dbt vs. Progress DataDirect

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

Hybrid Data Pipeline lets users consume or share data in can timely and compliant manner regardless of the application they use or the location of the data. It has defined a stringent set of policies and practices around product development and distribution. Enables customers to consume their data in your application using their BI tool of choice.

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

• Providing JDBC connectivity to CRM applications
• Raw data integration
• Customer support

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

• Seamless data access for BI and Analytics.
• Helps you comply with industry standards.
• Cloud and On-premises Data Access with Hybrid Data Pipelines.

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

Easy to use with JDBC interface, detailed debug logs for troubleshooting, and easier audit with detailed log. Cost of ingestion is relatively cheaper

Incentivized

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

Incentivized

• Create custom data access and security policies at a user level.
• Integrate HDP into your app seamlessly with management and administration API's.
• Hybrid Data Pipeline service can be deployed on multiple modes behind a load balancer.

Incentivized