Pentaho Data Integration and Analytics Reviews

My main use case for Pentaho Data Integration and Analytics is data transformation between databases, and I also used it for creating mail that needed to go out to customers.

A specific example of a project where I used Pentaho Data Integration and Analytics for data transformation or customer mailings is when my previous company sent emails related to late car payments to our customers. We would have a query that would hit the database, determine who was behind by a certain amount of time on their payments, and then create an email to go out to them with the correct information related to possible repo.

In addition to that, my previous company, Scopos, acted almost as a data hub between different vendors that we used for car financing, which meant we did a lot of transformation of data from one vendor to another vendor to provide files out to the second vendor.

The best features that Pentaho Data Integration and Analytics offers are its versatility with types of data sets that it could ingest, and the steps that it allowed. You could write custom steps in JavaScript or other things, and you could write custom SQL queries.

Out of those features, I rely on the drag-and-drop workflows the most day-to-day, but it is also really good at doing asynchronous processing, enabling it to take data sets from multiple sources and combine them into a single data set, which is important since we are dealing with a lot of data.

Pentaho Data Integration and Analytics has positively impacted my organization because it meant we didn't have to write a lot of custom API back-end processing logic; it did the majority of that heavy lifting for us. I actually, at my current company, T-Mobile, use Azure Data Factory, and it's not nearly as robust or capable in some of the features that it has; it's very Microsoft-centric, so I'm not nearly as pleased as I was with Pentaho Data Integration and Analytics.

Specific outcomes that show how Pentaho Data Integration and Analytics made things easier or more efficient for my team include the fact that my team was about four people, and we had about 26 partner companies that we either pulled data from, pushed data to, combined data, processed data, and sent it over to our own analytics team, which meant a lot less coding that our team had to do; we more had to set up the workflows and add any redundancy that we needed.

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.

I have been using Pentaho Data Integration and Analytics for about six years.

Pentaho Data Integration and Analytics was stable in my use of it; we never had any issues.

Pentaho Data Integration and Analytics seemed pretty scalable since we could spin up multiple jobs and run them in parallel; we didn't have any issues with scalability.

I didn't really have to use customer support that often, but when I did, they responded pretty quickly. For the most part, I never had any problems with the product.

Positive

I have seen a return on investment; my team was able to stay extremely small even though we had a lot of data integrations with many companies. My entire IT team was about four people that used the tool while our company at that time was over a $300 million company with over two dozen integrations with various companies.

My advice for others looking into using Pentaho Data Integration and Analytics is that you have to understand your use cases. Many companies choose other tools because they're very narrow in their tech stack, but my previous company was very broad in our tech stack, which required a lot of customization. Pentaho Data Integration and Analytics allows the ability to customize steps, write your own steps, and integrates with many different types of databases or types of languages, making this use case probably the best reason for people to choose Pentaho Data Integration and Analytics.

I wish I could use it again; I prefer Pentaho Data Integration and Analytics. I gave this review a rating of 8 out of 10.

Hybrid Cloud

Amazon Web Services (AWS)

I have been using Pentaho Data Integration and Analytics for approximately four years.

My main use case for Pentaho Data Integration and Analytics is data transformation related to the telecommunications sector. For example, I send notifications via SMS depending on the type of mobile plan contracted with the telecom company. Different flows are based on user segmentation, and therefore different SMS messages with different content are sent.

First, raw data comes in as an unpolished block of data where different values can appear in lowercase, uppercase, with an underscore, without an underscore, with a space, and so on. I perform basic data processing first. Once the data is in a uniform form, it goes through a series of filters where I start to categorize the user by the type of telecom plan contracted. Then there is another component in which I have the different SMS codes and texts depending on a key-value pair, which is the type of contract the user has. I match what comes from the raw data with how it is correctly categorized according to the filters and with the different type of text that users will receive, for example, to do the proration of plan changes since rate changes are made annually and the amount is increased.

Pentaho Data Integration and Analytics has been very useful for me in user segmentation when rate changes were made because prices change in the telecommunications sector, and depending on each user's billing cycle, I have to do proration for different days. One month might have eighteen days with the old plan and thirteen days with the new plan. This has been a very important use case in my career. I have also worked with Net Promoter Score, which is very important in the telecommunications sector. I have worked with all the information from all the segments, all the regions, divided by province, by product contracted, divided by operating system, by everything. I have worked on Net Promoter Score with Pentaho Data Integration and Analytics, and it has been very easy to handle everything there because it was quite easy to work with.

