Y42 vs Airflow
A head-to-head comparison between Y42, a turnkey data orchestration platform and Apache Airflow, a general-purpose orchestrator.
Build data pipelines that are easy to maintain
Y42's standardized configuration schema lets you ingest, transform, test and automate data flows on a unified architecture — so every component in your data pipelines work together seamlessly.
Dive into data, not infrastructure
All you need is a data warehouse to start using Y42. From setup to scaling, we've got infrastructure covered — so you can focus on shipping high-quality pipelines for your business.
Manage resource-intensive infrastructure
Airflow requires a web server, scheduler, metadata database, triggerer, and workers. In production, it's advisable to scale horizontally using Kubernetes or Docker Swarm.
Built-in ingestion capabilities
Leverage ready-to-use Y42 sources (powered by CData), Airbyte, Fivetran or Python scripts to ingest data. Just declare your source, we'll handle the infrastructure and execution.
Maintain a patchwork of tools
As a standalone orchestrator, Airflow needs external tool integrations to ingest data. While you can roll your own script, running them in Airflow is not ideal due to memory limitations.
Native compatibility with dbt
Y42 natively integrates dbt Core, so you can create dbt models, macros, tests and more, within your Y42 space. You can also import an existing dbt project to get started.
Manually integrate dbt with Airflow
Split dbt models into Airflow tasks or run them with Kubernetes. Both methods require significant effort and forces a compromise between observability and prolonged run times.
Y42 - trusted by data teams across the planet
Asset-based orchestration
Whether it's sources, models or Python scripts, Y42's asset-based orchestrator lets you implicitly declare dependencies within any step in your pipelines with a standardized method.
Task-based orchestration
Airflow's wide range of operators enables explicit dependency management across various tools, but this complexity makes data pipelines harder to maintain as they grow.
"Y42 brings Gitlab, dbt, and Airbyte seamlessly into the mix, enabling us to build, deploy, and maintain our pipelines effortlessly. From integration to transformation, it's all done right within our data warehouse. Plus with the Git interface, our team started collaborating effectively right away."
Spot code and data errors from light years away
Get a bird's-eye overview of your data pipelines' health or zoom in for granular analysis. Y42's asset monitor is a telescope and microscope rolled into one.
Centralized asset health monitoring
Track the build status and freshness of each step in your data pipelines from a unified mission control center — freeing you from the clutter of extensive job logs.
Limited observability of asset health
Airflow displays the run history of your DAGs. However, since each task may encompass multiple pipeline steps, it offers limited insight into the health status of your data assets.
Never let bad data enter production
In the event of a data test failure, Y42 defaults to the asset's most recent successful build, guaranteeing that your production data remains trustworthy.
Discover bad data after they go live
When a data test fails, erroneous data has already been materialized in production. Preventing this issue requires cumbersome and costly CI/CD tooling.
Automatically detect data anomalies
Y42's anomaly detection flags unusual patterns in data volumes, freshness, schemas and dimensions — so you can detect issues early for timely intervention.
Set static assertions for your data
While rule-based tests can catch errors, they only work retrospectively and often miss nuanced issues that require manual fine-tuning to minimize false positives.
See exactly where and why an error occured
Y42 offers in-depth, asset-specific build logs that show you the exact steps leading to failures, enabling you to effortlessly pinpoint and isolate errors.
Search for the needle in your data stack
Since Airflow's runtime is separate from your pipeline steps' execution environment, errors are not always clearly propagated, making them harder to trace and understand.
Make changes with utmost confidence
By versioning both the code and data, Y42 evaluates the materialized impact of your changes before they go live — so you can iterate rapidly while ensuring unwavering reliability in production.
Streamlined branch-based environments
Y42's branch environments let you create isolated development or pre-production sandboxes with a single click, offering a safe and seamless way to make experimental changes.
Exploding complexity
Managing multiple environments with Airflow and other tools often leads to exploding complexity, requiring consistent yet isolated configurations for each tool's runtime.
"The way environments work with virtual data builds is reason enough to use Y42. When you test in a branch, materialize and then instantly merge the data back to main... it just feels like magic"
Zero-config CI/CD
Y42 lets you run automated CI checks out-of-the-box. Once changes have been tested and merged, their materialized state is instantly available in production.
Set up and maintain CI/CD tooling
Custom CI/CD setups adds significant maintenance overhead because you have to coordinate the execution of tasks, such as dbt models runs, within Airflow's environment.
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