Pentaho Data Integration and Analytics vs Python Connectors comparison

Hitachi Vantara and Devart are both solutions in the Data Integration category. Hitachi Vantara is ranked #10 with an average rating of 7.6, while Devart is ranked #33 with an average rating of 10.0. Hitachi Vantara holds a 1.5% mindshare in DI, compared to Devart’s 0.7% mindshare. Additionally, 95% of Hitachi Vantara users are willing to recommend the solution, compared to 100% of Devart users who would recommend it.

Pentaho Data Integration an...

Read 59 Pentaho Data Integration and Analytics reviews

3,595 Views
2,840 Comparison Views

95% willing to recommend

Python Connectors

Read 1 Python Connectors review

587 Views
581 Comparison Views

100% willing to recommend

Pentaho Data Integration an...

Python Connectors

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Executive SummaryUpdated on Nov 23, 2025

Pentaho Data Integration and Analytics and Python Connectors compete in data processing enhancement. Pentaho has an upper hand in usability, while Python Connectors are favored for flexibility by technical teams.

Features: Pentaho provides extensive ETL capabilities, data transformation tools, and a wide range of analytics functionalities. Its pre-built interfaces make it ideal for rapid implementation. Python Connectors offer versatile connectivity, the ability to handle complex workflows, and custom script integration, appealing to developers.

Ease of Deployment and Customer Service: Pentaho has a straightforward deployment model with intuitive setup and accessible customer service through multiple channels. Python Connectors require higher technical knowledge for deployment and rely on community forums for support, beneficial for those familiar with open-source solutions.

Pricing and ROI: Pentaho presents moderate initial setup costs, focusing on scalable solutions and promising notable ROI in data management. Python Connectors, with lower upfront investment, offer significant ROI through extensive data handling and adaptability, justifying potential higher ongoing development costs.

To learn more, read our detailed Data Integration Report (Updated: January 2026).

Buyer's Guide

Data Integration

January 2026

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Helped 881,707 peers since 2012

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

Pentaho Data Integration an...

Ranking in Data Integration

10th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Python Connectors

Ranking in Data Integration

33rd

Average Rating

10.0

Reviews Sentiment

8.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of February 2026, in the Data Integration category, the mindshare of Pentaho Data Integration and Analytics is 1.5%, up from 1.3% compared to the previous year. The mindshare of Python Connectors is 0.7%. It is calculated based on PeerSpot user engagement data.

Data Integration Market Share Distribution
Product	Market Share (%)
Pentaho Data Integration and Analytics	1.5%
Python Connectors	0.7%
Other	97.8%

Data Integration

Featured Reviews

Michelle Lawson

Principal Software Engineer at a tech vendor with 10,001+ employees

Streamlines complex data workflows and has supported automated customer payment notifications

I haven't used Pentaho Data Integration and Analytics in a couple of years, so I don't know how it can be improved. I was pretty pleased with it and was self-taught on it, working a lot with their team at various times, but they were surprised that I was able to learn it all by myself. The documentation is not bad, and documentation is the main thing that any product can do to make themselves better because the easier it is to find examples of what you're trying to do improves the learning curve. I think it took me the longest to learn how to do the asynchronous processing and have things wait for other things to finish processing before continuing on in the workflow. I choose 8 out of 10 because the one reason that it's been rejected at T-Mobile is that everything has to go through a provisioning process and has to get approved, meaning the actual code base has to be investigated by T-Mobile before they'll allow us to use tools of that nature. For whatever reason, we just haven't been able to get that approval; I don't know if it's on Pentaho Data Integration and Analytics' side or if it's on our side. The more you can make it easier for companies to feel comfortable that your product is secure, robustly tested and bug-free, and free of any other kind of negative hacks, the more quickly it will get accepted.

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reviewer2761659

Product Engineer at a tech vendor with 10,001+ employees

Has improved data integration speeds and strengthened security through secure authentication

In my experience, the best features Python Connectors offers are easy database connectivity, support for secure authentication, and encryption options. We have used encrypted connections and secure authentication methods, so no plain text credentials have helped us, and we found it effective. We found that Python Connectors is compliance-friendly, very secure, and has no plain text passwords or anything.

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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

"I find the drag and drop feature in Pentaho Data Integration very useful for integration."

"Pentaho Data Integration is quite simple to learn, and there is a lot of information available online."