I think that in recent years the focus has been mainly on improving the interface of Pentaho Data Integration and Analytics to make it more intuitive and easier to understand how each type of tool works within a Data Integration flow. At the beginning, I think for a basic user it could have been more complicated ten years ago, but now the box itself, the icon, the text that defines the specific tool makes it easier to create the flow, and I don't have to resort much to forums or help or any documentation element to support me.

For managing very large volumes of data, Pentaho Data Integration and Analytics is not the best tool, but it is in a good position to handle millions of records. I am not talking about something so focused on Big Data, or at least that is not how I am using it. I think the advantages are that the integration is flexible, the graphical part is simple, and the connectivity is broad because it handles both relational databases and any type of file. I have worked with Excel as well as CSV, with JSONs, and I have also worked in MySQL directly connecting to the original source. I think it is robust, and although I haven't done batch-type loads, the working system is quite robust for that approach. I can orchestrate both simple transformations and jobs where I group many transformations, which also helps to modularize things so that when I find a bug it is in one module and not another and not in the entire complex process. It is helpful in that sense to create mini tasks that are inside a main task. I think it is well integrated with the rest of the analytics suite because I can later feed dashboards or do reporting directly from the ETL. It does not generate many errors, and that is also something to highlight. It doesn't crash, it doesn't close, and it withstands large data processing quite well.

In a specific project I was working on, I was hired to do the profit and loss statement in a telecommunications company where all the profit and loss work was done in Excel. It took much longer, it often got stuck, it could close unexpectedly, and I depended on numerous documents that had to be integrated one by one. When I started doing it and did the migration so that all that ETL process would be done in Pentaho Data Integration and Analytics, I reduced the processing time from two and a half hours per run to seven minutes. It was much easier to integrate the documents since they were placed in a specific location for everyone. Both amortizations, recoveries, operating expenses, costs, and all kinds of documents coming from any team were left in the same place, something that previously in Excel was not the case. It has been a very relevant improvement that has allowed me to dedicate those saved hours to other projects or to improving the project itself.

I think Pentaho Data Integration and Analytics could have better adaptation to architectures such as AWS, Azure, or GCP. It would be valuable for the tool to evolve more towards cloud architectures with simpler deployments in containers and with greater horizontal scalability. Pentaho Data Integration and Analytics has worked very well for me at the batch level, but it is not as robust in real time. I think the interface could still be improved to be even more modern and collaborative. The Spoon interface is functional, but it is not the most modern in terms of design and color scheme.

Pentaho Data Integration and Analytics could have real-time processing and automatic alerting, having alerts or automatic notifications when a job fails or when certain data doesn't meet certain rules, beyond manual configurations that I have to set up myself.

I consider Pentaho Data Integration and Analytics quite stable. I would give it an eight to nine out of ten.

As far as I have worked with Pentaho Data Integration and Analytics, I think the scalability has been moderately good. It is true that it is more oriented towards traditional environments, and it works very well with batch loads of large datasets and corporate ETL processes. However, I think its ability to scale horizontally in cloud-native architectures or for massive real-time processing is limited.

For Pentaho Data Integration and Analytics specifically, I haven't had to contact support, but for other tools in Pentaho Data Integration and Analytics suite, I have, and it was very good.

Positive

I had only worked with Excel before, and I found it much slower and harder to work with, with many more problems, errors, and application crashes without prior warning. This has been the first ETL tool I have worked with, and of all I have worked with, I find it one of the most useful and interesting.

I have noticed a return on investment with Pentaho Data Integration and Analytics in terms of time savings and staff reduction. Economically, in terms of costs, I am not in the line of work where I can evaluate that point.

My experience has been overall good regarding the price, implementation costs, and licenses for Pentaho Data Integration and Analytics because the company covered it and they had no problem paying for it because they saw that it was cost-effective in terms of performance afterwards.

We did not evaluate others; we went directly with Pentaho Data Integration and Analytics due to good references from colleagues.

I do not think the learning curve for Pentaho Data Integration and Analytics is intuitive for a user who has not worked with other ETLs before, but I do think that once the user starts working with it, they get used to it easily and then it is very quick to work with because it doesn't crash, doesn't break, doesn't cause compilation problems. It doesn't require major prior training, but it does require having to review forums and some documentation in order to get started and reach a basic user level. I would rate this product an eight out of ten.

