Pentaho Data Integration and Analytics Reviews

We use it for two major purposes. Most of the time it is for ETL of data. And based on the loaded and converted data, we are generating reports out of it. A small part of that, the pivot tables and the like, are also on the web interface, which is the more interactive part. But about 80 percent of our developers' work is on the background processes for running and transforming and changing data.

Before, a lot of manual work had to be done, work that isn't done anymore. We have also given additional reports to the end-users and, based upon them, they have to take some action. Based on the feedback of the users, some of the data cleaning tasks that were done manually have been automated. It has also given us a fast response to new data that is introduced into the organization.

Using the solution we were able to reduce our ETL deployment time by between 10 and 20 percent. And when it comes to personnel costs, we have gained 10 percent.

The graphical user interface is quite okay. That's the most important feature. In addition, the different types of stores and data formats that can be accessed and transferred are an important component.

We also haven't had to create any custom Java code. Almost everywhere it's SQL, so it's done in the pipeline and the configuration. That means you can offload the work to people who, while they are not less experienced, are less technical when it comes to logic. It's more about the business logic and less about the programming logic and that's really important.

Another important feature is that you can deploy it in any environment, whether it's on-premises or cloud, because you can reuse your steps. When it comes to adding to your data processing capacity dynamically that's key because when you have new workflows you have to test them. When you have to do it on a different environment, like your production environment, it's really important.

I would like to see better support from one version to the next, and all the more so if there are third-party elements that you are using. That's one of the differences between the Community Edition and the Enterprise Edition. 

In addition to better integration with third-party tools, what we have seen is that some of the tools just break from one version to the next and aren't supported anymore in the Community Edition. What is behind that is not really clear to us, but the result is that we can't migrate, or we have to migrate to other parts. That's the most inconvenient part of the tool.

We need to test to see if all our third-party plugins are still available in a new version. That's one of the reasons we decided we would move from the tool to the completely open-source version for the ETL part. That's one of the results of the migration hassle we have had every time.

The support for the Enterprise Edition is okay, but what they have done in the last three or four years is move more and more things to that edition. The result is that they are breaking the Community Edition. That's what our impression is.

The Enterprise Edition is okay, and there is a clear path for it. You will not use a lot of external plugins with it because, with every new version, a lot of the most popular plugins are transferred to the Enterprise Edition. But the Community Edition is almost not supported anymore. You shouldn't start in the Community Edition because, really early on, you will have to move to the Enterprise Edition. Before, you could live with and use the Community Edition for a longer time.

I have been working with Hitachi Lumada Data Integration for seven or eight years.

The stability is okay. In the transfer from before it was Hitachi to Hitachi, it was two years of hell, but now it's better.

At the scale we are using it, the solution is sufficient. The scalability is good, but we don't have that big of a data set. We have a couple of billion data records involved in the integration. 

We have it in one location across different departments with an outside disaster recovery location. It's on a cluster of VMs and running on Linux. The backend data store is PostgreSQL.

Maybe our design wasn't quite optimal for reloading the billions of records every night, but that's probably not due to the product but to the migration. The migration should have been done in a bit of a different way.

I had contact with their commercial side and with the technical side for the setup and demos, but not after we implemented it. That is due to the fact that the documentation and the external consultant gave us a lot of information about it.

We came from the Microsoft environment to Hitachi, but that was 10 years back. We switched due to the licensing costs and because there wasn't really good support for the PostgreSQL database.

Now, I think the Microsoft environment isn't that bad, and there is also better support for open-source databases.

I was involved in the initial migration from Microsoft to Hitachi. It was rather straightforward, not too complex. Granted, it was a new toolset, but that is the same with every new toolset. The learning curve wasn't too steep.

The maintenance effort is not significant. From time to time we have an error that just pops up without our having any idea where it comes from. And then, the next day, it's gone. We get that error something like three times a year. Nobody cares about it or is looking into the details of it. 

The migrations from one version to the next that we did were all rather simple. During that process, users don't have it available for a day, but they can live with that. The migration was done over a weekend and by the following Monday, everything was up and running again.

We had some external help from someone who knows the product and had already had some experience with implementing the tool.

In terms of ROI, over the years it was a good step to make the move to Hitachi. Now, I don't think it would be. Now, it would be a different story.

