AbhishekKatara - PeerSpot reviewer
Technical Lead at Sopra Steria
Real User
Top 10
Easy-to-use tool with no coding required
Pros and Cons
  • "StreamSets’ data drift resilience has reduced the time it takes us to fix data drift breakages. For example, in our previous Hadoop scenario, when we were creating the Sqoop-based processes to move data from source to destinations, we were getting the job done. That took approximately an hour to an hour and a half when we did it with Hadoop. However, with the StreamSets, since it works on a data collector-based mechanism, it completes the same process in 15 minutes of time. Therefore, it has saved us around 45 minutes per data pipeline or table that we migrate. Thus, it reduced the data transfer, including the drift part, by 45 minutes."
  • "The logging mechanism could be improved. If I am working on a pipeline, then create a job out of it and it is running, it will generate constant logs. So, the logging mechanism could be simplified. Now, it is a bit difficult to understand and filter the logs. It takes some time."

What is our primary use case?

StreamSets is a wonderful data engineering, data ops tool where we can design and create data pipelines, loading on-prem data to the cloud. One of our major projects was to move data from on-premises to Azure and GCP Cloud. From there, once data is loaded, the data scientist and data analyst teams use that data to generate patterns and insights. 

For a US healthcare service provider company, we designed a StreamSets pipeline to connect to relational database sources. We did generate schema from the source data loaded into Azure Data Lake Storage (ADLS) or any cloud, like S3 or GCP. This was one of our batch use cases. 

With StreamSets, we have also tried to solve our real-time streaming use cases as well, where we were streaming data from source Kafka topic to Azure Event Hubs. This was a trigger-based streaming pipeline, which moved data when it appeared in a Kafka topic. Since this pipeline was a streaming pipeline, it was continuously streaming data from Kafka to Azure for further analysis.

How has it helped my organization?

We can securely fetch the passwords and credentials stored in Azure Key Vault. This is a fundamentally very strong feature that has improved our day-to-day life.

What is most valuable?

It is a pretty easy tool to use. There is no coding required. StreamSets provides us a canvas to design our pipeline. At the beginning of any project, it gives us a picture, which is an advantage. For example, if I want to do a data migration from on-premise to cloud, I will draw it for easier understanding based on my target system, and StreamSets does exactly the same thing by giving us a canvas where I can design our pipeline.

There are a wide range of available stages: various sources, relational sources, streaming sources. There are various processes like to transform the source data. It is not only to migrate data from source to destination, but we can utilize different processes to transform the data. When I was working on the healthcare project, there was personal identification information on the personal health information (PHI) data that we needed to mask. We can't simply move it from source to destination. Therefore, StreamSets provides masking of that sensitive data.

It provides us a facility to generate schema. There are different executors available, e.g., Pipeline Finisher executor, which helps us in finishing the pipeline. 

There are different destinations, such as S3, Azure Data Lake, Hive, and Kafka Hadoop-based systems. There are a wide range of available stages. It supports both batch and streaming. 

Scheduling is quite easy in StreamSets. From a security perspective, there is integration with keywords, e.g., for password fetching or secrets fetching. 

It is pretty easy to connect to Hadoop using StreamSets. Someone just needs to be aware about the configuration details, such as which Hadoop cluster to connect and what credentials will be available. For example, if I am trying with my generic user, how do I connect with the Hadoop distributed system? Once we have the details of our cluster and the credential, we can load data to the Hadoop standalone file system. In our use case, we collected data from our RDBMS sources using JDBC Query Consumer. We queried the data from the source table, captured that data, and then loaded the data into the destination Hadoop distributed file system. Thus, configuration details are required. Once we have the configuration details, i.e., the required credentials, we can connect with Hadoop and Hive. 

It takes care of data drift. There are certain data rules, matrix rules, or capabilities provided by StreamSets that we can set. So, if the source schema gets deviated somehow, StreamSets will automatically notify us or send alerts in automated fashion about what is going wrong. StreamSets also provides Change Data Capture (CDC). As soon as the source data is changed, it can capture that and update the details into the required destination. 

What needs improvement?

The logging mechanism could be improved. If I am working on a pipeline, then create a job out of it and it is running, it will generate constant logs. So, the logging mechanism could be simplified. Now, it is a bit difficult to understand and filter the logs. It takes some time. For example, if I am starting with StreamSets, everything is fine. However, if I want to dig into problems that my pipeline ran into, it initially takes some time to get familiar with it and understand it.

I feel the visualization part can be simplified or enhanced a bit, so I can easily see what happened with my job seven days earlier and how many records it transmitted. 

Buyer's Guide
StreamSets
March 2024
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For how long have I used the solution?

I have been using StreamSets for close to four and a half years when creating my data pipelines in our projects.

What do I think about the stability of the solution?

Stability-wise, it is wonderful and quite good. Mostly, since the solution is completely cloud-based in our project, we just need to hit a URL and then we are logged into StreamSets with our credentials. Everything is present there. Other than some rare occasions, StreamSets behaves pretty well. 

There were certain memory leak issues for a few stages, like Azure Data Lake, but those were corrected with immediate solutions, like patches and version upgrades. 

Stability-wise, I would rate it as eight and a half or nine out of 10.

What do I think about the scalability of the solution?

I would like auto scaling for heavy load transfer. This applied particularly when we were our data migration project. The tables had more than 10 millions of records in them. When we utilized StreamSets, it took a huge amount of time. Though we were doing every schema generation, we were using ADLS as a destination, and it hung for a good amount of time. So, we considered PySpark processes for our tables, which have greater than 10 millions of records. Usually, it works pretty well with the source tables and the data size is close to five to six million records, but when it is closer to 10 million, I personally feel the auto scaling feature could be improved.

How are customer service and support?

We have spent a good amount of time dealing with their technical support team. The first step is to check the documentation, then work with them. 

I had a chance to work with StreamSets during our use case. They helped us out in a good manner with a memory leak issue that we were facing in our production pipeline. So, there was one issue where our pipelines were running fine in dev and the lower environment, i.e., dev and QA, but when we moved those pipelines into production, we were getting a memory leak issue where the JVM ran out of memory exception. 