On-premises

My main use case for Pentaho Data Integration and Analytics focuses primarily on integration. We utilize it to handle data from various front-end sources, such as Oracle databases, SAP data, and Salesforce and FIS data.

When I refer to integration, I mean connecting these sources and transforming the data before sending it elsewhere, such as loading the data into Snowflake. After loading the data into Snowflake, we perform transformations, create nodes, aggregations, and visualizations. We schedule tasks daily to transform the data received from the front-end and provide it to the data warehouse for further reporting use and integration into Salesforce.

Pentaho Data Integration and Analytics has positively impacted my organization by saving costs, managing large data sets, and integrating multiple sources. It assists in integrating data from Excel and CSV files quickly. It automates the data workflow, including extraction, cleansing, and loading into warehouses for BI reporting purposes, while also removing duplicates, validating data, and standardizing formats, enabling real-time decision-making.

Pentaho Data Integration and Analytics is highly scalable, able to accommodate growing data volumes and more complex workflows seamlessly.

Pentaho Data Integration and Analytics is generally stable; crashes are rare and only occurred during upgrades or due to JVM memory issues, which are addressed in newer versions.

The best features of Pentaho Data Integration and Analytics include a very friendly user interface. It has a no-code interface, allowing us to modify and create nodes. For example, if we need a Salesforce node that is not available, we can create our own nodes using JavaScript, supported by a drag-and-drop interface.

The no-code interface and custom node creation in Pentaho Data Integration and Analytics have greatly facilitated our work. We can easily search for any nodes from the panel and drag and drop them onto the main canvas. When performing tasks, the code is generated automatically, capturing the SQL queries and parameters seamlessly. Custom node creation allows flexibility in building complex workflows that streamline connection parts, reducing the time spent on writing and debugging and minimizing dependency on IT and specialized developers.

Additionally, we have different job schedulers available. Trigger tasks are sometimes manual, which means we can schedule them as needed. Pentaho Data Integration and Analytics allows us to create trigger tasks, though API calls need some improvement. The cloud version of Pentaho Data Integration and Analytics is much more advantageous than the earlier server-based remote workspace, and the Kettle files we use provide informative transformations. The integration of SQL queries, Salesforce data, and Oracle data is quite effective.

Pentaho Data Integration and Analytics encourages enterprise code, minimizing errors and providing excellent reusability. It fosters collaboration across different environments and modules, covering governance and standardization effectively. For data security, it includes robust features for maintaining PII data confidentiality, and the maintenance aspect is simplified with scheduled logs that help identify and resolve errors more efficiently.

In terms of improvements, while creating on a remote service, we face challenges related to limited memory, which calls for optimization. Additionally, Pentaho Data Integration and Analytics employs row-by-row operations that could benefit from block processing features for sorting and grouping. This improvement would enhance speed and reduce memory usage. We should also explore more effective partitioning for parallel processing and fine-tuning database connections to reduce load times and improve ETL speed.

To enhance the experience further, automation within Pentaho Data Integration and Analytics needs to be more reliable, with a better retry mechanism for failed tasks. I believe that implementing version control for Kettle and job files automatically rather than manually would be beneficial. Additionally, extending the capabilities of plugins and extensions would enhance functionality, especially as new globalization applications arise.

I have been working for more than one and a half years on Pentaho Data Integration and Analytics, focusing on integration and transformation for Power BI reporting and visualization, data ingestion, and transformation purposes. Pentaho Data Integration and Analytics provides us with a robust integration platform, complete with schedulers and job plans that enable us to run tasks daily, schedule tasks, and trigger tasks across various platforms like SAP, Oracle, and Salesforce, also integrating with Snowflake and PostgreSQL.

Pentaho Data Integration and Analytics is generally stable; crashes are rare and only occurred during upgrades or due to JVM memory issues, which are addressed in newer versions.

Pentaho Data Integration and Analytics is highly scalable, able to accommodate growing data volumes and more complex workflows seamlessly.

Our experience with customer support has been positive; they provide effective solutions and take the time to understand our business requirements, offering appropriate recommendations.