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.

When we made the choice, it was between Microsoft, Hitachi, and Cognos. The deciding factor in going with Hitachi was its better support for open-source databases and data stores. Also, the functionality of the Community version was what was needed by most of our customers.

Our experience with the query performance of Lumada on large data sets is that Lumada is not what determines performance. Most of the time, the performance comes from the database or the data store underneath Lumada. Depending on how big your data set is, you have to change or optimize your data store and then you can work with large data sets.

The fine-tuning of the database that is done outside of Lumada is okay because a tool can't provide every insight into every type of data store or dataset. If you are looking into optimization, you have to use your data store optimization tools. Hitachi isn't designed for that, and we were not expecting to have that.

I'm not really that impressed with Hitachi's ability to quickly and effectively solve issues we have brought up, but it's not that bad either. It's halfway, not that good and not that bad.

Overall, our Hitachi solution was quite good, but over the last couple of years, we have been trying to move away from the product due to a number of things. One of them is the price. It's really expensive. And the other is that more and more of what used to be part of the Community Edition functionality is moving to the Enterprise Edition. The latter is okay and its functions are okay, but then we are back to the price. Some of our customers don't have the deeper pockets that Hitachi is aiming for.

Before, it was more likely that I would recommend Hitachi Ventara to a colleague. But now, if you are starting in an environment, you should move to other solutions. If you have the money for the Enterprise Edition, then I would say my likelihood of recommending it, on a scale of one to 10, would be a seven. Otherwise, it would be a one out of 10.

If you are going with Hitachi, go for the Enterprise version or stay away from Hitachi.

It's also really important to think in great detail about your loading process at the start. Make sure that is designed correctly. That's not directly related to the tool itself, but it's more about using the tool and how the loads are transferred.

We use it for orchestration and as an ETL tool to move data from one environment to another, including moving data from on-premises to the cloud and moving operational data from different source systems into the data warehouse.

People are now able to get access to the data when they need it. That is what is most important. All the reports go out on time.

The solution enables us to use one tool that gives a single, end-to-end data management experience from ingestion to insights. From the reporting point of view, we are able to make our customers happy. Are they able to get their reports in time? Are they able to get access to the data that they need on time? Yes. They're happy, we're happy, that's it.

With the automation of everything, if I start breaking it into numbers, we don't have to hire three or four people to do one simple task. We've been able to develop some generic IT processes so that we don't have to reinvent the wheel. I just have to extend the existing pipeline and customize it to whatever requirements I have at that point in time. Otherwise, whenever we would get a project, we would actually have to reinvent the wheel from scratch. Now, the generic pipeline templates that we can reuse save us so much time and money.

It has also reduced our ETL development time by 40 percent, and that translates into cost savings.

Before we used Pentaho, we used to do some of this stuff manually, and some of the ETL jobs would run for hours, but most of the ETL jobs, like the monthly reports, now run within 45 minutes, which is pretty awesome. Everything that we used to do manually is now orchestrated.

And now, with everything in the cloud, any concerns about hardware are taken care of for us. That helps with maintenance costs.

I can use Python, which is open-source, and I can run other scripts, including Linux scripts. It's user-friendly for running any object-based language. That's a very important feature because we live in a world of open-source. With open-source on the table, I am in a position to transform the data where it's actually being moved from one environment to another.

Whether we are working with structured or unstructured data, the tool has been helpful. We are actually able to extend it to read JSON data by creating some Java components.

The solution gives us the flexibility to deploy it in any environment, including on-premises or in the cloud. That is another very important feature.

I would like to see improvement when it comes to integrating structured data with text data or anything that is unstructured. Sometimes we get all kinds of different files that we need to integrate into the warehouse. 

By using some of the Python scripts that we have, we are able to extract all this text data into JSON. Then, from JSON, we are able to create external tables in the cloud whereby, at any one time, somebody has access to this data on the S3 drive.

I've been using Hitachi Lumada Data Integration since 2014.

It's been stable.

We are able to scale our environment. For example, if I had that many workloads, I could scale the tool to run on three instances, and all the workloads would be distributed equally.

Their tech support is awesome. They always answer and attend to any incidents that we raise.

