Pentaho Data Integration and Analytics Reviews

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.

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.

We have an event planning system, which enables us to obtain a large report. It includes data Mart or data warehouse data. This is where we take data from the IT online system and pass it to the data warehouse. Then, from the data warehouse, they generate reports. We have 6 developers who are using the Panel Data Integrator, but there are no end users. We deploy the product, and the customer uses it for reporting. We have one person who undertakes a regular maintenance activity when it is required.

As we are a software company, we are using the tools provided with the Pentaho Data Integration for our various teams.

Pentaho Data Integration is quite simple to learn, and there is a lot of information available online. It is not a steep learning curve. It also integrates easily with other databases and that is great. We use the provided documentation, which is a simple process for integration compared to other proprietary tools.

I don't think they market it that well. We can make suggestions for improvements but they don't seem to take the feedback on board. This contrasts with Informatica who are really helpful and seem to listen more to their customer feedback. I would also really like to see improved data capture. At the moment the emphasis seems to be on data processing. I would like to see a real-time processing data integration tool. This would provide instant reporting whenever the data changes. I'm still in the very recent stage concerning Pentaho Data Integration, but it can't really handle what I describe as "extreme data processing" i.e. when there is a huge amount of data to process. That is one area where Pentaho is still lacking.

We have been using Pentaho Data Integration for 6 years. The customer is using Mirabilis Cloud, which is a public cloud. We are currently using version A.3.

Technical Support is really good. To get our answers only takes a little bit of time.

One of our customers was completely into the Microsoft core framework. We have to use SSIS because it's readily available with them, and is part of the system. We had to use it for five years. 

As mentioned, one of our teams has worked with Informatica in the past. In terms of integration, Informatica isn't more powerful, but more accurate in some aspects. The community is also quite strong.

The setup of Pentaho Data Integration is straightforward. 

We implemented Pentaho Data Integration in-house. The current deployment has taken three months for the current set of requirements. We have another deployment in the pipeline where we are connecting other different data sources. These projects usually take a few months to complete.

Sometimes we provide the licenses or the customer can procure their own licenses. Previously, we had an enterprise license. Currently, we are on a community license as this is adequate for our needs.

For newcomers to the product, it is best to start with something simple. You can then scale it up fast as it is not a steep learning curve. If somebody wants to set up a good inbound integration platform, they can use the Panel Data Integrator. It's really simple and easy to use. The online community really helps you with numerous issues, such as licensing and a lot of other things. I would rate Pentaho Data Integration 8 out of 10.

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 as an ETL tool. We take data from a source database and move it into a target database. We do some additional processing on our target databases as well, and then load the data into a data warehouse for reports. The end result is a data warehouse and the reports built on top of that.

We are a consulting company and we implement it for clients.

As a result of one of the projects that we did in the Middle East, we achieved the main goal of fully digitalizing their population census. They did previous censuses doing door-to-door surveys, but for the last census, using Pentaho Data Integration, we managed to get it all running in a fully digital way, with nothing on paper forms. No one had to go door-to-door and survey the people.

One of the valuable features is the ability to use PL/SQL statements inside the data transformations and jobs.

I would like to see support for some additional cloud sources - Azure, Snowflake.

I have been using Hitachi Lumada Data Integration for four years.

There have been some bugs and some weird things every now and then, but it is mostly fairly stable.

If you work with relatively small data sets, it's all okay. But if you are going to use really huge data sets, then you might get into a bit of trouble, at least from what I have seen.

The support from Hitachi is not the greatest, the fixing of bugs can take a really long time.

Neutral

The initial setup is very straightforward compared to many other ETL tools. It takes about half a day.

We have about five users, altogether. There are two to three developers, one to two customer people who run the ETLs and one to two admins who take care of the environment itself. It doesn't require much maintenance. Occasionally someone has to restart the server or take a look at logs.

