You probably have heard of the words “Modern Data Stack.” There is a nice article that explains in great detail the idea, and I highly recommend reading it. Long story short, the last couple of years brought us a set of great tools that made it much easier and cheaper for a company to build a Data Analysis process. And one of the directions of further modern data stack evolution is making data processing real-time or getting close to it.
Jitsu is proud to be a part of the modern data stack. And we put much effort into delivering data to your destinations as fast as possible by supporting streaming events into warehouses and reducing batch processing intervals to just a few minutes.
Today, we are announcing integration with dbt – a feature that allows you to reduce further the time it takes for data models to react to changes in a data flow. dbt – leading open-source data transformation platform and dbt Cloud is a cloud deployment of dbt tools with a friendly UI with IDE features like support for integrated documentation, version control, CI/DC, and API.
The integration consist of two parts:
- Jitsu can trigger a dbt Cloud job after each Source synchronization or Destination batch run
- Dbt package that makes it easier to build DBT models from Jitsu data
Triggering dbt Cloud Job#
Now you can set up dbt Cloud job settings in Jitsu UI. When configured and enabled – each successful run of Source sync task or Destination in batch mode will trigger a dbt Cloud Job run.
Jitsu dbt hub package#
We have released our first package for dbt hub: dbt-jitsu ts initial version implements the ‘Sessionization’ feature for Jitsu page views data similarly to dbt’s package for Segment.
Sessionization – is the process of aggregation of page view data into user sessions. The result is a table with data like session duration, page views per session, referral source, and a total number of sessions per user – in other words, data required for proper user analysis.
A nice addition if you already process Jitsu data with dbt or just a good reason to start.
We are going to expand our packages functions in the future by adding support for more kinds of source data, e.g., mobile application usage data and adding more data transformations for typical analytics demands.
Modern Data Stack with Jitsu and dbt#
Bringing both parts together that is how Modern Data Stack may look like with Jitsu and dbt integrated:
In the most simple case, Jitsu allows getting new page views sessions data with retroactive user recognition in just a few minutes.
But the fact that dbt Cloud integrated with Reverse ETL tools such as Hightouch and Census allows shortening data traveling time from an origin to the tools that bring value to your product.
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