"It makes it pretty simple to do some fairly complicated things. Both I and some of our other BI developers have made stabs at using, for example, SQL Server Integration Services, and we found them a little bit frustrating compared to Data Integration. So, its ease of use is right up there."

"Sometimes, it took a whole team about two weeks to get all the data to prepare and present it. After the optimization of the data, it took about one to two hours to do the whole process. Therefore, it has helped a lot when you talk about money, because it doesn't take a whole team to do it, just one person to do one project at a time and run it when you want to run it. So, it has helped a lot on that side."

"The fact that it enables us to leverage metadata to automate data pipeline templates and reuse them is definitely one of the features that we like the best. The metadata injection is helpful because it reduces the need to create and maintain additional ETLs. If we didn't have that feature, we would have lots of duplicated ETLs that we would have to create and maintain. The data pipeline templates have definitely been helpful when looking at productivity and costs."

"One of the most valuable features is the ability to create many API integrations. I'm always working with advertising agents and using Facebook and Instagram to do campaigns. We use Pentaho to get the results from these campaigns and to create dashboards to analyze the results."

"This solution allows us to create pipelines using a minimal amount of custom coding."

"Provides a good open source option."

More Pentaho Data Integration and Analytics pros

"Since we started to use Python Connectors, we found it very reliable, and there are many measurable benefits of this."

Cons

"Parallel execution could be better in Pentaho. It's very simple but I don't think it works well."

"When dealing with substantial data volumes from cloud systems, performance can become an issue; even using the Enterprise Edition, the time required for executing particular pipeline tasks is notably high compared to other ETL tools such as ADF, DataBricks, or SSIS."

"Although it is a low-code solution with a graphical interface, often the error messages that you get are of the type that a developer would be happy with. You get a big stack of red text and Java errors displayed on the screen, and less technical people can get intimidated by that. It can be a bit intimidating to get a wall of red error messages displayed. Other graphical tools that are focused at the power user level provide a much more user-friendly experience in dealing with your exceptions and guiding the user into where they've made the mistake."

"I could not connect to our Hadoop environment in an easy and flexible way, and it was important to scale our data warehouse."

"I would like to see more improvements with AS400 DB2."

"I work with the Community Edition, therefore I do not have support. There was an issue that I could not resolve with community support."

"It's not very stable, at least not in the case of the community edition. I'm working with the community edition right now and I think perhaps it is because of that it is not very stable, it causes the system to sometimes hang. I'm not sure if this is the case for pair tiers."

"Lumada could have more native connectors with other vendors, such as Google BigQuery, Microsoft OneDrive, Jira systems, and Facebook or Instagram. We would like to gather data from modern platforms using Lumada, which is a better approach. As a comparison, if you open Power BI to retrieve data, then you can get data from many vendors with cloud-native connectors, such as Azure, AWS, Google BigQuery, and Athena Redshift. Lumada should have more native connectors to help us and facilitate our job in gathering information from these new modern infrastructures and tools."

More Pentaho Data Integration and Analytics cons

"Python Connectors might get a little less costly, making it a common tool rather than being an expensive or fancy tool."

Pricing and Cost Advice

"The cost of these types of solutions are expensive. So, we really appreciate what we get for our money. Though, we don't think of the solution as a top-of-the-line solution or anything like that."

"It does seem a bit expensive compared to the serverless product offering. Tools, such as Server Integration Services, are "almost" free with a database engine. It is comparable to products like Alteryx, which is also very expensive."

"We are using the Community Edition. We have been trying to use and sell the Enterprise version, but that hasn't been possible due to the budget required for it."

"I think Lumada's price is fair compared to some of the others, like BusinessObjects, which is was the other thing that I used at my previous job. BusinessObject's price was more reasonable before SAP acquired it. They jacked the price up significantly. Oracle's OBIEE tool was also prohibitively expensive."

"You don't need the Enterprise Edition, you can go with the Community Edition. That way you can use it for free and, for free, it's a pretty good tool to use."

"I mostly used the open-source version. I didn't work with a license."

"You need to go through the paid version to have Hitachi Lumada specialized support. However, if you are using the free version, then you will have only the community support. You will depend on the releases from Hitachi to solve some problem or questions that you have, such as bug fixes. You will need to wait for the newest versions or releases to solve these types of problems."

"The price of the regular version is not reasonable and it should be lower."

More Pentaho Data Integration and Analytics pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

14%

Educational Organization

Computer Software Company

Manufacturing Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	18
Midsize Enterprise	17
Large Enterprise	30

No data available

Questions from the Community

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...

