Nov 16, 2021
Why Elementl and Dagster: The Decade of Data
Announcing our $14M Series A led by Mike Volpi of Index Ventures, alongside Sequoia Capital, Slow Ventures, Coatue, Amplify Partners, OSS Capital, and other leading investors.
Nov 8, 2021
New in Dagster 0.13.0: Logging Improvements!
Logging without context, instance-wide handlers, capturing python logs, and more! Learn about the improvements we've made to Dagster logging since 0.12.0.
- Owen Kephart
Oct 28, 2021
Dagster 0.13.0: A New Foundation
We’re proud to announce 0.13.0 of Dagster. We’ve made dramatic improvements to our core APIs, completely revamped our UI, and brought renewed clarity to our mission.
Aug 10, 2021
Community Memo: Moving Dagster's Core APIs Towards 1.0
As data practitioners increasingly depend on Dagster as a critical piece of their platforms, we believe it’s important to commit to a stable set of APIs that they can expect to remain the same for a very long time.
Jul 19, 2021
Dagster 0.12.0: Into the Groove
In 0.12.0, we introduce pipeline failure sensors, solid-level retries, and more convenient testing APIs.
- Owen Kephart
May 25, 2021
Community Memo: Approachability Improvements
In the last two months, we've made a set of changes aimed at making Dagster more approachable: to smooth out its learning curve and reduce its boilerplate.
May 18, 2021
Incrementally Adopting Dagster at Mapbox
At Mapbox, we've adopted Dagster without breaking compatibility with our legacy Airflow systems -- and with huge gains to developer productivity.
- Ben Pleasanton
May 13, 2021
Moving past Airflow: Why Dagster is the next-generation data orchestrator
Our most requested piece of content is a comparison between Dagster and Airflow. Here we detail the differences between the two systems, and make the case for choosing Dagster.
Apr 1, 2021
Dagster 0.11.0: Lucky Star
In 0.11.0, we introduce dynamic orchestration, a new backfill UI, and support for tracking asset lineage.
Mar 15, 2021
Building shared spaces for data teams at Drizly
Dagster lets a small data infrastructure team efficiently ship a data platform that supports users with different skillsets, letting anyone contribute with minimal coordination required.
- Dennis Hume
Dec 9, 2020
Good Data at Good Eggs: Using Dagster to manage the data platform
Running pipelines is only part of the operational burden of running a data platform. We also need to manage the platform itself and control associated technical debt. We found that Dagster was a very natural place to do that work, with the advantage that our entire operational view of the platform is consolidated in a single tool.
Nov 5, 2020
Good Data at Good Eggs: Data observability with the asset catalog
What we’re aiming for with Dagster is a completely horizontal view of our data assets. Our analysts will be able to look up when a raw data ingest from Stitch occurred, when a dbt model ran, or when a plot was generated by a Jupyter notebook and posted in Slack, through a single portal — a single "pane of glass."
Oct 1, 2020
Good Data at Good Eggs: Correctness and reliability for data infrastructure
Dagster’s support for custom data types helped us achieve better correctness and reliability in our data ingest process, which meant less downstream breakage and better recovery when bad data made it through.
Oct 1, 2020
Good Data at Good Eggs: Part 1 of 4
Adopting Dagster was a catalyst for the transformation of our data platform team. We hope our experience is encouraging to other teams facing similar challenges and opportunities.
Sep 16, 2020
Testing and Deploying PySpark Jobs with Dagster
Spark has a beautiful API — so why is developing with it such a pain?
Sep 15, 2020
Dagster Community Update: September 2020
Our monthly community meeting featured a retrospective of our 0.9.0 release, a preview of our 0.10.0 roadmap, and Prezi's journey from a homegrown orchestration solution to Dagster.
Sep 10, 2020
Great Expectations for Dagster
We’re thrilled to announce a new integration between Dagster and a fellow open-source project, Great Expectations (GE).
- Leor Fishman
Aug 11, 2020
Dagster: The Data Orchestrator
Dagster is a new type of workflow engine: a data orchestrator. Moving beyond just managing the ordering and physical execution of data computations, Dagster introduces a new primitive: a data-aware, typed, self-describing, logical orchestration graph.
Feb 26, 2020
Dagster 0.7.0: Waiting To Exhale
With 0.7.0 we set out improve the Dagster experience with large, production-scale pipelines, deployable to Kubernetes.
Oct 10, 2019
Dagster 0.6.0: Impossible Princess
With 0.6.0, Dagster comes “batteries-included” — but still with pluggable options — for everything you need to execute, monitor, schedule, deploy, and debug your data applications.
Jul 8, 2019
Today the team at Elementl is proud to announce an early release of Dagster, an open-source library for building systems like ETL processes and ML pipelines. We believe they are, in reality, a single class of software system. We call them data applications.