Altair Monarch (formerly Datawatch Monarch, acquired by Altair in December, 2018) works with both relational and multi-structured data including support for a wide range of formats including PDF, XML, HTML, text, spool and ASCII files. The product can access data from invoices, sales reports, balance sheets, customer lists, inventory, logs and more. According to the vendor, the system is easy to use, allowing users to quickly select any data source and automatically convert it into…
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AWS Glue
Score 7.5 out of 10
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AWS Glue is a managed extract, transform, and load (ETL) service designed to make it easy for customers to prepare and load data for analytics. With it, users can create and run an ETL job in the AWS Management Console. Users point AWS Glue to data stored on AWS, and AWS Glue discovers data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, data is immediately searchable, queryable, and available for ETL.
* Individual seat licenses are very expensive, which is one reason we are moving to CMOD/RMS. But RMS has less functionality than standalone Monarch (now known as "Modeler"). I would like to know what improvements we can expect in RMS, I would also ask, what is the future of the standalone version? * In the past there has been a dearth of user discussion and support in the online community, although this seems to be improving with the new "Datawatch Commmunity" (http://community.datawatch.com).
When the data which requires ETL has different formats, schema, and volume, this service suits them best. So, when the volume is not consistent (typical use-case of healthcare and online shopping), AWS Glue can be the prime choice. When the data is available in both batch and streaming mode, the developer needs to generate a separate codebase. This increases the source code management efforts. So, prefer to go with Glue when the nature of the data is the same (either batched or streamed).
After data cleansing, the team also implemented the best practices for using AWS platform services as a Data Lake, such as job bookmarking for AWS Glue jobs, proper delimiter for the AWS Glue crawlers, partitioning in AWS S3, and transformation to parquet file for compression and faster querying time in Amazon Athena.
Data modernization through combining data from multiple sources into a functioning datasets, rebuilding DW, and resctructuring data sources.
Aims to lessen customer complaints, eliminate manual data extraction requests via SR from different data sources, and Increase accuracy, consistency and speed up reconciliation process.
Setting up visualizations with time series data requires a good understanding of how the software works. I would like it to be more intuitive. Having said that, time series data is inherently complicated and I don't see any obvious ways to make it simpler. But I'm not a software designer myself; they could put more resources into the user experience.
Their video training is really helpful and they have a big library of videos, but the videos get out of date as they come out with new versions. I can imagine that it's difficult to keep all the videos updated, but it would be great if the videos were always using the latest major version of the product.
They need more visualizations. They have a pretty big collection now but it seems like there is often some other way to present and visually analyze data that would be a better/tighter fit with requirements than the visualizations available in the standard product. I understand it is possible to add more visualizations - custom visualizations - but that's beyond my expertise.
Datawatch recently repositioned Data Pump and essentially priced us out of the market. The initial investment was very inexpensive, but the yearly maintenance contract was viewed as being a little pricey. The only value of the contract was that it included software upgrades. The Professional Services portion of the contract that was meant to provide support was not viewed as being very effective or beneficial.
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.
Datawatch is very good value of money compared to QlikView; QlikView is really more of a BI tool and has a lot of functions that I didn't need. Datawatch is very strong in the real-time area where Tableau, Panorama, and Qlik don't do very well. If you need to set up a visual monitoring dashboard, Datawatch is the best product I've seen for that. if you want to do a lot of in depth statistical analysis of large databases, Tableau is probably a good option.
The cataloging of data objects is the best in the case of AWS Glue. We use AWS Glue in all of our data pipelines to sync external and internal data sources and to automatically produce SQL-based ETL based on AWS Glue catalog objects. Integration with Amazon products is the other advantage.
