025

Download IBM Analytics Engine product report

Buyer's Guide

Spark SQL

May 2025

Download Spark SQL product report

Overview

IBM Analytics Engine provides an architecture for Hadoop clusters that decouples the compute and storage tiers. Instead of a permanent cluster formed of dual-purpose nodes, the Analytics Engine allows users to store data in an object storage layer such as IBM Cloud Object Storage and spins up clusters of compute notes when needed. Separating compute from storage helps to transform the flexibility, scalability and maintainability of big data analytics platforms.

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: June 2025.

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