Because you can basically get Pentaho Data Integration for free, I would give the cost versus performance a pretty good rating.

Taking the census project that I mentioned earlier as an example (Pentaho Data Integration was not the only contributor though, it was just one part of the whole solution) the statistical authority managed to save huge amounts of money by making the census electronic, versus the traditional version.

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. 

If you pay for licenses, the only thing that you're getting, in addition, is customer support, which is pretty much nonexistent in any case. I would recommend going with the Community Edition.

I have had experience with other solutions, but for the last project we did not evaluate other options. Because we had previous experience with Pentaho Data Integration, it was pretty much a no-brainer to use it.

Hitachi Vantara's roadmap is promising. They came up with Lumada and it seems that they do have some ideas on how to make their product a bit more attractive than it currently is.

I'm fairly satisfied with using Pentaho Data Integration. It's more or less okay. When it comes to all the other parts, like Pentaho reports and Pentaho dashboards, things could be better there.

The biggest lesson I've learned from using this solution is that a cheap, open-source tool can sometimes be even more efficient than some of the high-priced enterprise ETL tools. Overall, the solution is okay, considering the low cost. It has all of the main things that you would expect it to have, from a technical perspective.

We use it to connect to multiple databases and generate reporting. We also have ETL processes running on it.

Portions of it are in AWS, but we also have desktop access.

The solution has allowed us to automate reporting by automating its scheduling. 

It is also important to us that the solution enables you to leverage metadata to automate data pipeline templates and reuse them. It allows us to generate reports with fewer resources.

If we didn't have this solution, we wouldn't be able to manage our workload or generate the volume of reporting that we currently do. It's very important for us that it provides a single, end-to-end data management experience from ingestion to insights. We are a high-volume department and without those features, we wouldn't be able to manage the current workload.

We're using the PDI and the repository function, and they give us the ability to easily generate reporting and output, and to access data. We also like the ability to schedule.

Since Hitachi took over, I don't feel that the documentation is as good within the solution. It used to have very good help built right in. There's good documentation when you go to the site but the help function within the solution hasn't been as good since Hitachi took over.

I've been using Lumada Data Integration since 2016, but the company has been using it much longer.

We are currently on version 8.3, but we're going to be doing an upgrade to 9.2 next month.

The stability is good. We haven't had any issues related to Pentaho.

Its scalability is very good. We use it with multiple, large databases. We've added to it over time and it scales.

We have about 10 users of the solution including a data quality manager, clinical analyst, healthcare informatics analysts, senior healthcare informatics analyst, and an analytics team leader. It's used very extensively by all of those job roles in their day-to-day work. When we add additional staff members, they routinely get access to and are trained on the solution.

Their ability to quickly and effectively solve issues we have brought up is very good. They have a ticketing system and they're very responsive to any tickets we enter. And that's true not only for issues but if we have questions about functionality.

Positive

The solution is very flexible. It's pretty easy to set up connections within the solution.

Maintenance isn't required day-to-day. Our technical staff does the upgrades. They also, on occasion, have to do things like restarting the services, but that's typically related to server issues, not Pentaho itself.

My advice would be to take advantage of the training that's offered.

The query performance of Lumada on large data sets is good, but the query performance is really only as good as the server.

In terms of Hitachi's roadmap, we haven't seen it in a little while. We did have a concern that they're going to be going away from Pentaho and rolling it into another product and we're not quite sure what the result of that is going to be. We don't have a good understanding of what's going to change. That's the concern.

We currently only use Pentaho. We don't have other Hitachi products but we're satisfied with it. We would recommend Pentaho.

Ease of use, stability, graphical interface, small amount of "voodoo" and cost.

There some steps that should perform better like the json input, but because of the flexibility we at inflow, override it by using scripting steps. Of course it's ideal to use the steps that come with the software but if you can write your own step that's powerful. Also, it would be nice to have the drivers for the data sources shipped with Pentaho Kettle instead of looking for the right ones on the Internet.