Previously, we used an internal tool called IDQMS, which had memory issues and limitations, prompting us to switch to Pentaho Data Integration and Analytics. Pentaho Data Integration and Analytics provided a free development studio, an environment-friendly tool, and excellent scheduling and transformation features, which enhanced our data quality management, especially critical as we transitioned to AWS S3.

I can testify to the return on investment with metrics regarding time saved; we have increased our efficiency by about 20 to 30 percent due to the swift migration processes facilitated by the tool. These enhancements make it easier for us to make prompt decisions regarding technology adoption.

Before choosing Pentaho Data Integration and Analytics, we evaluated other options including SnapLogic and Talend. We found them to be more expensive, and while their workflows were similar, Pentaho Data Integration and Analytics met our business requirements more feasibly in terms of cost-effectiveness and user-friendliness for developers.

My advice for others considering Pentaho Data Integration and Analytics is to first assess the memory and cloud version options, followed by checking scheduling and automation capabilities. Governance and data security are vital, as is error handling; thus, it is essential to ensure proper debugging and retry mechanisms are in place, as well as exploring command line automation for transformations and job files.

Pentaho Data Integration and Analytics encourages enterprise code, minimizing errors and providing excellent reusability. It fosters collaboration across different environments and modules, covering governance and standardization effectively. For data security, it includes robust features for maintaining PII data confidentiality, and the maintenance aspect is simplified with scheduled logs that help identify and resolve errors more efficiently. I would rate this product an 8 out of 10.

Hybrid Cloud

Other

Since 2020, I have been working with Pentaho Data Integration and Analytics. My main use case for Pentaho Data Integration and Analytics is to create end-to-end ETL pipelines, taking data from various sources such as APIs, Elasticsearch, or databases and then performing ETL processing using Pentaho Data Integration, such as removing duplicates, and then sending it into our data database target or data production. I have two use cases applying Pentaho Data Integration and Analytics: first in Bank Mandiri and the second one with one of Indonesia's military services, where I created them from scratch using Pentaho Data Integration and Analytics.

The best features Pentaho Data Integration and Analytics offers are friendly user interfaces and ease of use, which makes it the best way for data integration.

When I say it is easy to use, I mean we can address where the error occurs without having to create flow testing. We directly test our production end-to-end ETL flow by simply disabling the last step, making it easy to track how data is running, how data counts are incoming to our data flow, and the error tracing as well.

I use scheduling in Pentaho Data Integration and Analytics, perhaps with Apache Airflow or an easy way with cron jobs.

Pentaho Data Integration and Analytics has positively impacted my organization, as approximately 90% of my job involves using it. I work on project dashboards with Tableau, but before entering Tableau, I must gather or collect data from various sources, both databases and outside databases such as APIs or Elasticsearch, making Pentaho Data Integration and Analytics the best way for me to gather that data.

I can share specific outcomes from using Pentaho Data Integration and Analytics, such as reducing errors because it is easy to find where the error occurs, and making handovers to other teams easy since it is user-friendly.

To improve Pentaho Data Integration and Analytics, I suggest developing capabilities for cloud-based solutions instead of being solely on-premises. As long as I have been using Pentaho Data Integration and Analytics, I think it is already great, but improvements could include adding features with cloud-based functionalities.

In my experience, Pentaho Data Integration and Analytics is very stable.

Regarding the scalability of Pentaho Data Integration and Analytics, in my previous projects, I handled daily data of around 1 million rows, and it runs well, though it does slow down a bit with larger workloads.

I have never used customer support for Pentaho Data Integration and Analytics; instead, I self-improve to find solutions.

Before using Pentaho Data Integration and Analytics, I did use a different solution. I switched from a cloud system, which is why I suggested the need for improvements toward a cloud-based system. The very first ETL tool that I ever used was SSIS, which is not much different from Pentaho Data Integration and Analytics, but for me, Pentaho Data Integration and Analytics is still the best ETL tool I have ever used.

Regarding Pentaho Data Integration and Analytics's AI capabilities, I am not familiar with this aspect since I have never touched or heard much about it, but I believe that combining AI features with Pentaho Data Integration and Analytics could be the best approach. Regarding Pentaho Data Integration and Analytics's AI capabilities and my thoughts on its output reliability, I am unclear as I have not had enough experience to answer it. For others looking into using Pentaho Data Integration and Analytics, my advice is to keep trying and exploring its capabilities. I gave this product a rating of 9 out of 10.