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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, ...

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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...

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What needs improvement with Python Connectors?

Python Connectors might get a little less costly, making it a common tool rather than being an expensive or fancy tool. It is much costlier than other products, but considering Python language bein...

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What is your primary use case for Python Connectors?

Python Connectors are mainly used to connect between the front-end and back-end of a project. It may seem like an API or a framework. We have used Python Connectors to create an employee dashboard....

See all answers

What advice do you have for others considering Python Connectors?

Python Connectors is actually very easy and user-friendly and accessible, and the most reliable feature is that it is user-friendly. The person who knows Python from top to end feels a master in it...

See all answers

Comparisons

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Compared 13% of the time

Informatica PowerCenter vs Pentaho Data Integration and Analytics

Compared 8% of the time

SnapLogic vs Pentaho Data Integration and Analytics

Compared 7% of the time

Oracle Data Integrator (ODI) vs Pentaho Data Integration and Analytics

Compared 6% of the time

Qlik Talend Cloud vs Pentaho Data Integration and Analytics

Compared 6% of the time

More Pentaho Data Integration and Analytics Competitors

SSIS vs Python Connectors

Compared 33% of the time

Microsoft Graph Data Connect vs Python Connectors

Compared 18% of the time

MuleSoft Composer vs Python Connectors

Compared 13% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Python Connectors

Compared 12% of the time

Oracle Data Integrator (ODI) vs Python Connectors

Compared 11% of the time

More Python Connectors Competitors

Product Reports

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Pentaho Data Integration and Analytics

January 2026

Pentaho Data Integration and Analytics report

Download Pentaho Data Integration and Analytics product report

Buyer's Guide

Data Integration

January 2026

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Also Known As

Hitachi Lumada Data Integration, Kettle, Pentaho Data Integration

No data available

Overview

Pentaho Data Integration stands as a versatile platform designed to cater to the data integration and analytics needs of organizations, regardless of their size. This powerful solution is the go-to choice for businesses seeking to seamlessly integrate data from diverse sources, including databases, files, and applications. Pentaho Data Integration facilitates the essential tasks of cleaning and transforming data, ensuring it's primed for meaningful analysis. With a wide array of tools for data mining, machine learning, and statistical analysis, Pentaho Data Integration empowers organizations to glean valuable insights from their data. What sets Pentaho Data Integration apart is its maturity and a vibrant community of users and developers, making it a reliable and cost-effective option. Pentaho Data Integration offers a range of features, including a comprehensive ETL toolkit, data cleaning and transformation capabilities, robust data analysis tools, and seamless deployment options for data integration and analytics solutions, making it a go-to solution for organizations seeking to harness the power of their data.

Hitachi Vantara

Python Connectors enable seamless data connectivity between Python applications and external data sources, enhancing integration efficiency for development projects.

Python Connectors streamline the process of integrating Python applications with databases, APIs, and other data platforms. They support diverse data sources and protocols, ensuring robust data exchange and manipulation. By handling complex data interactions, Python Connectors empower developers to focus on application logic and innovation, resulting in time-effective project execution.

What are the essential features of Python Connectors?

Multi-Source Connection: Connect to multiple data sources concurrently, supporting a wide array of databases and APIs.
Data Transformation: Facilitate data transformation with built-in functions, allowing seamless integration and manipulation.
Security Protocols: Ensure data safety through advanced authentication and encryption methods.
Real-Time Data Sync: Keep data updated in real-time, improving decision-making capabilities.
Scalable Architecture: Accommodate user requirements with scalable solutions.

Which benefits and ROI should users consider when evaluating Python Connectors?

Increased Efficiency: Reduce development time, allowing teams to focus on core application functionalities.
Cost Savings: Lower costs by minimizing manual integration efforts and errors.
Improved Data Accuracy: Enhanced data accuracy through automated processes, ensuring reliable outputs.
Flexibility: Adapt to changing data structures with a highly flexible connection approach.
Enhanced Security: Benefit from improved security features, safeguarding sensitive information.

In industries like finance and healthcare, Python Connectors simplify data integration, allowing institutions to manage massive datasets effectively and maintain compliance standards. In e-commerce, they enable real-time inventory updates and customer data analysis, enhancing business agility and customer satisfaction.

Devart

Sample Customers

66Controls, Providential Revenue Agency of Ro Negro, NOAA Information Systems, Swiss Real Estate Institute

Information Not Available

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