In every project there are issues with the deployment, but we were able to overcome them.

I think that Pentaho Kettle is a stable software, if it wasn't, we wouldn't have used it (because we don’t like angry customers).

Actually, Pentaho Kettle comes equipped with the option to scale out, out of the box.
And no, we didn't encountered specific scalability problems.

We mainly use material which was written over the years (pentaho kettle materials), the forum, Matt casters blog, videos, etc. We also try to solve our issues inside the company for our customers before contacting customer service. We even developed a full-scale data integration Pentaho Kettle online course and built a website for it.

When we use the customer service it's very good. There is a large community for the tool, people gladly help each another.

Very good support.

Before Pentaho Kettle we used stored procedures, writing code and also Informatica. Informatica is a very good tool, but it is not open source so it is far more costly compared to Pentaho Kettle. From my perspective I don't see the difference, we can do almost everything with Pentaho Kettle and if we need a little extra we are tech guys, we solve it.

Of course that from the customer's perspective the cheaper the better, so if the customer has a smaller budget, they get more when using Pentaho Kettle open source. Even with the Pentaho Kettle enterprise edition.

I can say from the vendor perspective- usually the part of the data integration (from data source to the warehouse/target) takes at least 60% of the whole initial business intelligence project. It depends on the data sources and complexity, for example: big data, NoSql, xml, web services, "weird" files and more.

After the data integration project is "live" it will work fine until someone breaks something. (Network connectivity, servers, DBA that changes the data source, or any other change for that matter that changes variables that the data integration was built upon) but this is true for all data integration software.

The day-to-day costs are very low if there are no new requirements. Luckily for us (as a vendor) once the customer starts and the users get their fancy reports and dashboards there's no turning back, and the requirements are piling up. But these are new requirements, not maintenance.

Instead of trying to decide on a specific data integration tool, pick the right vendor partner, not a biased one. They will be able to recommend the set of tools you need according to your requirements and budget.

Business intelligence project are made up of at least three components:

• 1. Data integration tool
• 2. Data warehouse tool
• 3. Visualization tool
  

Several of the software vendors have them all, but not the best solution for each component. From my experience it's better to combine solutions. (Unless it is a small project.)

For example: data integration from Pentaho Kettle, if it's big data we need an in memory/ columnar database for data warehouse but if it's not we can use traditional databases (SQL Server, Oracle, even MySQL for smaller projects) and a BI visualization tool like Yellowfin/Tableau/Sisense/etc.

In the middle you have tens of software vendors that can be suitable for the customer needs.

Of course if the vendor partner is biased then suddenly Tableau/Sisense/Qlikview/etc. become the best data integration tool. Or "you don’t need a data integration tool at all" although they don't have the right components. (They are very good tools for visualization but not for "playing" extract and transform complex data). We work with several vendors such as Sisense and Yellowfin which are are great tool for the specific solution they were made for.

The most valuable thing for me is that it enables a technical product manager to be able to write ETL jobs themselves, which saves developers time so that they can do more important things.

Now developers focus on improving it as a tool (since it's open source) and teach Project Managers about it. The Project Managers are the ones responsible for their own ETL jobs as they know what they want, so hence it's best for them to manage their own jobs.

Its performance can be improved so it will work better with Big Data. Also, sometimes it can be very buggy which keeps away some potential users.

I've used it for two years.

We have had no issues with the deployment.

The performance for Big Data needs to be improved.

We have had no issues scaling it for our needs.

There is a community that can support limited technical help. I'll give a 6 to the community since it's not very active.

It was already in place when I joined the company.

It's very easy to install.

We did it in-hous. It's worth it to have someone in your company who knows Pentaho really well.

ROI is pretty good since it is kind of a major thing in our company.

The only cost is the time it takes for the developer to get to know it.

If your ETL jobs are small and straightforward, then this solution is definitely worth it.