Positive

Everything was done manually in Excel. The main reason we went with Pentaho is that it's open-source.

The deployment was like any other deployment. All the steps are written down in a document and you just have to follow those steps. It was simple for us.

The performance of Pentaho, like any other ETL tool, starts from the database side, once you write good, optimized scripts. The optimization of Pentaho depends on the hardware it's sitting on. Once you have enough RAM on your VM, you are in a position to run any workloads.

Overall it is an awesome tool. We are satisfied with our decision to go with Hitachi's product. It's like any other ETL tool.  It's like SQL Server Integration Services, Informatica, or DataStage. On a scale of one to 10, where 10 is best, I would give it a nine in terms of recommending it to a colleague.

It was our principle to make the whole ETL and data warehousing on our projects. We created a whole step for collecting all the raw data from APIs and other databases from flat files, like Excel files, CSV files, and JSON files, to do the whole transformation and data preparation, then model the data and put it in SQL Server and integration services.

For business intelligence projects, it is sometimes pretty good, when you are extracting something from the API, to have a step to transform the JSON file from the API to an SQL table.

We use it heavily as a virtual machine running on Windows. We have also installed the open-source version on the desktop.

Lumada provides us with a single, end-to-end data management experience from ingestion to insights. This single data management experience is pretty good because then you don't have every analyst doing their own stuff. When you have one unique tool to do that, you can keep improving as well as have good practices and a solid process to do the projects.

It has many resourceful things. It has a variety of the things that you can do. It is also pretty open, since you can put in a Python script or JavaScript for everything. If you don't have the native tool on the application, you can build your own using scripts. You can build your other steps and jobs on the application. The liberty of the application has been pretty good.

Lumada enables us to create pipelines with minimal manual coding efforts, which is the most important thing. When creating a pipeline, you can see which steps are failing in the process. You can keep up the process and debug, if you have problems. So, it creates a good, visual pipeline that makes it easy to understand what you are doing during the entire process.

There is no straight-line explanation about bugs and errors that happen on the software. I must search heavily on the Internet, some YouTube videos, and other forums to know what is happening. The proper site of Hitachi and Lumada doesn't have the best explanation about bugs, errors, and the functions. I must search for other sources to understand what is happening. Usually, it is some guy in India or Russia who knows the answer.

A big problem after deploying something that we do in Lumada is with Git. You get a binary file to do a code review. So, if you need to do a review, you have to take pictures of the screen to show each step. That is the biggest bug if you are using Git.

After you create a data pipeline, if you could make a JSON file or something with another language, we could simplify the steps for creating what we are doing. Or, a simple flat file of text could be even better than that, but generated by their own platform so people can look and see what is happening. You shouldn't need to download the whole project in your own Pentaho, I would like to just look at the code and see if there is something wrong.

When I use it for open-source applications, it doesn't handle big data too well. Therefore, we have to use other kinds of technologies to manage that.

I would like it more accessible for Macs. Previously, I always used Linux, but some companies that I worked for before used MacBooks. It would be good if I could use Pentaho in that too since I need to use other tools or create a virtual machine to use Pentaho. So, it would be pretty good if the solution had a friendly version for Macs or Linux-based programs, like Ubuntu.

I have been using it for six years, but more heavily over the last two years.

I don't bring issues to Hitachi since Lumada is open source in some kind of way. 

Once, when I had a problem with connections because of the software, I saw the issue in the forums on the Internet because there was some type of bug happening.

At my first company, we used just Lumada. At my second company, we used a lot of QlikView, SQL, Python, and Lumada. At my third company, we used Python and SQL much more. I used Lumada just once at that company. At my new company, I don't use it at all. I just use Azure Data Factory and SQL.

With Pentaho, we finally have data pipelines. We didn't have solid data pipelines before. After the data pipelines became very solid, the team who created them became very popular in the company.

To set up the things, we used a virtual machine. It was a version where we can download it and unlock a machine too. You can do Ctrl-C and Ctrl-V with Pentaho because all you need to have is the newest version of Java. So, it was pretty smooth to do the setup. It took an hour maximum to deploy.

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 solution reduced our ETL development time by a lot because a whole project used to take about a month to get done previously. After having Lumada, it took just a week. For a big company in Brazil, it saves a team at least $10,000 a month.

