IBM Analytics Engine vs Spark SQL comparison

IBM and Apache are both solutions in the Hadoop category. IBM is ranked #8 with an average rating of 8.0, while Apache is ranked #4 with an average rating of 8.3. IBM holds a 1.6% 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 Analytics Engine

Read 1 IBM Analytics Engine review

136 Views
67 Comparison Views

100% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

1,139 Views
835 Comparison Views

85% willing to recommend

IBM Analytics Engine

Spark SQL

Comparison Buyer's Guide

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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.

To learn more, read our detailed Hadoop Report (Updated: November 2024).

Buyer's Guide

Hadoop

November 2024

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Categories and Ranking

IBM Analytics Engine

Ranking in Hadoop

8th

Average Rating

8.0

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 November 2024, in the Hadoop category, the mindshare of IBM Analytics Engine is 1.6%, up from 0.6% 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

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.

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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 speed of getting data."

"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."

"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."

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

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

"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."

"I find the Thrift connection valuable."

More Spark SQL pros

Cons

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

"Anything to improve the GUI would be helpful."

"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."

"There should be better integration with other solutions."

"Being a new user, I am not able to find out how to partition it correctly. I probably need more information or knowledge. In other database solutions, you can easily optimize all partitions. I haven't found a quicker way to do that in Spark SQL. It would be good if you don't need a partition here, and the system automatically partitions in the best way. They can also provide more educational resources for new users."

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

"SparkUI could have more advanced versions of the performance and the queries and all."

"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."

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

More Spark SQL cons

Pricing and Cost Advice

Information not available

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

"The solution is bundled with Palantir Foundry at no extra charge."

"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 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

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What do you like most about IBM Analytics Engine?

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.

See all answers

What is your experience regarding pricing and costs for IBM Analytics Engine?

For large enterprises, it's a costly solution. I'd rate its pricing around seven out of ten.

See all answers

What needs improvement with IBM Analytics Engine?

One area for improvement would be the initial setup stage, which took longer than expected. However, the support team was helpful. If the technical requirements for setup were reduced, the solution...

See all answers

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

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Compared 100% of the time

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Compared 61% of the time

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Compared 10% of the time

HPE Ezmeral Data Fabric vs Spark SQL

Compared 10% of the time

Netezza Analytics vs Spark SQL

Compared 9% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 9% of the time

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Product Reports

Buyer's Guide

Hadoop

November 2024

Download IBM Analytics Engine product report

Buyer's Guide

Spark SQL

October 2024

Download Spark SQL product report

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IBM

Apache

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.

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: November 2024.

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