We performed a comparison between Equalum and StreamSets based on real PeerSpot user reviews.
Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."It's a really powerful platform in terms of the combination of technologies they've developed and integrated together, out-of-the-box. The combination of Kafka and Spark is, we believe, quite unique, combined with CDC capabilities. And then, of course, there are the performance aspects. As an overall package, it's a very powerful data integration, migration, and replication tool."
"Equalum is real-time. If you are moving from an overnight process to a real-time process, there is always a difference in what reports and analytics show compared to what our operational system shows. Some of our organizations, especially finance, don't want those differences to be shown. Therefore, going to a real-time environment makes the data in one place match the data in another place. Data accuracy is almost instantaneous with this tool."
"It's got it all, from end-to-end. It's the glue. There are a lot of other products out there, good products, but there's always a little bit of something missing from the other products. Equalum did its research well and understood the requirements of large enterprise and governments in terms of one tool to rule them all, from a data migration integration perspective."
"The main impact for Oracle LogMiner is the performance. Performance is drastically reduced if you use the solution’s Oracle Binary Log Parser. So, if we have 60 million records, initially it used to take a minute. Now, it takes a second to do synchronization from the source and target tables."
"I found two features in Equalum that I consider the most valuable. One is that Equalum is a no-code tool. You can do your activities on its graphical interface, which doesn't require complex knowledge of extracting, changing, or loading data. Another feature of Equalum that I like the most is that it monitors the data transfers and tells you if there's any issue so that you can quickly check and correct it. Equalum also tells you where the problem lies, for example, if it's a hardware or communication issue."
"All our architectural use cases are on a single platform, not multiple platforms. You don't have to dump into different modules because it is the same module everywhere."
"Equalum has resulted in system performance improvements in our organization. Now, I am ingressing data off of multiple S3 sources, doing data processing, and formatting a schema. This would usually take me a couple of days, but now it takes me hours."
"Equalum provides a single platform for core architectural use cases, including CDC replication, streaming ETL, and batch ETL. That is important to our clients because there is no other single-focus product that covers these areas in that much detail, and with this many features on the platform. The fact that they are single-minded and focused on CDC and ETL makes this such a rich solution. Other solutions cover these things a little bit in their multi-function products, but they don't go as deep."
"One of the things I like is the data pipelines. They have a very good design. Implementing pipelines is very straightforward. It doesn't require any technical skill."
"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."
"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."
"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 scheduling within the data engineering pipeline is very much appreciated, and it has a wide range of connectors for connecting to any data sources like SQL Server, AWS, Azure, etc. We have used it with Kafka, Hadoop, and Azure Data Factory Datasets. Connecting to these systems with StreamSets is very easy."
"The Ease of configuration for pipes is amazing. It has a lot of connectors. Mainly, we can do everything with the data in the pipe. I really like the graphical interface too"
"The ETL capabilities are very useful for us. We extract and transform data from multiple data sources, into a single, consistent data store, and then we put it in our systems. We typically use it to connect our Apache Kafka with data lakes. That process is smooth and saves us a lot of time in our production systems."
"The most valuable feature is the pipelines because they enable us to pull in and push out data from different sources and to manipulate and clean things up within them."
"I should be able to see only my project versus somebody else's garbage. That is something that would be good in future. Right now, the security is by tenants, but I would like to have it by project, e.g., this project has this source and flows in these streams, and I have access to this on this site."
"Their UI could use some work. Also, they could make it just a little faster to get around their user interface. It could be a bit more intuitive with things like keyboard shortcuts."
"There is not enough proven integration with other vendors. That is what needs to be worked on. Equalum hasn't tested anything between vendors, which worries our clients. We need more proven vendor integration. It is an expensive product and it needs to support a multi-vendor approach."
"Right now, they have a good notification system, but it is in bulk. For example, if I have five projects running and I put a notification, the notification comes back to me for all five projects. I would like the notification to come back only for one project."
"The deployment of their flows needs improvement. It doesn't work with a typical Git branching and CI/CD deployment strategy."
"They need to expand their capabilities in some of the targets, as well as source connectors, and native connectors for a number of large data sources and databases. That's a huge challenge for every company in this area, not just Equalum."
"If you need to use the basic features of Equalum, for example, you don't even need data integration, then many competitors in the market can give you basic features. For instance, if you need batch ETL, you can pick among solutions in the market that have been around longer than Equalum. What needs improvement in Equalum is replication, as it could be faster. Equalum also needs better integration with specific databases such as Oracle and Microsoft SQL Server."
"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."
"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."
"If you use JDBC Lookup, for example, it generally takes a long time to process data."
"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."
"We create pipelines or jobs in StreamSets Control Hub. It is a great feature, but if there is a way to have a folder structure or organize the pipelines and jobs in Control Hub, it would be great. I submitted a ticket for this some time back."
"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."
"One thing that I would like to add is the ability to manually enter data. The way the solution currently works is we don't have the option to manually change the data at any point in time. Being able to do that will allow us to do everything that we want to do with our data. Sometimes, we need to manually manipulate the data to make it more accurate in case our prior bifurcation filters are not good. If we have the option to manually enter the data or make the exact iterations on the data set, that would be a good thing."
"Sometimes, when we have large amounts of data that is very efficiently stored in Hadoop or Kafka, it is not very efficient to run it through StreamSets, due to the lack of efficiency or the resources that StreamSets is using."
Equalum is ranked 30th in Data Integration with 7 reviews while StreamSets is ranked 8th in Data Integration with 24 reviews. Equalum is rated 9.2, while StreamSets is rated 8.4. The top reviewer of Equalum writes "Frees staff to focus on data workflow and on what can be done with data, and away from the details of the technology". On the other hand, the top reviewer of StreamSets writes "We no longer need to hire highly skilled data engineers to create and monitor data pipelines". Equalum is most compared with Azure Data Factory and Fivetran, whereas StreamSets is most compared with Fivetran, Informatica PowerCenter, Azure Data Factory, SSIS and IBM InfoSphere DataStage. See our Equalum vs. StreamSets report.
See our list of best Data Integration vendors and best Cloud Data Integration vendors.
We monitor all Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
