SAP Data Hub vs erwin Data Intelligence by Quest comparison

"The biggest impact for us is that erwin generates DDL extremely quickly. We're able to pull in metadata, map it to a target, generate DDL to create the tables, and generate SSIS packages. Previously, especially going back 10 to 15 years ago, hundreds of hours had to be spent to manually perform these tasks. This solution completely automates it and gets it 90% done. We can then pass it off to a developer to create the items in SSIS."

"It is a central place for everybody to start any ETL data pipeline builds. This tool is being heavily used, plus it's heavily integrated with all the ETL data pipeline design and build processes. Nobody can bypass these processes and do something without going through this tool."

"erwin has tremendous capabilities to map right from the business technologies to the endpoint, such as physical entities and physical attributes, from a lineage standpoint."

"The interface is easy to use. I also like Erwin's automatic data classification and data quality checks."

"Mind map... is a really good feature because it is very helpful in seeing which column's tables are related. Also, you can flag them with "sensitive data" and other indicators. You can also customize your own features for the mind map. That was another very robust feature."

"We always know where our data is, and anybody can look that up, whether they're a business person who doesn't know anything about Informatica, or a developer who knows everything about creating data movement jobs in Informatica, but who does not understand the business terminology or the data that is being used in the tool."

"There is a wide range of widgets that enables the user to find the proper information quickly. The presentation of information is something very valuable."

"Data Intelligence creates a single source of truth for all of our metadata. This solution is better for data warehousing, but the metadata features speed up our development work. It's easy to create and manage mappings because we can export them to Informatica and pick up the work where we left off."

"The most valuable feature is the S/4HANA 1909 On-Premise"

"SAP is one of the most seamless ERPs that have integrated SAP archiving within Excel. I have not seen this with any other database."

"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."

"Another area where it can improve is by having BB-Graph-type databases where relationship discovery and relationship identification are much easier."

"Everything about Data Intelligence is complex. Though we've used the tool for five years, we're still only using about 30 to 40 percent of its capabilities. It would be helpful if we could customize and simplify the user interface because there are so many redundant things."

"The metadata ingestion is very nice because of the ability to automate it. It would be nice to be able to do this ingestion, or set it up, from one place, instead of having to set it up separately for every data asset that is ingested."

"The technical support could be improved."

"One big improvement we would like to see would be the workflow integration of codeset mapping with the erwin source to target mapping. That's a bit clunky for us. The two often seem to be in conflict with one another. Codeset mappings that are used within the source to target mappings are difficult to manage because they get locked."

"There are a lot of little things like moving between read screens and edit screens. Those little human interface type of programming pieces will need to mature a bit to make it easier to get to where you want to go to put the stuff in."

"The SDK behind this entire product needs improvement. The company really should focus more on this because we were finding some inconsistencies on the LDK level. Everything worked fine from the UI perspective, but when we started doing some deep automation scripts going through multiple API calls inside the tool, then only some pieces of it work or it would not return the exact data it was supposed to do."

"The versioning can sometimes be confusing because we use the publishing feature for the mapping. Technical analysts sometimes have two versions, and they should know that the public version is the correct one."

"Nowadays there are some inconsistencies in data bases, however, they upgrade and release the versions to market."

"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 company has everything offshore."

"The whole suite, not just the DI but the modeling software, the harvester, Mapping Manager — everything we have — is about $100,000 a year for our renewals. That works out to each module being something like $8,000 to $10,000."

"There is an additional fee for the server maintenance."

"The price is reasonable, and a subscription is required."

"We operate on a yearly subscription and because it is an enterprise license we only have one. It is not dependent on the number of users."

"Smart Data Connectors have some costs, and then there are user-based licenses. We spend roughly $150,000 per year on the solution. It is a yearly subscription license that basically includes the cost for Smart Data Connectors and user-based licenses. We have around 30 data stewards who maintain definitions, and then we have five IT users who basically maintain the overall solution. It is not a SaaS kind of operation, and there is an infrastructure cost to host this solution, which is our regular AWS hosting cost."

"erwin was at a good price. The federal government wouldn't buy something if the pricing wasn't good."

"The licensing cost was very affordable at the time of purchase. It has since been taken over by erwin, then Quest. The tool has gotten a bit more costly, but they are adding more features very quickly."

"The licensing cost is around $7,000 for user. This is an estimation."

"The Cloud is very expensive, but SAP HANA previous service is okay."