Ascend.io announced an integration
that allows users to painlessly scale and run sophisticated dbt models
in production with a single command. Ascend for dbt represents the
industry's first automation and optimization controller for dbt models,
giving data and analytics engineers access to Ascend's advanced
orchestration capabilities with no added overhead.
Ascend for dbt allows analytics engineers to continue to build with
dbt Core while deploying their models to Ascend for intelligent
execution. This solves a major challenge with dbt Core, the open-source
version of dbt, which requires users to bring in a separate
orchestration tool to execute their models. By automating the process of
deploying dbt models with DataAwareTM intelligence, Ascend helps data
teams deliver data products faster and more efficiently.
"With data teams under pressure to innovate faster and generate more
value for their organizations, they need systems to automate
traditionally manual processes that deploy and maintain data products at
scale," said Sean Knapp, founder and
CEO, Ascend.io. "This new integration eliminates the pain of
operationalizing dbt models, providing a smooth journey from design to
production."
With Ascend for dbt, Ascend automatically tracks lineage across
multiple dbt projects. By profiling the data against the generated dbt
code, Ascend's automation controller identifies every change in code or
data that affects a pipeline. This enables Ascend to auto-generate the
jobs required to operate a sophisticated network of pipelines with
maximum efficiency. Its dynamic intelligence limits unnecessary data
processing, reducing cloud bills by up to 30% and engineering operations
work by up to 80%.
Ascend's data pipeline automation allows engineers to continue to
build ontop of dbt Core. Once the updated models are compiled and pushed
into Ascend, the platform automatically identifies any change from
previous versions and autonomously orchestrates pipeline operation in
response to those changes.
"In addition to the cost and time saving benefits, we see this
integration as a critical step in organizations' AI readiness strategy,"
said Knapp. "AI projects often require entirely new datasets at a speed
most data engineering teams are currently not equipped to meet. By
launching dbt pipelines in a fraction of the time it traditionally
takes, data teams can create and manage new data products at scale and
ultimately become more efficient and responsive."