We tried reducing the number of threads and the batch size for the small table, but it was still creating issues. Then, we connected with StreamSets' support team. They gave us a customized patch, which our platform team installed in our production environment. With some collaborative effort of around a week, we were finally able to run our pipeline pretty well.

I would rate the customer support and the technical support as quite good and knowledgeable (eight out of 10). They helped with issues that were occurring in our work. They accepted that there were some issues with the version, which StreamSets released and we were using. They accepted that the version particularly had some issues with the memory management. Therefore, the immediate solution that they provided was a patch, which our platform team installed. However, the long-term solution was to update or upgrade our StreamSets Data Collector platform from version 3.11 to 4.2, and that solved our problem.

How would you rate customer service and support?

Positive

Which solution did I use previously and why did I switch?

We were using Cloudera distribution. All our projects were running, utilizing Hadoop, and the distribution was Cloudera Hortonworks. We were utilizing Sqoop and Hive as well as PySpark or Scala-based processes to code. However, StreamSets helped us a lot in designing our data pipeline quickly in a very fast way.

It has made our job pretty easy in terms of designing, managing, and running our data engineering pipeline. Previously, if I needed to transfer data from source to destination, I would need to use Sqoop, which is a Hadoop stack technology used to establish connectivity with the RDBMS, then load it to the Hadoop distributed file system. With Sqoop, I needed to have my coding skills ready. I needed to be very precise about the connection details and syntax. I needed to be very aware of them. StreamSets solved this problem. 

Its greatest feature is that it provides an easy way to design your pipeline. I just need to drag and drop source JDBC Query Consumer to my canvas as well as drag and drop my destination to the canvas. I then need to connect both these stages and be ready with my configuration details. As soon as I am done with that, I will validate the pipeline. I can create a job out of it and schedule it, even the monitoring. All these things can be achieved by a single control panel. So, it not only solves the developer's basic problems, but it also has greatly improved the experience.

We were previously completely using the Hadoop technology stack. Slowly, we started converting our processes into data engineering pipelines, which are designed into StreamSets. Earlier, the problem area was to write code into Sqoop or create Sqoop scripts to capture data from source, then put it into HDFS. Once data was in HDFS, we would write another PySpark process, which did the optimization and faster loading of the data, which is in Hadoop Distributed File System to a cloud-based storage data lake, like ADLS or S3. However, when StreamSets came into picture, we didn't need an intermediary, three-storage distributed file system like HDFS. We could simply create a pipeline that connects to RDBMS and load data directly to the cloud-based Azure Data Lake. So there is no requirement for an intermediary Hadoop Distributed File System (HDFS), which saves us a great amount of time and also helps us a lot in creating our data engineering pipelines.

Microsoft provided Change Data Capture tools, which one of our team members was using. Performance-wise, I personally feel StreamSets is way faster. A few of the support team members were using Informatica as well, but it does not provide powerful features that can handle big amounts of data.

How was the initial setup?

For our deployment model, we were following three environments: dev, QA and prod. Our team's main responsibility is to hydrate Azure Data Lake and GCP from the source system. Control Hub is hosted on GCP, and we were hitting the URL to log into StreamSets. All the data collector machines are created on Google Cloud Platform, and we use a dev environment. Whenever we create and do a PoC, we work in a dev environment. Once our pipeline and jobs are working fine, we move our pipelines to our QA environment, which is export and import. It is pretty easy to do via StreamSets Control Hub. We can simply select a job and export it, then log back into the QA environment and import the job. Once we import the job, the associated pipeline, and all the parameters, we have an option to import the whole bundle, like the pipeline, parameter, and instances. We can import everything. Once this is also working fine, we have another final environment, which is the production which is based on the source refresh frequencies. 

What about the implementation team?

In our company, we have a good data engineering team. We have a separate administrator team who is mainly responsible for deploying it on cloud, providing us libraries whenever required. There is a separate team who is taking care of all the installations and platform-related activities. We are primarily data engineers who utilize the product for solutions.

What was our ROI?

StreamSets’ data drift resilience has reduced the time it takes us to fix data drift breakages. For example, in our previous Hadoop scenario, when we were creating the Sqoop-based processes to move data from source to destinations, we were getting the job done. That took approximately an hour to an hour and a half when we did it with Hadoop. However, with the StreamSets, since it works on a data collector-based mechanism, it completes the same process in 15 minutes of time. Therefore, it has saved us around 45 minutes per data pipeline or table that we migrate. Thus, it reduced the data transfer, including the drift part, by 45 minutes.

What's my experience with pricing, setup cost, and licensing?

StreamSets Data Collector is open source. One can utilize the StreamSets Data Collector, but the Control Hub is the main repository where all the jobs are present. Everything happens in Control Hub. 

What other advice do I have?

For people who are starting out, the simple advice is to first try out the cloud login of StreamSets. It is freely available for everyone these days. StreamSets has released its online practice platform to design and create pipelines. Someone simply needs to go to cloud.login.streamsets.com, which is StreamSets official website. It is there that people who are starting out can log into StreamSets cloud and spin up their StreamSets Data Collector machines. Then, they can choose their execution mode. It is all in a Docker-containerized fashion. You don't need to do anything. 

You simply need to have your laptop ready and step-by-step instructions are given. You just simply spin up your Data Collector, the execution mode, and then you are ready with the canvas. You can design your pipeline, practice, and test there. So, if you want to evaluate StreamSets in basic mode, you can take a look online. This is the easiest way to evaluate StreamSets.

It is a drag-and-drop, UI-based approach with a canvas, where you design the pipeline. It is pretty easy to follow. So, once your team feels confident, then they can purchase the StreamSets add-ons, which will provide them end-to-end solutions and vendor support. The best way is to log into their cloud practice platform and create some pipelines.

In my current project, there is a requirement to integrate with Snowflake, but I don't have Snowflake experience. I have not integrated Snowflake with StreamSets yet.

I personally love working on StreamSets. It is part of my day-to-day activities. I do a lot of work on StreamSets, so I would rate them pretty well as nine out of 10.

Disclosure: PeerSpot contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
PeerSpot user
Avinash Mukesh - PeerSpot reviewer
IT Specialists at Soft Hostings
Real User
Top 5Leaderboard
User-friendly interface and easy integration, but needs easier transformation logic and faster support
Pros and Cons
  • "It's very easy to integrate. It integrates with Snowflake, AWS, Google Cloud, and Azure. It's very helpful for DevOps, DataOps, and data engineering because it provides a comprehensive solution, and it's not complicated."
  • "The data collector in StreamSets has to be designed properly. For example, a simple database configuration with MySQL DB requires the MySQL Connector to be installed."

