Pentaho Data Integration and Analytics

I use Pentaho Data Integration  for data integration and ETL processes. I developed with Pentaho  from CoproSema. I work on machine learning projects using Pentaho  in different projects, such as forecasting for clients who have not paid their credit.

I find the drag and drop feature in Pentaho Data Integration  very useful for integration. I can use JavaScript and Java in some notes for ETL development. It's easy to use and friendly, especially for larger data sets. 

I use Pentaho for ETLs while relying on other tools like Power BI for data visualization and Microsoft Fabric  for other tasks.

While Pentaho Data Integration is very friendly, it is not very useful when there isn't a lot of data to handle. Communicating with the vendor is challenging, and this hinders its performance in free tool setups.

It's pretty stable, however, it struggles when dealing with smaller amounts of data.

Pentaho Data Integration handles larger datasets better. It's not very useful for smaller datasets.

Communication with the vendor is challenging, which makes customer service less satisfactory despite being a free tool.

Neutral

I use Pentaho for data integration, however, for machine learning and business intelligence, I rely on other tools such as Power BI and Microsoft Fabric .

The initial setup of Pentaho is easy and straightforward.

Deploying Pentaho usually requires around two people, possibly with roles such as server administrator or technical lead.

I use Power BI for business intelligence, Microsoft Fabric for other tasks, and AWS Glue  for data processing in other projects. I do not have experience with Azure Data Box .

On a scale of one to ten, I would rate Pentaho Data Integration around an eight.

Currently, I am using Pentaho Data Integration  for transforming data and then loading it into different platforms. Sometimes, I use it in conjunction with AWS , particularly S3  and Redshift, to execute the copy command for data processing.

Pentaho Data Integration  is easy to use, especially when transforming data. I can find the necessary steps for any required transformation, and it is very efficient for pivoting, such as transforming rows into columns. It is also free of cost and rich in available transformations, allowing extensive data manipulations.

I experience difficulties when handling millions of rows, as the data movement from one source to another becomes challenging. The processing speed slows down significantly, especially when using a table output for Redshift. The availability of Python code integration as an inbuilt function would be beneficial.

I have been using Pentaho Data Integration since 2018.

I would rate the stability of Pentaho Data Integration as eight out of ten.

Pentaho Data Integration has a scalability rating around 8.5 out of ten, as noted from our experience.

I have contacted customer support once or twice, however, did not receive a response. Therefore, I have not had much interaction with the support team, and their assistance does not seem frequent.

Neutral

Pentaho Data Integration's main competitor is Talend. Many companies are moving towards cloud-based ETL solutions.

The initial setup is simple. It involves downloading the tool, installing necessary libraries, like the JDBC library for your databases, and then creating a connection to start working.

Pentaho Data Integration is low-priced, especially since it is free of cost.

I rate Pentaho Data Integration seven out of ten. I definitely recommend it for small to medium organizations, especially if you are looking for a cost-effective product.

I have a team who has experience with integration. We are service providers and partners. Generally, clients buy the product directly from the company.

It is easy to use, install, and start working with. This is one of the advantages compared to other key vaulting products. The relationship between price and functionality is excellent, resulting in time and money savings of between twenty-five and thirty percent.

One of the advantages is that it is easy to use, install, and start working with. For certain volumes of data, the solution is very efficient.

Pentaho  may be less efficient for large volumes of data compared to other solutions like Talend or Informatica. Larger data jobs take more time to execute.

Pentaho  is more appropriate for jobs with smaller volumes of data.

I have used the solution for more than ten years.

The solution is stable. Generally, one person can manage and maintain it.

Sometimes, for large volumes of data, a different solution might be more appropriate. Pentaho is suited for smaller volumes of data, while Talend is better for larger volumes.

Based on my experience, the solution has been reliable.

Positive

We did a comparison between Talend and Pentaho last year.

The initial setup is straightforward. It is easy to install and start working with.

A team with experience in integration manages the implementation.

The relationship between price and functionality is excellent. It results in time and money savings of between twenty-five and thirty percent.

Pentaho is cheaper than other solutions. The relationship between price and functionality means it provides good value for money.

We evaluated Talend and Pentaho.

I'd rate the solution seven out of ten.

The use cases involve loading the data into the required tables based on the transformations. We do a couple of transformations, and based on the business requirement, we load the data into the required tables.

It's a very lightweight tool. It can be plug-and-played easily and read data from multiple sources. It's a very good tool for small to large companies. People or customers can learn very easily to do the transformations for loading and migrating data. It's a fantastic tool in the open-source community.

When compared to other commercial ETL tools, this is a free tool where you can download and do multiple things that the commercial tools are doing. It's a pretty good tool when compared to other commercial tools. It's available in community and enterprise editions. It's very easy to use.

It is difficult to process huge amounts of data. We need to test it end-to-end and conclude how much is the processing of data. If it is an enterprise edition, we can process the data.

I have been using Pentaho Data Integration and Analytics for 11-12 years.

We process a small amount of data, but it's pretty good.

It's scalable across any machine,

Support is satisfactory. A few of my colleagues are also there, working with Hitachi to provide solutions whenever a ticket or Jira is raised for them. 

Positive

Installation is very simple. When you go to the community and enterprise edition, it's damn simple. Even you can install it very easily.

One person is enough for the installation

The product is quite cheap.

It can quickly implement slowly changing dimensions and efficiently read flat files, loading them into tables quickly. Additionally, "several copies to the stat h enables parallel partitioning. In the Enterprise Edition, you can restart your jobs from where they left off, a valuable feature for ensuring continuity. Detailed metadata integration is also very straightforward, which is an advantage. It is lightweight and can work on various systems.

Any technical guy can do everything end to end.

Overall, I rate the solution a ten out of ten.