"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."
"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."
"In StreamSets, everything is in one place."
"It is really easy to set up and the interface is easy to use."
"I have used Data Collector, Transformer, and Control Hub products from StreamSets. What I really like about these products is that they're very user-friendly. People who are not from a technological or core development background find it easy to get started and build data pipelines and connect to the databases. They would be comfortable like any technical person within a couple of weeks."
"Offers great flexibility."
"We are mostly using transmission rules. It has a lot of functions and logic related to transmission. It is a user-friendly tool with in-built functions."
"Once you have Infosphere up and running properly, it is stable."
"The Hierarchical Data Stage is good."
"It is quite useful and powerful."
"As a data integration platform, it is easy to use. It is quite robust and useful for volumetric analysis when you have huge volumes of data. We have tested it for up to ten million rows, and it is robust enough to process ten million rows internally with its parallel processing. Its error logging mechanism is far simpler and easier to understand than other data integration tools. The newer version of InfoSphere has the data catalog and IDC lineage. They are helpful in the easy traceability of columns and tables."
"It's a robust solution."
"The performance optimization is quite good in DataStage. It provides parallelism and pipelining mechanisms"
"The tool is reliable, quick, and powerful."
"The technical support is excellent."
"Its robustness is valuable. It is a full-fledged suite. We have a data warehouse model, and there are also a lot of data quality management tools. The repository and all other tools are there. So, it is a full package in terms of reporting tools."
"The product offers very good flexibility."
"In terms of which features I have found most valuable, I would say the importing and exporting features. Additionally, the data sorting, categorizing and summarizing features, especially how it can summarize based on categories. These are the key features."
"We've seen a couple of cases where it appears to have a memory leak or a similar problem."
"If you use JDBC Lookup, for example, it generally takes a long time to process data."
"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."
"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."
"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."
"Currently lacking virtualization ability."
"The response time from support is slow and needs to be improved."
"The pricing should be lower."
"The initial setup could be more straightforward."
"In the future, I would like to see more integration with cloud technologies."
"Its documentation is not up to the mark. While building APIs, we had a lot of problems trying to get around it because it is not very user-friendly. We tried to get hold of API documentation, but the documentation is not very well thought out. It should be more structured and elaborate. In terms of additional features, I would like to see good reporting on performance and performance-tuning recommendations that can be based on AI. I would also like to see better data profiling information being reported on InfoSphere."
"The setup is extremely difficult."
"It would be useful to provide support for Python, AR, and Java."
"The solution could use better documentation."
"With SAS Data Management, you have to purchase an external driver, configure all of the tables for all of the data that you will extract from Salesforce. It's not a straightforward process."
"One problem is accessing the data using a solution other than SAS. The SAS data, which we create in the SAS, cannot be accessed by other tools. We can't open those data in other applications. So we need to have that application in place."
"We implemented it a while ago, and we are trying to improve the data delivery performance. We are looking into how to get faster and automated reporting. We would need better designs and workflows."
"The solution is quite expensive and hard to install/configure."
IBM InfoSphere DataStage is ranked 9th in Data Integration Tools with 9 reviews while SAS Data Management is ranked 15th in Data Integration Tools with 5 reviews. IBM InfoSphere DataStage is rated 7.6, while SAS Data Management is rated 7.8. The top reviewer of IBM InfoSphere DataStage writes "Robust, easy to use, has a simple error logging mechanism, and works very well for huge volumes of data". On the other hand, the top reviewer of SAS Data Management writes "One-stop-shop solution for sorting, categorizing and summarizing your data". IBM InfoSphere DataStage is most compared with SSIS, Talend Open Studio, AWS Glue, Informatica PowerCenter and Azure Data Factory, whereas SAS Data Management is most compared with Informatica PowerCenter, Informatica Axon, Collibra Governance, Microsoft Purview and Palantir Gotham. See our IBM InfoSphere DataStage vs. SAS Data Management report.
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