What is our primary use case?

We are sharing data between platforms. It's helping me to be independent of the ETL tools as well as have the data format without using any programming language.

How has it helped my organization?

It's helping us to be more organized. It's a tool that helps a lot in easily extracting data sets from CRM tools, and it can be integrated with external sources to make sure that you are having a good platform. It has improved our organization in the way we perform tests and the way we perform data transfers and streaming.

The data collection process is straightforward and easy. It allows us to move data into modern analytics platforms.

It allows us to build data pipelines without knowing how to code. It allows developers to make sure they are getting the correct data. It works for departments that can code and that can't code. It's a universal tool.

It's very effective. It gives you a clear understanding of the architecture of the data that you have in your company.

StreamSets’ data drift resilience saved us a lot of time. If we were taking seven days previously to build something, now it takes us three days. It has saved about 30% of the time.

It has helped to break down data silos within the organization. It helps to make sure that we are on time with data analysis. It brings efficiency. Overall, it has saved us about 25% of the time.

StreamSets’ reusable assets have helped to reduce workload. There is about a 25% workload reduction.

StreamSets saves us money by not having to hire people with specialized skills. It's saving us 300 USD every month.

StreamSets has helped to scale our data operations. In our business, we process the data the whole time, and we share it with the analytics team to identify and understand what needs to be fixed and what needs to be improved. It's good for our organization.

What is most valuable?

Its user interface is friendly. It's straightforward to implement batch, streaming, or ETL pipelines.

It's very easy to integrate. It integrates with Snowflake, AWS, Google Cloud, and Azure. It's very helpful for DevOps, DataOps, and data engineering because it provides a comprehensive solution, and it's not complicated.

What needs improvement?

When using Transformer for Snowflake, it's a bit complex to understand the transformation logic. You need someone who has some technical skills to handle it. You need to have some skills to transform the data. However, it's important that Transformer for Snowflake is a serverless engine embedded within the platform, so there is no need for maintenance. Having a serverless engine makes it easy for any enterprise to not think about or worry about the cost of maintaining the software.

The data collector in StreamSets has to be designed properly. For example, a simple database configuration with MySQL DB requires the MySQL Connector to be installed.

For how long have I used the solution?

I've been using StreamSets for three years.

What do I think about the stability of the solution?

It's very stable. It's very hard to find any downtime for the software.

What do I think about the scalability of the solution?

It's scalable enough. It integrates with AWS, Snowflake, Google Cloud, and Azure. It gives you a very good way to process and store your data.

We're using it in multiple departments in the same location. It's being used by the analytics team and our senior developers. There are about 10 people using this solution.

How are customer service and support?

They take a long time to respond to queries, but they are good people. They should improve the time to respond to queries. I'd rate them a six out of ten.

How would you rate customer service and support?

Neutral

Which solution did I use previously and why did I switch?

I didn't use any other solution previously.

How was the initial setup?

Deploying StreamSets is not so complex. It's easy. It takes about three days.

It doesn't require any maintenance from our side.

What about the implementation team?

We have an in-house team of five people.

What was our ROI?

We have seen an ROI. We use data analytics in marketing and knowing where we need to market and where we need to improve, increases our success rate. We have seen about 30% ROI.

What's my experience with pricing, setup cost, and licensing?

It's not expensive because you pay per month, and the tasks you can perform with it are huge. It's reliable and cost-effective.

What other advice do I have?

It's a very good tool if you need to access data from a CRM system, Salesforce, etc. However, it can't be used as an end-to-end integration tool because it lacks certain functionality. It could also be very expensive for small enterprises. 

Overall, I'd rate it a seven out of ten.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Amazon Web Services (AWS)
Disclosure: PeerSpot contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
PeerSpot user
Buyer's Guide
StreamSets
March 2024
Learn what your peers think about StreamSets. Get advice and tips from experienced pros sharing their opinions. Updated: March 2024.
765,234 professionals have used our research since 2012.
Software Engineer at ZIDIYO
Real User
Enables us to create streams and pipelines that our analytics team can utilize to identify areas for improvement
Pros and Cons
  • "The UI is user-friendly, it doesn't require any technical know-how and we can navigate to social media or use it more easily."
  • "Using ETL pipelines is a bit complicated and requires some technical aid."

What is our primary use case?

We use StreamSets to create data pipelines and to make sure that we know the exact analytics of our data usage within our company.

How has it helped my organization?

We use StreamSets' ability to connect to enterprise data stores such as Kafka. It is easy and simple to connect enterprise data stores as long as we follow the documentation.

We use StreamSets' ability to move data into the analytic platforms easily because we can use the template provided to extract data from the pipeline.

Being able to use Transformer for Snowflake to design both simple and complex transformation logic is important because it helps us break out a live amount of data interfaces that can be understood by the analytics team and identify areas of improvement. As the Transformer for Snowflake operates as a serverless engine, we can reduce our costs as we no longer need to purchase servers.

StreamSets enables us to create streams and pipelines that our analytics team can utilize to identify areas for improvement. Additionally, our marketing team can leverage the data generated from these reports to understand how we can integrate our products and services to benefit our brand.

StreamSets' data drift resilience is effective and user-friendly. We can use templates or use them from scratch. Data drift resilience saves us around 35 percent of the time fixing duplicates.

StreamSets has helped us break down data silos within our organization by providing a clear path forward and enhancing our productivity by breaking down a large amount of data that we can understand.

StreamSets saved us around 40 percent of our time.

We can use a small team using StreamSets to create data pipelines that would normally require an expert that costs around $500 per month.

StreamSets helps us scale our operations because we understand the quality of the data we have and how we can integrate the data into our marketing needs.

What is most valuable?

The UI is user-friendly, it doesn't require any technical know-how and we can navigate to social media or use it more easily.

What needs improvement?

Using ETL pipelines is a bit complicated and requires some technical aid.

The Transformer for Snowflake functionality is complex and requires a lot of logic.

For how long have I used the solution?

I have been using the solution for three years.

