dbt vs. SAS Data Management

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

When data is in a system that needs a complex transformation to be usable for an average user. Such tasks as data residing in systems that have very different connection speeds. It can be integrated and used together after passing through the SAS Data Integration Studio removing timing issues from the users' worries. A part that is perhaps less appropriate is getting users who are not familiar with the source data to set up the load processes.

Incentivized

• 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

• SAS/Access is great for manipulating large and complex databases.
• SAS/Access makes it easy to format reports and graphics from your data.
• Data Management and data storage using the Hadoop environment in SAS/Access allows for rapid analysis and simple programming language for all your data needs.

Incentivized

• 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

• Requires third-party drivers to connect to common data sources like SFDC, MS SQL, Postgres.
• Debugging errors from the logs is a complicated process.
• E-mail alert system is very primitive and needs customization to make it more modern,
• Cannot send SMS alerts for jobs.

Incentivized

We are happy with the software and its functionality. As a SAS-shop, DataFlux is a logical choice for complex data integration.

The main negative point is the use of a non-standard language for customizations, as well as the poor integration with non-SAS systems. However, there is no doubt that it is a high-performance and powerful product capable of responding optimally to certain requirements.

Incentivized

It worked as expected.

Incentivized

With SAS, you pay a license fee annually to use this product. Support is incredible. You get what you pay for, whether it's SAS forums on the SAS support site, technical support tickets via email or phone calls, or example documentation. It's not open source. It's documented thoroughly, and it works.

Incentivized

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

Because of ease of using SAS DI and data processing speed. There were lots of issues with AWS Redshift on cloud environment in terms of making connections with the data sources and while fetching the data we need to write complex queries.

Incentivized

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

Incentivized

• We have more users who can connect to the many different data sources.
• Our users do have existing SAS programming knowledge and that can carry over.
• Business functions are starting to rely on SAS Data Integration Studio work product shortly after introduction.

Incentivized