dbt vs. Progress DataDirect

dbt (Data Build Tool) is best suited for doing the data transformation. dbt is just a transformation tool and it is not suitable for building a data pipeline which requires extraction of data and loading. dbt is well suited for SQL based transformation logic and it is less appropriate when transformation logic requires python.

Incentivized

Data ingestion and connectivity to CRM applications made easy by Progress data direct JDBC interface. The interface integrates well with Spark and AWS Glue etc. May not be a great fit for ingestion of very large objects from systems like Salesforce.

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

dbt is very easy to use. Basically if you can write SQL, you will be able to use dbt to get what you need done. Of course more advanced users with more technical skills can do more things.

Incentivized

Matillion is graphical versus dbt, which is SQL code-based (that, of course, is a matter of personal preference and not an objective advantage). The integrated testing, documentation generation, lineage, etc., were additional criteria that led us to choose dbt.

Incentivized

Ended hybrid Data Pipeline info your app, white label it and ship it to your customers to give them the very best cloud connectivity. Has an encryption algorithm that can be made compliant using the FIPS Compliance mode. Get real-time connectivity to popular data sources such as Salesforce, Google Analytics, and Oracle.

Incentivized

• In 3 months we re-wrote the data warehouse (15-20 sources) in dbt with 3 developers.
• We are using it continually for the past year with no issues.
• Sorry, I don't have ROI numbers but the impact was huge.

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