What do I think about the stability of the solution?

The solution is stable with no issues.

What do I think about the scalability of the solution?

The solution is scalable.

How are customer service and support?

The technical support team takes over eight hours to respond to our requests.

How would you rate customer service and support?

Neutral

How was the initial setup?

The initial setup is straightforward. I deployed the solution myself.

What about the implementation team?

The implementation was completed in-house.

What was our ROI?

StreamSets helps us increase our sales by 45 percent.

What's my experience with pricing, setup cost, and licensing?

StreamSets is expensive, especially for small businesses.

What other advice do I have?

I give the solution a nine out of ten.

The solution does not require maintenance from our end.

We have deployed StreamSets across our engineering team, data analytics team, and software development team.

StreamSets is an excellent solution for organizations that have a budget. The solution allows for various streaming capabilities and seamless integration with customer messaging, all within one environment. I highly recommend StreamSets.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Amazon Web Services (AWS)
Disclosure: PeerSpot contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
PeerSpot user
Kevin Kathiem Mutunga - PeerSpot reviewer
Chief software engineer at Appnomu Business Services
Real User
Top 10
Enables us to build data pipelines without knowing how to code and helped us break down data silos within our organization
Pros and Cons
  • "The best feature that I really like is the integration."
  • "Visualization and monitoring need to be improved and refined."

What is our primary use case?

In our department, we use StreamSets to design data pipelines that load all data from various RD and VMS sources to the cloud, such as Azure. We also use the data set for data analysts to generate panels for our organization, as well as for real-time use cases for monitoring and consuming other streaming data. Additionally, we are able to customize StreamSets to suit our needs and budget.

How has it helped my organization?

Using StreamSets to create pipelines for batch streaming or ETL is easy and straightforward. However, if one is new to StreamSets, it may not be so simple and may require a lot of documentation for assistance.

We utilize StreamSets' ability to connect to enterprise data stores, making it easy to begin trading instantly without needing to be technically skilled. We use StreamSets to move data into analytics platforms. In my experience, it is initially quite easy to move data back if we have a clear understanding of data transit, importation, and exporting from external sources.

This solution enables us to build data pipelines without knowing how to code. The solution includes templates that guide us and help us customize our data easily. It is essential that StreamSets does not necessitate coding, as this saves a considerable amount of time that would otherwise be spent writing code, as well as resources that would be required to hire experts.

Transformer for Snowflake can help with both simple and complex transformation logic. For example, creating a plan to perform EPL and machine learning operations is easy and fast. However, if the same operations are performed on-site, it can be difficult to troubleshoot events due to limited visibility into the results. StreamSets' Transformer for Snowflake is important to us because it saves us a lot of time and enables us to complete a task remotely with only two or three people.

It is important that Transformer for Snowflake is a serverless engine embedded within the platform. We have the capability of creating a data operations platform, so we don't have to worry or even be aware of what we are doing at the moment. We can simply create a device and use it in the pipeline we want it to be in.

The solution improved the way we work, benefiting both our customers and our development and retainer teams. StreamSets helps us develop a platform manually, with a lot of teamwork, either remotely or on-site, depending on which option we use. This has had a significant impact on our organization in terms of how we process and transform data.

I would say that it is very easy for us to update the template so that we can have real, actual data in APL claims and in the supply chain. StreamSets' data drift resilience is very effective and can run in the data grid. The data drift resilience has reduced the time it takes us to fix data drift breakages by approximately 25 percent.

StreamSets helped us break down data silos within our organization. The ability to break down data silos helps StreamSets to gain quick insights. In general, it is a great feature that ensures we have activities or processes in place. We know precisely what to prevent and what to implement.

StreamSets saved us around 30 percent of our time, meaning that a task that would take five hours to complete manually can now be done in around three and a half hours.

The reusable assets are reducing workload by 35 percent by allowing different people to use a single platform or resource, regardless of whether they have a similar SKU or a different SKU. This feature can help an organization simplify, implement, and transmit more easily.

It is not only the cost of one packet that we paid for, but now we are implementing a strategy using different people within the company. It would be very expensive if we had to hire a new person to manage that task and it would also take a lot of time. StreamSets is not only saving us money, but it is also ensuring that we complete strategies on time.

StreamSets as well helped us scale our operations, which has had a significant impact on our business. We now have a better understanding of how to secure data and provide reliable security for the transmission of data from internal servers to external services, as well as meeting our client's application needs.

What is most valuable?

The best feature that I really like is the integration. The software can be integrated with Azure Keyvault or AWS Secrets Manager, as well as scheduling. It is very easy to schedule an event, which is much easier than I expected through StreamSets. The solution is also fast at determining pipelines. Additionally, I like that StreamSets has many components, such as sources, processes, execution, and other useful elements that I need to plan.

What needs improvement?

There should be a concept of creating double variables because it's still missing.

The loading machine mechanism needs to be simplified. Currently, it takes some time to get familiar with and understand that. 

Visualization and monitoring need to be improved and refined. For example, it is difficult to monitor a job to see what happened in the past seven days when a transfer occurred.

The licensing model also has room for improvement. The solution is currently expensive.

For how long have I used the solution?

I have been using the solution for five years.

What do I think about the stability of the solution?

The solution is stable.

What do I think about the scalability of the solution?

The solution is scalable. We currently have four people using StreamSets in our organization.

How are customer service and support?

The technical support is good and they prioritize issues based on their severity, so sometimes we have to wait a while for a response.

How would you rate customer service and support?

Neutral

How was the initial setup?

The initial setup is a bit complex for first-time people. There is a lot of documentation that needs to be reviewed before deploying. The deployment takes around one month.

What about the implementation team?

The implementation is completed in-house.

What was our ROI?

StreamSets simplified our data ingestion and integration process without the need for the large financial investment that would be required if we were to use other, cheaper solutions. This is due to StreamSets' security and safety in supporting various heterogeneous sources such as RDZMS, and Salesforce. StreamSets ensures that we have a secure and easy way to launch any integration tool, resulting in increased profits. StreamSets is very stable, secure, and compliant, and has yielded a return on investment of around 30 percent.

What's my experience with pricing, setup cost, and licensing?

