Try our new research platform with insights from 80,000+ expert users

Elastic Search vs Pentaho Data Integration and Analytics comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

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

ROI

Sentiment score
6.0
Organizations report increased efficiency and ROI from Elastic Search, with proper implementation and data integration being crucial.
Sentiment score
7.9
Pentaho offers cost-effective integration, reducing ETL time, lowering expenses, and enhancing competitiveness with open-source flexibility and efficiency.
We have not purchased any licensed products, and our use of Elastic Search is purely open-source, contributing positively to our ROI.
 

Customer Service

Sentiment score
6.6
Elastic Search's customer service is supported by a strong community and resources, though response times can be slow.
Sentiment score
5.2
Users rely on community support over customer service due to mixed experiences, despite responsive technical support and Hitachi's involvement.
Communication with the vendor is challenging
 

Scalability Issues

Sentiment score
7.2
Elastic Search offers strong scalability and ease of use but may face challenges with large databases and complex indexes.
Sentiment score
7.3
Pentaho excels in scalability and efficient data handling but faces challenges with exceptionally large data and complex growth scenarios.
I can actually add more storage and memory because I host it in the cloud.
Pentaho Data Integration handles larger datasets better.
 

Stability Issues

Sentiment score
7.7
Elastic Search is stable and reliable for enterprise use, with occasional issues in large-scale data or new releases.
Sentiment score
7.1
Pentaho Data Integration offers reliability for small to midsize operations but may lag and freeze with complex uses.
It's pretty stable, however, it struggles when dealing with smaller amounts of data.
 

Room For Improvement

Elastic Search users seek improved security, scalability, integration, and support, alongside better UI, onboarding, and licensing enhancements.
Pentaho needs improvements in big data performance, error handling, UI, scheduling, backward compatibility, cloud integration, and Python support.
This can create problems for new developers because they have to quickly switch to another version.
Pentaho Data Integration is very friendly, it is not very useful when there isn't a lot of data to handle.
 

Setup Cost

Elastic Search is cost-effective initially but can become expensive with additional nodes and premium features despite flexible licensing.
Pentaho offers a cost-effective solution with its free Community Edition and affordable subscription-based Enterprise Edition for varying needs.
 

Valuable Features

ELK offers fast search, scalable architecture, advanced analytics, and integration with Logstash, X-Pack, for flexible, cost-effective enterprise data management.
Pentaho provides an intuitive, open-source platform for efficient ETL development and data integration with minimal coding and broad compatibility.
Elastic Search makes handling large data volumes efficient and supports complex search operations.
Aggregation is faster than querying directly from a database, like Postgres or Vertica.
I find the drag and drop feature in Pentaho Data Integration very useful for integration.
 

Categories and Ranking

Elastic Search
Average Rating
8.2
Reviews Sentiment
6.7
Number of Reviews
67
Ranking in other categories
Indexing and Search (1st), Cloud Data Integration (9th), Search as a Service (1st), Vector Databases (2nd)
Pentaho Data Integration an...
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
53
Ranking in other categories
Data Integration (22nd)
 

Featured Reviews

Anand_Kumar - PeerSpot reviewer
Captures data from all other sources and becomes a MOM aka monitoring of monitors
Scalability and ROI are the areas they have to improve. Their license terms are based on the number of cores. If you increase the number of cores, it becomes very difficult to manage at a large scale. For example, if I have a $3 million project, I won't sell it because if we're dealing with a 10 TB or 50 TB system, there are a lot of systems and applications to monitor, and I have to make an MOM (Mean of Max) for everything. This is because of the cost impact. Also, when you have horizontal scaling, it's like a multi-story building with only one elevator. You have to run around, and it's not efficient. Even the smallest task becomes difficult. That's the problem with horizontal scaling. They need to improve this because if they increase the cores and adjust the licensing accordingly, it would make more sense.
Ryan Ferdon - PeerSpot reviewer
Low-code makes development faster than with Python, but there were caching issues
If you're working with a larger data set, I'm not so sure it would be the best solution. The larger things got the slower it was. It was kind of buggy sometimes. And when we ran the flow, it didn't go from a perceived start to end, node by node. Everything kicked off at once. That meant there were times when it would get ahead of itself and a job would fail. That was not because the job was wrong, but because Pentaho decided to go at everything at once, and something would process before it was supposed to. There were nodes you could add to make sure that, before this node kicks off, all these others have processed, but it was a bit tedious. There were also caching issues, and we had to write code to clear the cache every time we opened the program, because the cache would fill up and it wouldn't run. I don't know how hard that would be for them to fix, or if it was fixed in version 10. Also, the UI is a bit outdated, but I'm more of a fan of function over how something looks. One other thing that would have helped with Pentaho was documentation and support on the internet: how to do things, how to set up. I think there are some sites on how to install it, and Pentaho does have a help repository, but it wasn't always the most useful.
report
Use our free recommendation engine to learn which Cloud Data Integration solutions are best for your needs.
850,028 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Computer Software Company
17%
Financial Services Firm
15%
Government
9%
Manufacturing Company
8%
Financial Services Firm
22%
Computer Software Company
15%
Government
8%
Manufacturing Company
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about ELK Elasticsearch?
Logsign provides us with the capability to execute multiple queries according to our requirements. The indexing is very high, making it effective for storing and retrieving logs. The real-time anal...
What is your experience regarding pricing and costs for ELK Elasticsearch?
I don't know about pricing. That is dealt with by the sales team and our account team. I was not involved with that.
What needs improvement with ELK Elasticsearch?
I found an issue with Elasticsearch in terms of aggregation. They are good, yet the rules written for this are not really good. There is a maximum of 10,000 entries, so the limitation means that if...
Which ETL tool would you recommend to populate data from OLTP to OLAP?
Hi Rajneesh, yes here is the feature comparison between the community and enterprise edition : https://www.hitachivantara.com/en-us/pdf/brochure/leverage-open-source-benefits-with-assurance-of-hita...
What do you think can be improved with Hitachi Lumada Data Integrations?
In my opinion, the reporting side of this tool needs serious improvements. In my previous company, we worked with Hitachi Lumada Data Integration and while it does a good job for what it’s worth, ...
What do you use Hitachi Lumada Data Integrations for most frequently?
My company has used this product to transform data from databases, CSV files, and flat files. It really does a good job. We were most satisfied with the results in terms of how many people could us...
 

Also Known As

Elastic Enterprise Search, Swiftype, Elastic Cloud
Hitachi Lumada Data Integration, Kettle, Pentaho Data Integration
 

Overview

 

Sample Customers

T-Mobile, Adobe, Booking.com, BMW, Telegraph Media Group, Cisco, Karbon, Deezer, NORBr, Labelbox, Fingerprint, Relativity, NHS Hospital, Met Office, Proximus, Go1, Mentat, Bluestone Analytics, Humanz, Hutch, Auchan, Sitecore, Linklaters, Socren, Infotrack, Pfizer, Engadget, Airbus, Grab, Vimeo, Ticketmaster, Asana, Twilio, Blizzard, Comcast, RWE and many others.
66Controls, Providential Revenue Agency of Ro Negro, NOAA Information Systems, Swiss Real Estate Institute
Find out what your peers are saying about Elastic Search vs. Pentaho Data Integration and Analytics and other solutions. Updated: April 2025.
850,028 professionals have used our research since 2012.