On-premises

Pentaho Data Integration and Analytics is my primary tool for ETL activities, where I extract data from different kinds of sources. We have two editions available: Community Edition and Enterprise Edition. Since Community Edition is open-source with limited features, we opted for the Enterprise Edition. I was able to perform transformations after extracting data from multiple heterogeneous sources, applying operations such as merge, SCDs, data cleansing, and data transformations according to client requirements, and finally loading the processed data to cloud solutions, cloud storage, or data warehouses in either SQL or NoSQL databases like MongoDB and Cassandra.

I used Pentaho Data Integration and Analytics for a specific project with a client called Walmart, a retail chain located in the US, Mexico, and Canada, where I aimed to build a centralized data warehouse for daily sales analytics. The data came from various sources including online sales, Excel sheets, REST API, MySQL DB, CSV, and flat files, requiring necessary transformations dictated by the transformation sheet provided by the client, which detailed how to handle the input source data, the required cleansing, and the transformations needed. This led to a recurring activity where I created pipelines with Pentaho Data Integration and Analytics, designing jobs and transformations, and eventually loading the data into Azure SQL DB, NoSQL databases, or Snowflake, while scheduling the ETL pipelines based on activity frequency—daily, weekly, or monthly.

After creating jobs and transformations for each activity with Pentaho Data Integration and Analytics, we scheduled these jobs, allowing the Business Intelligence team to review the loaded data in the data warehouses. We also used Pentaho Report Designer for conducting predictive analysis, such as forecasting sales over the next five years based on prior data, enabling us to conduct in-depth analysis for future trends.

Pentaho Data Integration and Analytics offers valuable features such as a drag-and-drop option, which eliminates the need for extensive coding, making it accessible even to data engineers with little experience in programming. The platform affords clear visual transformations and strong connectivity to multiple data sources, allowing connections with flat files, JSON files, Hadoop systems, HDFS, JDBC files, and others.

Pentaho Data Integration and Analytics has had a notably positive impact on my organization, enabling us to complete several projects efficiently. Having begun my journey with Pentaho Data Integration and Analytics in 2013 without prior experience, I found the interface quite simple, which allowed me to navigate through all components and create the necessary pipelines without significant difficulties, contributing positively to our project timelines.

Improvements for Pentaho Data Integration and Analytics could include enhancements to the UI and the addition of more complex features, including a wider range of transformations. I also lack the option to use programming languages beyond Python and SQL, and a provision to incorporate Scala code in the scripting component would be beneficial, along with a simpler interface overall.

I have been using Pentaho Data Integration and Analytics for more than five years, with my last use in 2023, and I am currently focused entirely on cloud solutions.

In my experience, Pentaho Data Integration and Analytics is stable, though it does experience performance issues when handling huge data volumes, such as three billion or four billion records, due to reliance on a flowchart-based mechanism instead of scripts, which can lead to longer execution times.

Pentaho Data Integration and Analytics' scalability is commendable, as it allows us to scale up according to our needs.

I do not have extensive knowledge about customer support, but besides troubleshooting, the enterprise support seems to include customer services, technical guidance, mentorship, and consulting as required.

Regarding support, while 24/7 assistance is available for the Enterprise Edition, the end-to-end support is not always optimal, as issue resolution can sometimes take three to four hours, and the lack of sufficient forums complicates troubleshooting.

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.

Regarding specific outcomes, while I initially encountered issues with the performance of the Community Edition, leading me to upgrade to the Enterprise Edition, I did notice a reduction in time spent on tasks and improvements in pipeline execution speeds with the new edition. I would not recommend the Community Edition for large data volumes, and to fully utilize Pentaho Data Integration and Analytics, the Enterprise Edition is necessary, requiring a subscription, which I consider a downside.

I would recommend Pentaho Data Integration and Analytics to others. My overall rating for this product is seven out of ten.

My main use case for Pentaho Data Integration and Analytics is creating ETLs to extract data from different sources, be it databases, APIs, or files and storing it into a certain database, be it a data warehouse or data lake.

One specific example of an ETL process I built with Pentaho Data Integration and Analytics is one that took data from a public API, normalized it with different parameters, and extracted the data so that it made sense from a business standpoint, then loaded it into our data warehouse in Snowflake.

Pentaho Data Integration and Analytics offers excellent ease of use and a quick learning curve, enabling me to build ETLs rapidly without having to navigate complicated processes to get the data and transform it, along with the impressive amount of native integrations instead of having to rely on scripts.

