Astera ReportMiner vs. dbt

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.

If you can load your data first into your warehouse, dbt is excellent. It does the T(ransformation) part of ELT brilliantly but does not do the E(xtract) or L(oad) part. If you know SQL or your development team knows SQL, it's a framework and extension around that. So, it's easy to learn and easy to hire people with that technical skill (as opposed to specific Informatica, SnapLogic, etc. experience). dbt uses plain text files and integrates with GitHub. You can easily see the changes made between versions. In GUI-based UIs it was always hard to tell what someone had changed. Each "model" is essentially a "SELECT" statement. You never need to do a "CREATE TABLE" or "CREATE VIEW" - it's all done for you, leaving you to work on the business logic. Instead of saying "FROM specific_db.schema.table" you indicate "FROM ref('my_other_model')". It creates an internal dependency diagram you can view in a DAG. When you deploy, the dependencies work like magic in your various environments. They also have great documentation, an active slack community, training, and support. I like the enhancements they have been making and I believe they are headed in a good direction.

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.

• user experience makes it easy to work with SQL and version control
• customer success team and the dbt (data build tool) community help establish best practices
• thorough and clear documentation

• 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.

• Slow load times of the dbt cloud environment (they're working on it via a new UI though)
• More out-of-the-box solutions for managing procedures, functions, etc would be nice to have, but honestly, it's pretty easy to figure out how to adapt dbt macros

Incentivized

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

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.

Most ETL pipeline products have a T layer, but dbt just does it better. The transformation is on steroids compared to the others. Also, just allows much more Adhoc solutions for very specific projects. Those ETL tools are probably better on the T part if you don't need too many transforms - also dbt is pretty much free dependent on how you work it, also extremely scalable.

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.

• Simplified our BI layer for faster load times
• Increased the quality of data reaching our end users
• Makes complex transformations manageable

Incentivized