We performed a comparison between IBM InfoSphere DataStage and SAP Data Hub based on real PeerSpot user reviews.
Find out what your peers are saying about Microsoft, Informatica, Oracle and others in Data Integration."The most valuable feature of the solution is the ability to incorporate very complex business rules in Data Stage."
"The solution's scalability is really good...we are using multi-instance jobs where you can scale them easily."
"When we have needed help from the IBM team, they were helpful. Our company is a premium partner so we get fast responses."
"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."
"Offers great flexibility."
"Finding logs is very easy on the solution."
"I am impressed with the tool's ETL tracing."
"The most valuable feature is the ability to transfer information via notes."
"Its connection to on-premise products is the most valuable. We mostly use the on-premise connection, which is seamless. This is what we prefer in this solution over other solutions. We are using it the most for the orchestration where the data is coming from different categories. Its other features are very much similar to what they are giving us in open source. Their push-down approach is the most advantageous, where they push most of the processing on to the same data source. This means that they have a serverless kind of thing, and they don't process the data inside a product such as Data Hub. They process the data from where the data is coming out. If it is coming from HANA, to capture the data or process it for analytics, orchestration, or management, they go to the HANA database and give it out. They don't process it on Data Hub. This push-down approach increases the processing speed a little bit because the data is processed where it is sitting. That's the best part and an advantage. I have used another product where they used to capture the data first and then they used to process it and give it. In Data Hub, it is in reverse. They process it first and give it, and then they put their own manipulations. They lead in terms of business functions. No other solution has business functions already implemented to perform business analysis. They have a lot of prebuilt business functions for machine learning and orchestration, which we can use directly to get an analysis out from the existing data. Most of the data is sitting as enterprise data there. That's a major advantage that they have."
"SAP is one of the most seamless ERPs that have integrated SAP archiving within Excel. I have not seen this with any other database."
"The most valuable feature is the S/4HANA 1909 On-Premise"
"The documentation and in-application help for this solution need to be improved, especially for new features."
"I'd like to be able to do more with the data and metadata, including copy and pasting, et cetera."
"The setup is extremely difficult."
"I really like this tool, but the administration should be on the same client application because a lot of administration features are not on the client-side, and they usually need to have administrative access. It's quite complicated to force IT teams to have separate administrative access from the developers."
"The response time from support is slow and needs to be improved."
"The interface needs improvement. It is really too technical. That is the main problem."
"So, there are some features that are missing. If I compare DataStage to Talend, Talend allows you to write custom code in Java or use these tools in your applications as well if you are building a job application. But in DataStage, it does not allow you to write custom code for any component."
"The interface needs improvement."
"In 2018, connecting it to outside sources, such as IoT products or IoT-enabled big data Hadoop, was a little complex. It was not smooth at the beginning. It was unstable. It took a lot of time for the initial data load. Sometimes, the connection broke, and we had to restart the process, which was a major issue, but they might have improved it now. It is very smooth with SAP HANA on-premise system, SAP Cloud Platform, and SAP Analytics Cloud. It could be because these are their own products, and they know how to integrate them. With Hadoop, they might have used open-source technologies, and that's why it was breaking at that time. They are providing less embedded integration because they want us to use their other products. For example, they don't want to go and remove SAP Analytics Cloud and put everything in Data Hub. They want us to use SAP Analytics Cloud somewhere else and not inside the Data Hub. On the integration part, it lacks real-time analytics, and it is slow. They should embed the SAP Analytics Cloud inside Data Hub or support some kind of analysis. They do provide some analysis, but it is not extensive. They are moreover open source. So, we need a lot of developers or data scientists to go in and implement Python algorithms. It would be better if they can provide their own existing algorithms and give some connections and drop-down menus to go and just configure those. It will make things really quick by increasing the embedded integrations. It will also improve the process efficiency and processing power. Its performance needs improvement. It is a little slow. It is not the best in the market, and there are other products that are much better than this. In terms of technology and performance, it is a little slow as compared to Microsoft and other data orchestration products. I haven't used other products, but I have read about those products, their settings, and the milliseconds that they do. In Azure Purview, they say that they can copy, manage, or transform the data within milliseconds. They say that they can transform 100 gigabytes of data within three to five seconds, which is something SAP cannot do. It generally takes a lot of time to process that much amount of data. However, I have never tested out Azure."
"Nowadays there are some inconsistencies in data bases, however, they upgrade and release the versions to market."
"The company has everything offshore."
IBM InfoSphere DataStage is ranked 7th in Data Integration with 37 reviews while SAP Data Hub is ranked 25th in Data Governance with 3 reviews. IBM InfoSphere DataStage is rated 7.8, while SAP Data Hub is rated 7.6. The top reviewer of IBM InfoSphere DataStage writes "User-friendly with a lot of functions for transmission rules, but has slow performance and not suitable for a huge volume of data". On the other hand, the top reviewer of SAP Data Hub writes "The solution is seamless, but the database sometimes leads to confusion". IBM InfoSphere DataStage is most compared with SSIS, IBM Cloud Pak for Data, Azure Data Factory, Talend Open Studio and Informatica PowerCenter, whereas SAP Data Hub is most compared with Microsoft Purview, SAP Data Services, Alation Data Catalog, Azure Data Factory and Palantir Foundry.
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.