The native integrations I use most often in Pentaho Data Integration and Analytics are the ones for databases and APIs. Specifically, with APIs, I do not have to worry about headers as much and the formatting—I simply fill a few boxes before getting it ready. Additionally, the email feature that can be sent from Pentaho in case an ETL goes wrong or has any issues is valuable.

Pentaho Data Integration and Analytics positively impacted my organization by organizing data that was scattered across many different databases and APIs, reducing a lot of manual work and centralizing it into one data warehouse. It helped me grab data from different sources, thereby reducing manual labor and increasing automatic processes, allowing us to create many more reports and use the data efficiently instead of having it without utilizing it.

It reduced the manual labor significantly. I saw a reduction in hours spent getting data, specifically going from around three weeks of manual labor for each month for singular data that was collected daily to around two to three hours a week for a month, thanks to the checks that Pentaho Data Integration and Analytics has in place, transforming how we manage that data.

One thing I wish was better about Pentaho Data Integration and Analytics is its performance, as it sometimes gets intensive on the CPU standpoint, taking a lot of memory in some cases, especially when dealing with big data sets. Improving the performance would be beneficial.

One thing that could improve my experience with Pentaho Data Integration and Analytics is the ease of opening the application, as sometimes it can be a long process to open it, and the same applies when closing it.

I have been using Pentaho Data Integration and Analytics since I started my professional career in data engineering, so around three years ago, and three years in total.

Pentaho Data Integration and Analytics is stable. I do not have many issues with stability, other than sometimes running out of memory.

Pentaho Data Integration and Analytics is scalable. It can be scaled well until you reach a point where you need to perform a lot of operations, and the issue arises when it runs out of memory to handle some data, but aside from that, it scales pretty well with a lot of data.

This was the first solution that we used, as we brainstormed a few options and ended up deciding on Pentaho Data Integration and Analytics without making any changes afterward.

The setup cost was minimal, and the pricing experience was pretty good, as the free version is good enough to provide many features that a small to medium company needs.

Before choosing Pentaho Data Integration and Analytics, I evaluated some options such as AWS Glue and Apache Airflow, but I decided on Pentaho Data Integration and Analytics mainly due to the availability of the free edition.

A unique scenario where Pentaho Data Integration and Analytics helped me out was when starting with ETL tools. The ease of use that it has came in very handy, allowing for a transition from Python or other software and languages to an ETL tool that is just grab and paste—grabbing the little boxes—helping me understand how ETL tools work and start using that one for many cases.

My advice for others considering using Pentaho Data Integration and Analytics is to just try the community edition, which is free, and if you find it suitable, keep using it until you need the paid version.

I would rate this product a seven out of ten.

Private Cloud

Amazon Web Services (AWS)

My main use case for Pentaho Data Integration and Analytics is to build an ETL process.

The best features Pentaho Data Integration and Analytics offers are difficult for me to pinpoint because it is my first integration tool I have ever used, but Pentaho Data Integration and Analytics has many features that are sufficient for my needs.

Since Pentaho Data Integration and Analytics has many features and it is my first integration tool, the feature that stands out to me is the drag-and-drop interface, which I find very useful for a user that does not have any experience using SQL commands or writing code. Pentaho Data Integration and Analytics has a very friendly interface.

Pentaho Data Integration and Analytics has positively impacted my organization because, at this moment, I cannot measure it precisely, but since we started using Pentaho Data Integration and Analytics, many of our manual tasks have become automatic, and we have increased our time for productive things.

I can share that the kinds of tasks that were automated include the company's assurance and accounts department working with monthly reports. Since I have been working with Pentaho Data Integration and Analytics, I am starting to join all the information and increase our pipeline every day, so they do not need to spend all the time they spent before using Pentaho Data Integration and Analytics, resulting in approximately a 100% increase in their time.

Regarding the features, I find many issues using Pentaho Data Integration and Analytics when working with different environments, especially development environments and production environments, where I have faced many issues working with two environments.

Pentaho Data Integration and Analytics can be improved by working with different environments, specifically the possibility to change the variables, meaning I write my variables only once and can change them for different environments such as production or development.

About the needed improvements, I think that if Pentaho Data Integration and Analytics could bring more flexibility to work with Python scripts in the future, it would be beneficial.

