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 Well suited for my big data related project or a static data set analysis especially for uploading huge dataset to the cluster. But had some issues with connecting IoT real-time data and feeding to Power BI. It might be my understanding please take it as a mere comment rather than a suggestion. 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 Jobs with Spark, Hadoop, or Hive queries are rapidly attained Can collect, organize and analyze your data accurately You can customize, for example, Spark or Hadoop configuration settings, or Python, R, Scala, or Java libraries. 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 Easier pricing and plug-and-play like you see with AWS and Azure, it would be nice from a budgeting and billing standpoint, as well as better support for the administration. Bundling of the Cloud Object Storage should be included with the Analytics Engine. The inability to add your own Hadoop stack components has made some transfers a little more complex. 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 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 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 We initially wanted to go with
Google BigQuery , mainly for the name recognition. However, the pricing and support structure led us to seek alternatives, which pointed us to IBM.
Apache Spark was also in the running, but here IBM's domination in the industry made the choice a no-brainer. As previously stated, the support received was not quite what we expected, but was adequate.
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 This product has allowed us to gather analytics data across multiple platforms so we can view and analyze the data from different workflows, all in one place. IBM Analytics has allowed us to scale on demand which allows us to capture more and more data, thus increasing our ROI. The convenience of the ability to access and administer the product via multiple interfaces has allowed our administrators to ensure that the application is making a positive ROI for our business users and partners. Read full review ScreenShots