I believe the pricing is not equitable. Different businesses operate in various models and ways, so I wish StreamSets would be able to adjust their pricing depending on the intended use of the software. This would be beneficial to businesses with limited budgets. Currently, the cost of StreamSets is the same regardless of the amount of backup, which is costly.

What other advice do I have?

I give the solution an eight out of ten. StreamSets still needs to improve the monitoring and visualization before the solution can be a ten out of ten.

Since StreamSets is deployed in the cloud, we don't have any maintenance requirements or costs.

I highly recommend StreamSets; it is an excellent tool with both batch and streaming capabilities. StreamSets is a great option for anyone to try, though it does require an organization to have the budget to use it.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Other
Disclosure: PeerSpot contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
PeerSpot user
Sumesh Gansar - PeerSpot reviewer
Product Marketing Manager at a tech vendor with 10,001+ employees
Real User
Top 5Leaderboard
We are now able to run pipelines that scale horizontally, improving efficiency and significantly reducing workload
Pros and Cons
  • "For me, the most valuable features in StreamSets have to be the Data Collector and Control Hub, but especially the Data Collector. That feature is very elegant and seamlessly works with numerous source systems."
  • "Also, the intuitive canvas for designing all the streams in the pipeline, along with the simplicity of the entire product are very big pluses for me. The software is very simple and straightforward. That is something that is needed right now."
  • "In terms of the product, I don't think there is any room for improvement because it is very good. One small area of improvement that is very much needed is on the knowledge base side. Sometimes, it is not very clear how to set up a certain process or a certain node for a person who's using the platform for the first time."

What is our primary use case?

My primary use case with StreamSets is to integrate large data sets from multiple sources into a destination. We also use it as a platform to ingest data and deliver data for database analytics.

How has it helped my organization?

One major benefit that we have realized with StreamSets is that we are now able to run pipelines that scale horizontally, instead of using a static service to host the service. This has improved efficiency and reduced our workload by around 85 percent. Initially, we started out with around 40 users. Now, there are 100 users. We have definitely scaled up, in terms of usage, with StreamSets.

The fact that it is a single centralized platform saves us a lot of time. It's very intuitive and very effective, saving us a lot of resources with its built-in capabilities. No manual intervention is needed, and nobody needs to oversee it. It's an "all-in-one" deal for us. We are able to save 15 to 18 hours per week. Tasks that required three people can be done with StreamSets itself.

And with its ability to integrate large data sets, we are now able to pull thousands of records instantly, thereby reducing the need to do some complex coding for this asset. That has also been a very big plus for us.

We also use it to connect our Apache Kafka with data lakes and, as a result, this connection has gotten much more efficient and quicker for us. The overall efficiency has also drastically improved for us with this. Connecting these enterprise systems using StreamSets is pretty easy. The StreamSets platform is very straightforward. There is no major coding required, so any non-technical person can also do it.

Without the need for any complex coding at all, we are able to pull records. The records are vast and very large and pulling them usually requires coding, but the fact that there is literally no coding required is a very big plus for us. Once you start to code, there is a lot of time involved and a lot of QA involved, but all of that is eliminated here.

And it has definitely helped us break down data silos. With our large amount of data, we have different data formats, and as a result, there are data silos that are present by default. With StreamSets, we were able to completely eliminate that because StreamSets has become a centralized system for us to accommodate everything. We have been able to get a single, centralized view of all our data.

We have a lot of different data formats, and transforming them manually without any tool or system is a cumbersome and frustrating process. We use StreamSets to do that. It has made that process much more elegant and efficient for us.

What is most valuable?

For me, the most valuable features in StreamSets have to be the Data Collector and Control Hub, but especially the Data Collector. That feature is very elegant and seamlessly works with numerous source systems. 

Also, the intuitive canvas for designing all the streams in the pipeline, along with the simplicity of the entire product are very big pluses for me. The software is very simple and straightforward. That is something that is needed right now. 

Apart from that, the user interface of StreamSets is very good. It's very user-friendly and very appealing. Moving data into modern analytics platforms is a very straightforward procedure. There is no difficulty involved in it.

In addition, the ETL capabilities of StreamSets are also very useful for us. We are able to extract and transform data from multiple data sources into a single, consistent data store that is loaded into our target system.

What needs improvement?

In terms of the product, I don't think there is any room for improvement because it is very good. One small area of improvement that is very much needed is on the knowledge base side. Sometimes, it is not very clear how to set up a certain process or a certain node for a person who's using the platform for the first time.

Some visual explanation or some visually appealing knowledge-based content would be very good. That is something that I could have done with, once I started using it, because I found it very difficult.

For how long have I used the solution?

I have been using StreamSets for about a year.

What do I think about the stability of the solution?

It is definitely a stable product. In fact, it is one of the top products in the market in that particular category. We have not faced any stability issues so far, in terms of server speed, latency, or deployment.

What do I think about the scalability of the solution?

It's a scalable product. In our company, the platform is used across seven teams in our organization.

A couple of more teams are evaluating StreamSets in our organization. They're running things and asking for some feedback from our side as well. There are plans to expand our use of it.

How are customer service and support?

I have been in contact with their technical support and I would rate them very highly. They're very knowledgeable and patient. That is something that I like very much. For a very new user, it's not very easy to understand and we contact the support team over email.

We do have a relationship manager as well, who acts as the central point of contact for us. They're very prompt, knowledgeable, and friendly.

How would you rate customer service and support?

Positive

Which solution did I use previously and why did I switch?

This was one of the first products we used.

What was our ROI?

Within about three months we were able to see benefits from the system. We saw a lot of time being saved, and about a 30 percent increase in our overall efficiency.

Apart from reducing our workload and improving our efficiency, we saw a 12 percent increase in our revenue last year after we implemented StreamSets. I know people will definitely see a return investment on their money from it.

What's my experience with pricing, setup cost, and licensing?

From what I hear from my team, I believe it's moderately priced because they're happy with the pricing.

What other advice do I have?

Server update maintenance is required, but that is minimal. Any product would require that type of maintenance. I don't think we are investing a lot of time and money in maintenance. The maintenance is just another cost for us. We have only two guys working on the maintenance part of the software.

It's a very intuitive product, modern, and very user-friendly in terms of the UI. Almost all our requirements have been met by StreamSets and we don't have any complaints so far.