I just use the ETL tool. For data visualization, we are using Power BI. For data storage, we use SQL Server, Azure, or Google BigQuery.

We are just using the open-source application for ETL. We have never looked into other tools of Hitachi because they are paid.

I know other companies who are using Alteryx, which has a friendlier user interface, but they have fewer tools and are more difficult to utilize. My wife uses Alteryx, and I find it is not as good after I used Lumada because they have more solutions and it's open-source. Though, Alteryx has more security and better support.

For someone who wants simple solutions, open-source tools are very perfect for someone who isn't a programmer or knowledgeable about technology. In one week, you can try to understand this solution and do your first project. In my opinion, it is the best tool for people starting out.

Lumada is a great tool. I would rate it as a straight seven out of 10. It gets the work done. The open-source version doesn't work well with big data sources, but there is a lot of flexibility and liberty to do everything you want and need. If the open-source version worked better with big data, then I would give it a straight eight since there is always room for improvement. Sometimes when debugging, some errors can be pretty difficult. It is a tool in principle, when you are starting business intelligence and data engineering, to understand everything that is going on.

We started using Pentaho for two purposes:

1. As an ETL tool to bring data in. 
2. As an analytics tool. 

As our solution progressed, we dropped the ETL piece of Pentaho. We didn't end up using it. What remains in our product today is the analytics tool.

We do a lot of simulations on our data with Pentaho reports. We use Pentaho's reporting capabilities to tell us how contracts need to be negotiated for optimal results by using the analytics tool within Pentaho.

This was an OEM solution for our product. The way it has improved our product is by giving our users the ability to do ad hoc reports, which is very important to our users. We can do predictive analysis on trends coming in for contracts, which is what our product does. The product helps users decide which way to go based on the predictive analysis done by Pentaho. Pentaho is not doing predictions, but reporting on the predictions that our product is doing. This is a big part of our product.

There is an end-to-end flow, where a user can say, "I am looking at this field and want to slice and dice my data based on these parameters." That flexibility is provided by Pentaho. This minimal manual coding is important to us.

The performance could be improved. If they could have analytics perform well on large volumes, that would be a big deal for our products.  

I have been using it for eight years.

We are on-prem. Once the product was installed and up and running, I haven't had issues with the product going down or not being responsive.

We have one technical lead who is responsible for making sure that we keep upgrading the solution so we are not on a version that is not supported anymore. In general, it is low maintenance.

The only complaint that I have with Pentaho has been with scaling. As our data grew, we tested it with millions of records. When we started to implement it, we had clients that went from 80 million to 100 million. I think scale did present a problem with the clients. I know that Pentaho talks about being able to manage big data, which is much more data than what we have. I don't know if it was our architecture versus the product limitations, but we did have issues with scaling.

Our product doesn't deal with big data at large. There are probably 17 million records. With those 17 million records, it performs well when it has been internally cached within Pentaho. However, if you are loading the dataset or querying it for the first time, then it does take awhile. Once it has been cached in Pentaho, the subsequent queries are reasonably fast.

We haven't had a lot of functional issues. We had performance issues, especially early on, as we were trying to spin up this product. The response time from the support group has been a three on a scale of one to five.

We had trouble with the performance and had their engineers come in. We shared our troubles and problems, then those engineers had brainstorming sessions. Their ability to solve problems was really good and I would rate that as four out of five.

A lot of the problems were with the performance and scale of data that we had. It could have been that we didn't have a lot of upfront clean architecture. With the brainstorming sessions, we tried giving two sets of reports to users: 

1. One was more summary level, which was quick, and that is what 80% of our clients use. 
2. For 20% of our clients, we provided detailed reports that do take awhile. However, you are then not impacting performance for 80% of your clients. 

This was a good solution or compromise that we reached from both a business and technology perspective. 

Now, I feel like the product is doing well. It is almost like their team helped us with rearchitecting and building product expectations.

Positive

Previously, we used to have something called QlikView, which is almost obsolete now. We had a lot of trouble with QlikView. Anytime processing was done, it would take a long time for those processed results to be loaded into QlikView's memory. This meant that there was a lot of time spent once an operation was done. Before users could see results or reports, it would take a couple of hours. We didn't want that lag. 

