Astera ReportMiner vs. Databricks Data Intelligence Platform

ReportMiner is well suited for digitizing PDFs that includes data that follows a pattern such as a bank statement. It saves a bunch of time to make one report that will be able to be used each month for that bank. The program would not be useful if you had to spend the time making a new report each month for a new bank with a few lines of data

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.

Incentivized

• It performs its function well, with flexibility and relative ease.
• Ability to easily convert PDF into text, especially in those situations where PDF documents have complicated formats.

• There is databricks community, which is a free version. It is available for beginners to have an easy start with a big data platform. It does not have every feature of the full version but is still adequate for extremely new coders.
• There are many resourceful training elements that are available to developers, data scientists, data engineers and other IT professionals to learn Apache Spark.

Incentivized

• Could provide some features to help with advanced analytics for big data. (i.e. larger data sets)
• Too much clutter on their Youtube page, they should highlight the tutorials so they are easier to find for new users. Get rid of old tutorial video playlists so the organization is clean and up to date.
• Have sales rep follow up with customers to offer product updates, new product releases, and do check ins to see if customers have suggestions for feature improvement.

• 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

Incentivized

It is the best way to get a multi line record from pdf to Excel in one row.

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

Incentivized

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.

Astera ReportMiner had a more concentrated feature stack for what we were looking for at a cheaper price so we went with it instead of other competitors that had more features, more complicated UIs, and had more expensive subscription packages because of it. They also had an easy purchase/setup process at the time of our procurement.

Databricks is a true all-in-one platform, and at the time of implementation, it had more features available to us, making it a clear choice over Snowflake. Moving our workloads from local computing to the servers in Databricks gave our start-up staff a great quality of life boost.

Incentivized

• Efficient and automated data extraction. Saves time and resources.
• User-friendly interface and good documentation. It is therefore easiest to learn and apply in a short time.
• Documents, which have various formats of data tables or arrangement, needed a lot of manual fixing. So it required a lot of time for validation and quality control.

• ROI for us has been tremendous. Time to market by processing raw data in our big data infrastructure has been pretty fast.
• Non engineers can easily use Databricks, hence helping business customers.
• Thousands of different data combinations can easily be joined and used by our data teams.

Incentivized