I chose an eight because of those two things: working with different environments, the possibility to change the environments with variables, and the limitation to working with Python scripts.

I have been using Pentaho Data Integration and Analytics for about two years.

Pentaho Data Integration and Analytics is stable, and I find that version 3.1 is the most stable version I have ever used.

Regarding scalability, I have found different issues working with Pentaho Data Integration and Analytics as my data increased, mainly because we are limited by the default JVM. Even if I change the JVM, Pentaho Data Integration and Analytics does not work well when we have a lot of data transport in memory RAM.

Before using Pentaho Data Integration and Analytics, I previously used Apache Hop.

Before choosing Pentaho Data Integration and Analytics, I evaluated other options, including SQL Server Integration Services.

I switched from SQL Server Integration Services to Pentaho Data Integration and Analytics because we had difficulty connecting with open-source databases. We found Pentaho Data Integration and Analytics which works well with MySQL and PostgreSQL, and this is the main reason we changed to Pentaho Data Integration and Analytics.

I have seen a return on investment because we saved money and saved time.

My experience with pricing, setup cost, and licensing for Pentaho Data Integration and Analytics is that I am using the community edition.

My advice to others looking into using Pentaho Data Integration and Analytics is to go ahead and enjoy it because it is very simple to use.

I have additional thoughts about Pentaho Data Integration and Analytics, and I say keep doing what you are doing because you have a great product and help many people like me. I appreciate your work and will support and provide feedback about the pipeline that has been built. I rate Pentaho Data Integration and Analytics an eight overall.

On-premises

I use Pentaho Data Integration and Analytics to perform ETL processes to migrate data between different systems. For example, I need to move patient data from our system database to a sub database. I believe this is better for my organization, but I must complete this task because if I do not, my organization will not know the solution. For this reason, I complete the task with Pentaho Data Integration and Analytics because it is a comprehensive solution that allows me to accomplish large tasks in less time.

I use Pentaho Data Integration and Analytics because it is easier for me, and Pentaho Data Integration and Analytics offers many different ways to complete my work. It is easier because Pentaho Data Integration and Analytics has a drag-and-drop interface, and I do not need to write additional code to perform the process.

I frequently connect to systems and move data between them, and I work with relational databases. Pentaho Data Integration and Analytics drag-and-drop solutions are easier to use for this process because I do not have development knowledge. Pentaho Data Integration and Analytics has positively impacted my organization because it is easier to use, and my knowledge about this work facilitates the translation from the source to my final system.

Pentaho Data Integration and Analytics helps me complete tasks in API solutions, and I think that is more difficult for me because I do not work with APIs. However, I think these features are interesting for Pentaho Data Integration and Analytics.

I think Pentaho Data Integration and Analytics needs additional plugins for the market, and for some specific tasks it is very difficult. However, this is not a problem because I work with my colleagues in the development team, and if I need help to complete this type of work, my colleagues assist me.

The documentation in Pentaho Data Integration and Analytics is challenging to navigate to find different solutions from other ETL developers who are users of Pentaho Data Integration and Analytics.

I have been working with Pentaho Data Integration and Analytics for many years, with more intensive use in the last ten months.

Pentaho Data Integration and Analytics is stable in my experience.

I have worked with files of five gigabytes, for example, and I do not experience any problems completing tasks with this volume.

I do not know what your support department for Pentaho Data Integration and Analytics offers.

I have worked with Talend and Data Services from SAP, but I prefer Pentaho Data Integration and Analytics because I am comfortable with this solution. The variability of the tasks in Pentaho Data Integration and Analytics and the drag-and-drop feature make it easier for the user.

It is easier to start with Pentaho Data Integration and Analytics.

I do not evaluate other solutions when choosing Pentaho Data Integration and Analytics, but if I need to, I obviously want to work with open-source solutions.

For example, to complete different migrations in projects in the past, the time to finish this task is faster than before.

I use the community version of Pentaho Data Integration and Analytics, and I do not need additional costs.

I rate Pentaho Data Integration and Analytics a seven out of ten overall. I choose seven out of ten because in my daily work, the things that I need are in Pentaho Data Integration and Analytics, but there are small things I must do more or must review more carefully. Perhaps incorporating the Spanish language would facilitate the interview. Pentaho Data Integration and Analytics is a great solution to accomplish big things in a very short time. I gave this review a rating of seven.

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.