Pentaho offered an option not to have that lag. It did not have its own in-memory database, where everything had to be loaded. That was one of the big reasons why we wanted to switch away from QlikView, and Pentaho fit that need.

I would say the deployment/implementation process was straightforward enough for both data ingestion and analytics.

When we started with the data ingestion, we went with something called Spoon. Then we realized, while it was a Pentaho product, Spoon was open source. We had integrated with the open source version of it, but later found that it didn't work for commercialization. 

For us to integrate Pentaho and get it working, it took a couple of months because we needed to figure out authentication with Pentaho. So, learning and deployment within our environment took a couple of months. This includes the actual implementation and figuring out how to do what we wanted to do.

Because this is a licensed product, the deployment for the client was a small part of the product's deployment. So, on an individual client basis, the deployment is easy and a small piece. 

It gives us the flexibility to deploy it in any environment, which is important to us.

If we went to the cloud version of Pentaho, that would be a big maintenance relief. We wouldn't have to worry about getting the latest version, installing it, and sending it out to our clients.

For the deployment, we had people come in from Pentaho for a week or two. They were there with us through the process.

We looked at Tableau, Pentaho and an IBM solution. In the absence of Pentaho, we would have gone with either Tableau or building our own custom solution. When we were figuring out what third-party tool to use, we did an analysis and a bunch of other tools were compared. Ultimately, we went with Pentaho because it did have a wide variety of features and functionalities within its reports. Though I wasn't involved, there was a cost analysis done and Pentaho did favorably in terms of cost.

For the product that we use Pentaho for, I think we're happy with their decision. There are a few other products in our product suite. Those products ended up using Tableau. I know that there have been discussions about considering Tableau over Pentaho in the future. 

Engage Pentaho's architects early on, so you know what data architecture works best with the product. We built our database and structures, then had performance issues. However, it was too late when we brought in the Pentaho architects, because our data structure was out in the field with multiple clients. Therefore, I think engaging them early on in the data architecture process would be wise.

I am not very familiar with Hitachi's roadmap and what is coming up for them. I know that they are good with sending out newsletters and keeping their customers in the know, but unfortunately, I am unaware of their roadmap.

I feel like this product is doing well. There haven't been complaints and things are moving along. I would rate it as seven out of 10.

We run the payment systems for Canada. We use it as a typical ETL tool to transfer and modify data into a data warehouse. We have many different pipelines that we have built with it.

I love the fact that we haven't come up with a problem yet that we haven't been able to address with this tool. I really appreciate its maturity and the breadth of its capabilities.

If we did not have this tool, we would probably have to use a whole different variety of tools, then our environment would be a lot more complicated.

We develop metadata pipelines and use them.

Flexible deployment, in any environment, is very important to us. That is the key reason why we ended up with these tools. Because we have a very highly secure environment, we must be able to install it in multiple environments on multiple different servers. The fact that we could use the same tool in all our environments, on-prem and in the cloud, was very important to us. 

Because it comes from an open-source background, it has so many different plugins. It is just extremely broad in what it can do. I appreciate that it has a very broad, wide spectrum of things that it can connect to and do. It has been around for a while, so it is mature and has a lot of things built into it. That is the biggest thing. 

The visual nature of its development is a big plus. You don't need to have very strong developers to be able to work with it.

We often have to drop down to JavaScript, but that is fine. I appreciate that it has the capability built-in. When you need to, you can drop down to a scripting language. This is important to us.

The documentation is very basic.

The testing and quality could really improve. Every time that there is a major release, we are very nervous about what is going to get broken. We have had a lot of experience with that, as even the latest one was broken. Some basic things get broken. That doesn't look good for Hitachi at all. If there is one place I would advise them to spend some money and do some effort, it is with the quality. It is not that hard to start putting in some unit tests so basic things don't get broken when they do a new release. That just looks horrible, especially for an organization like Hitachi.

Overall, I have been using it for about 10 years. At my current organization, I have been using it for about seven years. It was used a little bit at my previous organization as well.

The stability is not great, especially when you start patching it a lot because things get broken. That is not a great look. When you start patching, you are expecting things to get fixed, not new things to get broken.