I would recommend starting to use it as soon as possible. No tool is perfect. You have to choose the best of the lot. I certainly believe StreamSets is at the top of the ladder when it comes to similar software.

My biggest lesson from using StreamSets is that data integration can be done much more easily now. I only knew that after starting to use StreamSets. When it comes to data integration from multiple sources, and having multiple destinations, people always assume it's a time-consuming, cumbersome project. But once we started using StreamSets, all those assumptions were broken. It's very straightforward and elegant software.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Amazon Web Services (AWS)
Disclosure: PeerSpot contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
PeerSpot user
Senior Data Engineer at a energy/utilities company with 1,001-5,000 employees
Real User
Top 20
Quite simple to use for anybody who has an ETL or BI background
Pros and Cons
  • "StreamSets data drift feature gives us an alert upfront so we know that the data can be ingested. Whatever the schema or data type changes, it lands automatically into the data lake without any intervention from us, but then that information is crucial to fix for downstream pipelines, which process the data into models, like Tableau and Power BI models. This is actually very useful for us. We are already seeing benefits. Our pipelines used to break when there were data drift changes, then we needed to spend about a week fixing it. Right now, we are saving one to two weeks. Though, it depends on the complexity of the pipeline, we are definitely seeing a lot of time being saved."
  • "Currently, we can only use the query to read data from SAP HANA. What we would like to see, as soon as possible, is the ability to read from multiple tables from SAP HANA. That would be a really good thing that we could use immediately. For example, if you have 100 tables in SQL Server or Oracle, then you could just point it to the schema or the 100 tables and ingestion information. However, you can't do that in SAP HANA since StreamSets currently is lacking in this. They do not have a multi-table feature for SAP HANA. Therefore, a multi-table origin for SAP HANA would be helpful."

What is our primary use case?

We are using the StreamSets DataOps platform to ingest data to a data lake.

How has it helped my organization?

Our time to value has increased because our development time has been considerably reduced. The major benefit that we are getting out of the solution is the ability to easily transform and upskill a person who has already worked on an ETL or BI background. We don't need to specifically look for people who know programming or worked on Python, DataOps, or a DevOps sort of functionality. In the market, it is easier to find people with ETL or BI skills than people with hardcore DevOps or programming skills. That is the major benefit that we are getting out of moving to a GUI-based tool like StreamSets. How quickly we are delivering to our customers, as well as our ability to ingest to a data lake, have actually improved a lot by using this tool.

What is most valuable?

The types of the source systems that it can work with are quite varied. There are numerous source systems that it can work with, e.g., a SQL Server database, an Oracle Database, or REST API. That is an advantage we are getting. 

The most important feature is the Control Hub that comes with the DataOps Platform and does load balancing. So, we do not worry about the infrastructure. That is a highlight of the DataOps platform: Control Hub manages the data load to various engines.

It is quite simple for anybody who has an ETL or BI background and worked on any ETL technologies, e.g., IBM DataStage, SAP BODS, Talend, or CloverETL. In terms of experience, the UI and concepts are very similar to how you develop your extraction pipeline. Therefore, it is very simple for anybody who has already worked on an ETL tool set, either for your data ingestion, ETL pipeline, or data lake requirements.

We use StreamSets to load into AWS S3 and Snowflake databases, which are then moved forward by Power BI or Tableau. It is quite simple to move data into these platforms using StreamSets. There are a lot of tools and destination stages within StreamSets and Snowflake, Amazon S3, any database, or an HTTP endpoint. It is just a drag-and-drop feature that is saving a lot of time when rewriting any custom code in Python. StreamSets enables us to build data pipelines without knowing how to code, which is a big advantage.

The data resilience feature is good enough for our ETL operations, even for our production pipelines at this stage. Therefore, we do not need to build our own custom framework for it since what is available out-of-the-box is good enough for a production pipeline.

StreamSets data drift feature gives us an alert upfront so we know that the data can be ingested. Whatever the schema or data type changes, it lands automatically into the data lake without any intervention from us, but then that information is crucial to fix for downstream pipelines, which process the data into models, like Tableau and Power BI models. This is actually very useful for us. We are already seeing benefits. Our pipelines used to break when there were data drift changes, then we needed to spend about a week fixing it. Right now, we are saving one to two weeks. Though, it depends on the complexity of the pipeline, we are definitely seeing a lot of time being saved.

What needs improvement?

One room for improvement is probably the GUI. It is pretty basic and a lot of improvement is required there. 

In terms of security, from an architecture perspective, when we want to implement something, and because our organization is very strict when it comes to cybersecurity, we have been struggling a bit because the platform has a few gaps. Those gaps are really gaps based on our organization's requirements. These are not gaps on StreamSets' side. The solution could improve a lot in terms of having more features added to the security model, which would help us.

There are quite a few features that we wanted. One is SAP HANA. Currently, we can only use the query to read data from SAP HANA. What we would like to see, as soon as possible, is the ability to read from multiple tables from SAP HANA. That would be a really good thing that we could use immediately. For example, if you have 100 tables in SQL Server or Oracle, then you could just point it to the schema or the 100 tables and ingestion information. However, you can't do that in SAP HANA since StreamSets currently is lacking in this. They do not have a multi-table feature for SAP HANA. Therefore, a multi-table origin for SAP HANA would be helpful.

For how long have I used the solution?

I have been using it for the past 12 months.

What do I think about the stability of the solution?

I have no concerns in terms of the application's core stability. We haven't had any major outages as such, and even if we had one, those were internal and related to our network, proxy, or firewall. As someone who implemented it and has been working on it day in, day out, sometimes 24/7, I am quite confident with the stability of the solution.

As with any application, it requires periodical maintenance, at least to do an upgrade. That maintenance is to simply upgrade the product, and nothing more than that.

What do I think about the scalability of the solution?

A core feature of the DataOps Platform is you can easily scale through engines when you have more pipelines running and data to process. So, if you would need to purchase more engines or cores, it is quite scalable. That is a major advantage that we are getting. 

In the Control Hub Platform, the orchestration and load balancing are quite scalable. You don't need to fiddle with the existing solution. Everything is run on another engine that gets hooked up automatically to Control Hub, which makes it seamless.

There is sort of a developed template out of StreamSets, where you just have one template and can point it to any source system. You can just start ingesting, which has reduced a lot of time in building our new pipelines.

