Likelihood to Recommend If you need a managed big data megastore, which has native integration with highly optimized
Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.
Read full review My recommendation obviously would depend on the application. But I think given the right requirements, IBM DB2 Big SQL is definitely a contender for a database platform. Especially when disparate data and multiple data stores are involved. I like the fact I can use the product to federate my data and make it look like it's all in one place. The engine is high performance and if you desire to use Hadoop, this could be your platform.
Gene Baker Vice President, Chief Architect, Development Manager and Software Engineer
Read full review Pros Process raw data in One Lake (S3) env to relational tables and views Share notebooks with our business analysts so that they can use the queries and generate value out of the data Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers Read full review data storage data manipulation data definitions data reliability Read full review Cons Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code). Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally. Visualization in MLFLOW experiment can be enhanced Read full review Cloud readiness. Ease of implementation. Gene Baker Vice President, Chief Architect, Development Manager and Software Engineer
Read full review Usability Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured. in terms of graph generation and interaction it could improve their UI and UX
Read full review IBM DB2 is a solid service but hasn't seen much innovation over the past decade. It gets the job done and supports our IT operations across digital so it is fair.
Read full review Support Rating One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.
Read full review IBM did a good job of supporting us during our evaluation and proof of concept. They were able to provide all necessary guidance, answer questions, help us architect it, etc. We were pleased with the support provided by the vendor. I will caveat and say this support was all before the sale, however, we have a ton of IBM products and they provide the same high level of support for all of them. I didn't see this being any different. I give IBM support two thumbs up!
Gene Baker Vice President, Chief Architect, Development Manager and Software Engineer
Read full review Alternatives Considered Compared to
Synapse &
Snowflake , Databricks provides a much better development experience, and deeper configuration capabilities. It works out-of-the-box but still allows you intricate customisation of the environment. I find Databricks very flexible and resilient at the same time while
Synapse and
Snowflake feel more limited in terms of configuration and connectivity to external tools.
Read full review MS SQL Server was ruled out given we didn't feel we could collapse environments. We thought of MS-SQL as more of a one for one replacement for Sybase ASE, i.e., server for server.
SAP HANA was evaluated and given a big thumbs up but was rejected because the SQL would have to be rewritten at the time (now they have an accelerator so you don't have to). Also, there was a very low adoption rate within the enterprise. IBM DB2 Big SQL was not selected even though technically it achieved high scores, because we could not find readily available talent and low adoption rate within the enterprise (basically no adoption at the time). We ended up selecting Exadata because of the high adoption rate within the enterprise even though technically HANA and Big SQL were superior in our evaluations.
Gene Baker Vice President, Chief Architect, Development Manager and Software Engineer
Read full review Return on Investment The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin DB has the ability to terminate/time out instances which helps manage cost. The ability to quickly access typical hard to build data scenarios easily is a strength. Read full review better data visibility solid reliability for mission critical data Read full review ScreenShots