With modern programming, you build a lot of automated testing around your solution, and it is specifically for that. I changed this piece of code. Well, what else got broken? Obviously they don't have a lot of unit tests built into their code. They need to start doing that because it looks horrible when they change one thing, then two other things get broken. Then, they released that as a commercial product, which is horrible. Last time, somehow they broke the ability to connect with databases. That is something incredibly basic. How could you release this product without even testing for that?

We don't have a huge amount of data, so I can't really answer how we could scale up to very large solutions.

Lumada’s ability to quickly and effectively solve issues we have brought up is not great. We have a service for the solution with Hitachi. I don't get the sense that Pentaho, and Hitachi still calls it Pentaho, is a huge center of focus for them. 

You kind of get help, but the people from whom you get help aren't necessarily super strong. It often goes around in circles forever. I eventually have to find my own solution. 

I haven't found that the Hitachi support site has a depth of understanding for the solution. They can answer simple questions, but when it gets more in-depth, they have a lot of trouble answering questions. I don't think the support people have the depth of expertise to really deal with difficult questions.

I would rate them as five out of 10. They are responsive and polite. I don't feel ignored or anything like that, just the depth of knowledge isn't there.

Neutral

It has always been here. There was no solution like it until I got to the company.

The initial setup was complex because we had to integrate with SAML. Even though they had some direction on that, it was really a do-it-yourself kind of thing. That was pretty complicated, so if they want to keep this product fresh, I think they have to work on making it integrate more with modern technology, like single sign-on and stuff like that. Every organization has that now and Pentaho doesn't have a good story for that. However, it is the platform that they don't give a lot of love to.

It took us a long time to figure it out, something like two weeks.

This has reduced our ETL development time. If it wasn't for this solution, we would be doing custom coding. The reason why we are using the solution is because of its simplicity of development.

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.

Apache has a project going on called Apache Hop. Because Pentaho was open sourced, people have taken and forged it. They are really modernizing the solution. As far as I know, Hitachi is not involved yet. I would highly advise them to get involved in that open-source project. It will be the next generation of Pentaho. If they get left behind, they're not going to have anything. It would be a very bad move to just ignore it. Hitachi should not ignore Apache Hop.

I really like the data integration tool. However, it is part of a whole platform of tools, and it is obvious the other tools just don't get a lot of love. We are in it for Pentaho Data Integration (PDI) because that is what we want as our ETL tool. We use their reporting platform and stuff like that, but it is obvious that they just don't get a lot of love or concern.

I haven't looked at the roadmap that much. We are also a Google customer using BigQuery, etc. Hitachi is really just a very niche part of what we do. Therefore, we are not generally looking very seriously at what Hitachi is doing with their products nor a big investor in what Hitachi is doing.

I would recommend this specific Hitachi product to a friend or colleague, depending on their use case and need. If they have a very similar need, I would recommend it. I wouldn't be saying, "Oh, this is the best thing next to sliced bread," but say, "Hey, if this is what you need, this works well for us."

On a scale of one to 10 for recommending the product, I would rate it as seven out of 10. Overall, I would also rate it as seven out of 10.

We really appreciated the breadth of its capabilities. It is not the top-of-the-line solution, but you really get a lot for what you pay for.

The company where I was working previously was using this product. We were using it for ETL process management. It was like a data flow automatization.

In terms of deployment, we were using an on-premise model because we had sensitive data, and there were some restrictions related to information security.

Our data flow processes became faster with this solution.

It has a really friendly user interface, which is its main feature. The process of automating or combining SQL code with some databases and doing the automation is great and really convenient.

As far as I remember, not all connectors worked very well. They can add more connectors and more drivers to the process to integrate with more flows.

The last time I saw this product, the onboarding instructions were not clear. If the process of onboarding this product is made more clear, it will take the product to the next level. There is a possibility that the onboarding process has already improved, and I haven't seen it. 

I have used this solution for two or three years.

I would rate it an eight out of ten in terms of stability.

We didn't have to scale too much. So, I can't evaluate it properly in terms of scalability.

In terms of its users, only our team was using it. There were approximately 20 users. It was not for the whole company.

We didn't use too much customer support. We were using the open-source resources through Google Search. So, we were just using text search. There were some helpful forums where we were able to find the answers to our questions.