How are customer service and support?

They are quite good and responsive. We have a dedicated support portal for StreamSets. We have authorized members who can raise support tickets using the portal, including myself. They have a quick turnaround with good responses, so we are quite happy as of now. I would rate the technical support between 7.5 and 8 out of 10.

How would you rate customer service and support?

Positive

Which solution did I use previously and why did I switch?

We previously developed our own custom platform. We switched because maintaining a custom platform is difficult. We are not a product team. We are an energy company who services business customers. Therefore, maintaining a custom platform is difficult. Another thing was that the custom platform was written programmatically. So, you need a lot of people who have a programmatic knowledge, both to maintain and use it.

The time to value is quite a critical KPI. Before, when our business needed data quickly on the platform, our previous solutions struggled to get it. Thus, our time to value has improved a lot and our customers are happy because they are able to get the data quickly.

How was the initial setup?

I was there right from the start when they adopted an open-source version. Late last year, we moved to an enterprise version, i.e., the DataOps platform. So, I worked on the 3.2.2 version, and now I am working on the 5.0 version, which is the enterprise license version.

The implementation is straightforward, except for a few hiccups with known network, process, and firewall issues. Other than that, it was a very simple, lean implementation.

Because we had a lot of firewall issues and issues with our optimization, it took probably four weeks for us to get things running. However, if you exclude the issues, it took probably a week to a week and a half to get things up and running.

We are working, as a separate piece of the project, to migrate whatever is running in our existing custom platform to StreamSets. From a certain date, we started to work purely on StreamSets. For any future ingestion requirements, we are using StreamSets DataOps platform. However, the previous platform is inactive at the moment. We are only using it for existing pipelines, and the plan is to migrate them to the DataOps platform this year very soon.

What about the implementation team?

Two people were needed for the deployment of this solution: a cloud engineer and a senior data engineer.

What was our ROI?

First, it has saved us a lot of time because we do not need to come up with our own custom platform, which is a huge expenditure in building and maintaining the custom platform. Second, even if we go for other products in the market, there are lots of gaps with the other products. Even if we picked up another product, we would have to customize it. An off-the-shelf product is not enough to meet our needs. Therefore, StreamSets has definitely helped us in getting the information into our data lake very quickly, in terms of ingestion.

The most important thing is it has helped us from a resourcing point of view. You can easily upskill a BI or ETL resource without any programming knowledge to work with this. That is a major advantage that we are getting since we have a lot of ETL people who do not have programming knowledge. They have vast ETL experience working with GUI-based tools, and StreamSets is really useful for them.

It has drastically reduced the time that we are spending on workloads by 60% to 70% as well as reducing the time spent on ingestion by 30%. 

What's my experience with pricing, setup cost, and licensing?

It has a CPU core-based licensing, which works for us and is quite good.

Which other solutions did I evaluate?

We did evaluate other solutions. It was not a quick decision for us to take this product. We evaluated other products in the market, but they were not close to StreamSets or not in the data integration space. One thing that caught our attention with StreamSet was the processes that it could work with. Secondly, the Control Hub DataOps platform manages the load balancing, etc. We were quite interested in that since we would not need to maintain it ourselves. The third most important thing was that you can create job templates in StreamSets. So, this means you create a template for a particular type of ingestion. Going forward, you just change the parameters, then you can point it to any source. This means there is less pipeline development and we can quickly ingest data into the data lake. Those are the features that we were interested in and why we switched StreamSets.

There is actually a gap in the entire data integration market at the moment, and StreamSets Data Collector is trying to fill that gap. The reason is because most data ingestion has to be done through programming languages, like Python or Java. We currently do not have a GUI-based tool set that is as robust as StreamSets. That is what I found out in the lab over the last 12 months. There are new products coming up, but it will still be a few more years until they are stabilized. Whereas, StreamSets is already there to solve your immediate data ingestion requirements. 

What other advice do I have?

Every tool in the market at the moment has some major gaps, especially for large enterprises. It could be the way that the data or pipeline is secured. At present, StreamSets looks like the market leader and is trying to fill that gap. For anyone going through a proof of concept for various tools, StreamSets is almost at the top. I don't think that they need to look any further.

We are working only with API, a relational database management system, and our enterprise warehouses at the moment. We are not using any streaming sort of ingestion at the moment.

We are not using Snowflake Transformer yet. It just got released. We are using a traditional Snowflake destination stage because our enterprise is huge. We have our own Snowflake architecture. We load the security in the data into our own databases using the destination stage, not Transformer yet.

I would rate the solution as 7.5 out of 10.

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Amazon Web Services (AWS)
Disclosure: PeerSpot contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
PeerSpot user
Director Data Engineering, Governance, Operation and Analytics Platform at a financial services firm with 10,001+ employees
Real User
Top 20
Ease of configuring and managing pipelines centrally
Pros and Cons
  • "I really appreciate the numerous ready connectors available on both the source and target sides, the support for various media file formats, and the ease of configuring and managing pipelines centrally."
  • "StreamSets should provide a mechanism to be able to perform data quality assessment when the data is being moved from one source to the target."

What is our primary use case?

We are using StreamSets to migrate our on-premise data to the cloud.

What is most valuable?

I really appreciate the numerous ready connectors available on both the source and target sides, the support for various media file formats, and the ease of configuring and managing pipelines centrally. It's like a plug-and-play setup.

What needs improvement?

StreamSets should provide a mechanism to be able to perform data quality assessment when the data is being moved from one source to the target. So the ability to validate the data against various data rules. Then, based on the failure of data quality assessment, be able to send alerts or information to help people understand the data validation issues.

For how long have I used the solution?

I have been using StreamSets for a year and a half. 

What do I think about the stability of the solution?

It's reasonably stable.

What do I think about the scalability of the solution?

It's reasonably easy to scale. Around 25 to 30 end users are using this solution in our organization.

How are customer service and support?

Customer service and support are good. 

How would you rate customer service and support?

Positive

How was the initial setup?

It's reasonably easy to deploy. However, since it is used at an enterprise level, it requires maintenance. So we had a maintenance contract. 

In the financial industry, we have very strict regulations around deploying something in the cloud. So, it requires a lot of permission and other processes.

