dbt-timescaledb 1.7.0a2

The TimescaleDB adapter plugin for dbt

dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

dbt is the T in ELT. Organize, cleanse, denormalize, filter, rename, and pre-aggregate the raw data in your warehouse so that it's ready for analysis.

TimescaleDB

Timescale extends PostgreSQL for all of your resource-intensive production workloads, so you can build faster, scale further, and stay under budget.

dbt-timescaledb features

Hypertables

You can materialize your models as hypertables. The hypertable materialization requires you to set a time_column_name configuration option in your models. This will create a hypertable in TimescaleDB.

{{
  config(
    materialized='hypertable',
    time_column_name='time_column'
  )
}}
select current_timestamp as time_column

Continuous aggregates

There is support for a continuous_aggregate materialization. This materialization will create a continuous aggregate in TimescaleDB.

{{
  config(
    materialized='continuous_aggregate',
  )
}}
select
    count(*),
    time_bucket(interval '1 day', time_column) as bucket
from {{ ref('a_hypertable') }}
group by 2

More

Feel free to request things you're interested in by creating an issue.

The following things are planned:

• Basic hypertable support
• Basic continuous aggregate support
• Configure continuous aggregate policies through dbt

Join the dbt Community

• Be part of the conversation in the dbt Community Slack
• Read more on the dbt Community Discourse

Reporting bugs and contributing code

• Want to report a bug or request a feature? Let us know on Slack, or open an issue
• Want to help us build dbt? Check out the Contributing Guide

Code of Conduct

Everyone interacting in the dbt project's codebases, issue trackers, chat rooms, and mailing lists is expected to follow the dbt Code of Conduct.