We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"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."
"You can create a lot of ingestions based on the file levels or based on the time."
"The most valuable feature is the ability to map data and write workflows with logic inside them."
"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."
"The major pain point with Zaloni is that their exception handling is not good. If any event happens, it doesn't tell you at which point it failed and it doesn't tell the operations team how they should take corrective actions unless you call Zaloni and then identify the issues. That is one issue."
"Technical support is in need of improvement."
The SAP® Data Hub solution enables sophisticated data operations management. It gives you the capability and flexibility to connect enterprise data and Big Data and gain a deep understanding of data and information processes across sources and systems throughout the distributed landscape. The unified solution provides visibility and control into data opportunities, integrating cloud and on-premise information and driving data agility and business value. Distributed processing power enables greater speed and efficiency.
Zaloni simplifies big data for transformative business insights. We work with pioneering enterprises to modernize their data architecture and operationalize their data to accelerate insights for everyday business practices.
SAP Data Hub is ranked 7th in Data Governance with 1 review while Zaloni Data Platform is ranked 15th in Data Governance with 2 reviews. SAP Data Hub is rated 6.0, while Zaloni Data Platform is rated 6.6. The top reviewer of SAP Data Hub writes "Good push-down approach, on-premise connection, and integration with SAP products, but needs better performance and integration with other solutions". On the other hand, the top reviewer of Zaloni Data Platform writes "Solid multi-ingestion tool but with poor exception handling". SAP Data Hub is most compared with SAP Data Services, Palantir Foundry, Azure Data Factory, SAP Process Orchestration and Talend Data Fabric, whereas Zaloni Data Platform is most compared with Collibra Governance, Alteryx and Alation Data Catalog.
See our list of best Data Governance vendors.
We monitor all Data Governance 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.