Just one person is enough for the maintenance. 

What's my experience with pricing, setup cost, and licensing?

The pricing was reasonably economical and easy for us to afford when we engaged with StreamSets. It was not part of Software AG at that time.

What other advice do I have?

It's a very good tool. Overall, I would rate the solution an eight out of ten. 

Which deployment model are you using for this solution?

Hybrid Cloud
Disclosure: I am a real user, and this review is based on my own experience and opinions.
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PeerSpot user
Al Mercado - PeerSpot reviewer
AI Engineer at Techvanguard
Real User
A no-code solution with a drag-and-drop UI, but the execution engine should be better
Pros and Cons
  • "The most valuable would be the GUI platform that I saw. I first saw it at a special session that StreamSets provided towards the end of the summer. I saw the way you set it up and how you have different processes going on with your data. The design experience seemed to be pretty straightforward to me in terms of how you drag and drop these nodes and connect them with arrows."
  • "The execution engine could be improved. When I was at their session, they were using some obscure platform to run. There is a controller, which controls what happens on that, but you should be able to easily do this at any of the cloud services, such as Google Cloud. You shouldn't have any issues in terms of how to run it with their online development platform or design platform, basically their execution engine. There are issues with that."

What is our primary use case?

I was working on an integration project where I was using the StreamSets platform. I was looking at both their data collector and their transformer. The idea was to integrate it with AWS SageMaker Canvas. Both of them are what they call no-code options. StreamSets is for data pipelining, managing your data flow, and transforming your data. SageMaker is AWS, and Canvas is basically their no-code option for machine learning.

I was trying to connect it to a data object repository. For AWS, that's a specific managed service called S3. I wasn't trying to run it with a data warehouse.

How has it helped my organization?

It's still in the trial stage. I don't get a 30-day trial period or anything like that. I just got to write about what's involved and then see if that's something that justifies the use case for going ahead and purchasing the license for it.

It enables you to build data pipelines without knowing how to code. It abstracts away the need for Spark or anything like that. This ability is highly important because it reduces development time.

It saves time because you don't have to write code. 

It saves money by not having to hire people with specialized skills. You don't need Spark or anything like that for doing the same thing.

It helps to scale your data operations. You can get to the execution engine and provision bigger machines or bigger clusters. You can scale out to however much data you need to scale out to.

What is most valuable?

The most valuable would be the GUI platform that I saw. I first saw it at a special session that StreamSets provided towards the end of the summer. I saw the way you set it up and how you have different processes going on with your data. The design experience seemed to be pretty straightforward to me in terms of how you drag and drop these nodes and connect them with arrows.

What needs improvement?

The execution engine could be improved. When I was at their session, they were using some obscure platform to run. There is a controller, which controls what happens on that, but you should be able to easily do this at any of the cloud services, such as Google Cloud. You shouldn't have any issues in terms of how to run it with their online development platform or design platform, basically their execution engine. There are issues with that.

It can break down data silos within the organization. One person can do the whole thing with StreamSets and SageMaker Canvas, but it hasn't yet had any effect on our operations or business because it's one of those situations where you can either get a demo from them or you basically have to go to one of these sessions and they give you temporary credentials and try to work with your use case. Personally, I would change their model a bit and give a two-week trial license for a cloud platform at the very least. You can then try to get something to work or call up their technical department and say, "Look, I've been evaluating this thing for the last few days. I don't know exactly how to resolve this issue."

For how long have I used the solution?

I started using it in June of this year. 

What do I think about the stability of the solution?

The whole issue of the execution engine needs to be better resolved. If you pick a cloud, why isn't it working with this cloud? Or what do I need to do to get it to work with one specific cloud service if it can be deployed across multiple clouds?

What do I think about the scalability of the solution?

It seems pretty highly scalable to me. That's not going to be an issue. Just the administration of it could be an issue.

It's currently being used in a dev department for machine learning. It's being used by the business analyst team.

How are customer service and support?

I haven't contacted their support.

Which solution did I use previously and why did I switch?

AWS has native solutions. There are AWS Data Wrangler and others that come bundled with their services, like AWS Glue. We haven't yet switched to StreamSets. It's still in the evaluation stage, but the no-code and the drag-and-drop option with a GUI are some of the things that seem to resonate with people. 

How was the initial setup?

I was involved in its setup. I was the one who basically had to try to get it to run with whatever process or custom processor I developed. 

It was complex to set up. I had to go to the sessions. On a couple of occasions, I was doing it directly from the cloud platform, and apparently, that wasn't the way to do it. You have to go through their universal designer platform first. 

In terms of maintenance, once you're deployed from the cloud, that's all handled for you. It's managed for you directly from the cloud service. So, you don't have to worry about that. They maintain their design platform.

What about the implementation team?

I didn't use any consultant.

What's my experience with pricing, setup cost, and licensing?

I didn't get into that with the StreamSets representative. It seems to be pay-as-you-go, but I don't know exactly how they do it.

Which other solutions did I evaluate?

Alteryx is another option. It's a similar tool, and it looks almost the same as StreamSets. Alteryx is something that's available for any cloud. It doesn't matter which cloud. You go on the various clouds, and you look and see what they have.

What other advice do I have?

To those evaluating this solution, I would advise looking into how it integrates with the cloud service that they're going to try it with. Does it naturally integrate better with AWS or Azure? It's one of those situations.

I used StreamSets' ability to move data into a modern analytics platform. That's what the AWS SageMaker Canvas is. It's like predictive analytics. In terms of ease of moving data into this analytics platform, doing the design on the StreamSets platform is one thing, but having the execution engine and getting that provision is a totally different ball game. Basically, that's where its limitation comes in.

Overall, I would rate it a seven out of ten. The issue that was never resolved for me was if you're running a compute or execution engine on AWS versus Azure versus GCP, how does that integration work because that has got nothing to do with StreamSets? That is outside of StreamSets. You're now dealing with the cloud service, and there's a good reason for that.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Amazon Web Services (AWS)
Disclosure: PeerSpot contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
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Buyer's Guide
Download our free StreamSets Report and get advice and tips from experienced pros sharing their opinions.
Updated: March 2024
Product Categories
Data Integration
Buyer's Guide
Download our free StreamSets Report and get advice and tips from experienced pros sharing their opinions.