Astera ReportMiner vs. Databricks Data Intelligence Platform

Astera ReportMiner is well suited for those who work with EMR Data or business operations data where different platforms may report on similar data but not everything is the same between the different systems and how they export their data. It works especially well when systems can't edit their exports so you need to clean up data a lot before they are combined into a single file. It is less appropriate a solution when you are trying to combine large data, let's say thousands of files and need some sort of platform to deduce analytic patterns out of it. This is more used for data preparedness rather than big data analysis. This may have changed with recent updates, however they do not do a good job of updating customers on new releases so keeping track of their developments is something worth doing if data management is an important part of your/your company's role.

Medium to Large data throughput shops will benefit the most from Databricks Spark processing. Smaller use cases may find the barrier to entry a bit too high for casual use cases. Some of the overhead to kicking off a Spark compute job can actually lead to your workloads taking longer, but past a certain point the performance returns cannot be beat.

Incentivized

• ReportMiner's OCR accuracy is flawless to my experience. I use the program to pull values from check details, and have yet to find an error.
• ReportMiner's interface is easy to learn. I never even got formal training on the program. I watched some YouTube videos and learned as I went.
• ReportMiner training personnel is very knowledgeable and quick to respond. Anytime I ran into a problem I could not figure out on my own, they would get back with me within a half a day.

• 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

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.

I have never actually used another program like ReportMiner. Before I was just using PDF viewers such as Adobe that had OCR capabilities. I would have to select a range of numbers and copy and paste to Excel. It was tedious, slow, and sometimes error prone. I chose to learn ReportMiner because it saves me at least 4 times the amount of time I would have to use by copy and pasting.

The most important differentiating factor for Databricks Lakehouse Platform from these other platforms is support for ACID transactions and the time travel feature. Also, native integration with managed MLflow is a plus. EMR, Cloudera, and Hortonworks are not as optimized when it comes to Spark Job Execution. Other platforms need to be self-managed, which is another huge hassle.

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.

• 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.

Incentivized