I didn't use any other solution previously. This was the only one.

I wasn't a part of its deployment. In terms of maintenance, as far as I know, it didn't require much maintenance.

We absolutely saw an ROI. It was hard to calculate, but we felt it in terms of
the speed of our processes. After using this product, we could do some of the things much faster than before.

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

I did not evaluate other options.

I would recommend using this product for data engineering and Extract, Transform, and Load (ETL) processes.

I would rate it an eight out of ten.

I use it for ETL. We receive data from our clients and we join the most important information and do many segmentations to help with communication between our product and our clients.

Before we used Pentaho, our processes were in Microsoft Excel and the updates from databases had to be done manually. Now all our routines are done automatically and we have more time to do other jobs. It saves us four or five hours daily.

In terms of ETL development time, it depends on the complexity of the job, but if the job is simple it saves two or three hours.

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.

I'm working with large data sets. One of the clients I'm working with is a large credit card company and the database from this client is very large. Pentaho allows me to query large data sets without affecting its performance.

I use Pentaho with Jenkins to schedule the jobs. I'm using the jobs and transformations in Pentaho to create many links. 

I always find ways to have minimal code and create the processes with many parameters. I am able to reuse processes that I have created before. 

Creating jobs and putting them into production, as well as the visibility that Pentaho gives, are both very simple.

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

I've been working with Pentaho for four or five years.

The stability is good. 

It's scalable.

I find help on the forums.

I used SQL Server Integration Services, but I have much more experience with Pentaho. I have also worked with Apache NiFi but it is more focused on single data processes but I'm always working with batch processes and large data sets.

The first deployment was very complex because we didn't have experience with the solution, but the next deployment was simpler.

We create jobs weekly in Pentaho. The development time takes, on average, one week and the deployment takes just one day or so.

We just put it on Git and pull a server and schedule the execution.

We use it on-premises while the infrastructure is Amazon and Azure.

I always recommend Pentaho for working with automated processes and to do API integrations.

I have used this ETL tool for working with data in projects across several different domains. My use cases include tasks such as transforming data that has been taken from an API like PayPal, extracting data from different sources such as Magenta or other databases, and transforming all of the information.

Once the transformation is complete, we load the data into data warehouses such as Amazon Redshift.

There are a lot of different benefits we receive from using this solution. For example, we can easily accept data from an API and create JSON files. The integration is also very good.

I have created many data pipelines and after they are created, they can be reused on different levels.

The best feature is that it's simple to use. There are simple data transformation steps available, such as trimming data or performing different types of replacement.

This solution allows us to create pipelines using a minimal amount of custom coding. Anyone in the company can do so, and it's just a simple step. If any coding is required then we can use JavaScript.

I have been facing some difficulties when working with large datasets. It seems that when there is a large amount of data, I experience memory errors. If there is a large amount of data then there is definitely a lag.

I would like to see a cloud-based deployment because it will allow us to easily handle a large amount of data.

I have been working with Hitachi Lumada Data Integration for almost three years, across two different organizations.

There is definitely some lag but with a little improvement, it will be a good fit.

This is a good product for an enterprise-level company.

We use this solution for all of our data integration jobs. It handles the transformation. As our business grows and the demand for data integration increases, our usage of this tool will also increase.

Between versions, they have added a lot of plugins.

The technical support does not reply in a timely manner. I have filled out the support request form, one or two times, asking about different things, but I have not received a reply.

The support they have in place does not work very well. I would rate them one or two out of ten.

Negative

In this business, they initially began with this product and did not use another one beforehand. I have also worked on the cloud-level integration tool. 

The initial setup and deployment are straightforward.

I have deployed it on different servers and on average, it takes an hour to complete. I have not read any documentation regarding installation. With my experience, we were able to set everything up.

I primarily work on the Community Version, which is available to use free of charge. I have asked for pricing information but have not yet received a response.

We are currently using version 8.3 but version 9 is available. More features to support big data are available in the newest release.

My advice for anybody who is considering this product is if they're looking for any kind of custom transformation, or they're gleaning data from multiple sources and sending it to multiple destinations, I definitely recommend this tool.

Overall, this is a good product and I recommend it.

I would rate this solution an eight out of ten.