Astera ReportMiner vs. AWS Glue

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.

One of AWS Glue's most notable features that aid in the creation and transformation of data is its data catalog. Support, scheduling, and the automation of the data schema recognition make it superior to its competitors aside from that. It also integrates perfectly with other AWS tools. The main restriction may be integrated with systems outside of the AWS environment. It functions flawlessly with the current AWS services but not with other goods. Another potential restriction that comes to mind is that glue operates on a spark, which means the engineer needs to be conversant in the language.

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.

• It is extremely fast, easy, and self-intuitive. Though it is a suite of services, it requires pretty less time to get control over it.
• As it is a managed service, one need not take care of a lot of underlying details. The identification of data schema, code generation, customization, and orchestration of the different job components allows the developers to focus on the core business problem without worrying about infrastructure issues.
• It is a pay-as-you-go service. So, there is no need to provide any capacity in advance. So, it makes scheduling much easier.

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.

• In-Stream schema registries feature people can not use this more efficiently
• in Connections feature they can add more connectors as well
• The crucial problem with AWS Glue is that it only works with AWS.

Incentivized

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

Amazon responds in good time once the ticket has been generated but needs to generate tickets frequent because very few sample codes are available, and it's not cover all the scenarios.

Incentivized

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.

AWS Glue is a fully managed ETL service that automates many ETL tasks, making it easier to set AWS Glue simplifies ETL through a visual interface and automated code generation.

• 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.

• It had a positive impact on the way we build our data lake.
• It is the single source of truth for data structure (schemas/tables/views).

Incentivized