Y42 vs dbt Cloud
A head-to-head comparison between Y42, a data orchestration platform and dbt Cloud, a point solution for data transformations.
Ingest, transform, test and automate on a unified architecture
Y42's asset-based orchestrator provides astandard configuration schema across all steps in your data pipelines. Whether it's ELT or data testing, all your pipeline components work together seamlessly.
Built-in ingestion capabilities
Ingest data using Y42 sources (powered by CData), Airbyte, Fivetran or Python scripts. All you need to do is declare a source — Y42 manages the execution and infrastructure.
Ingest data with third-party tools
Extract and load data using external ingestion tools or roll your own connectors, then use an orchestrator to keep multiple tools in sync.
Enhanced UI and web IDE
Create dbt models with a user-friendly UI that auto-generates YAML and SQL files or use a fully-fledged web IDE powered by the open source distribution of Visual Studio Code.
Limited web IDE features
Write dbt models in a web-based environment without quality-of-life features such inline asset previews and column-level lineage.
Orchestrate all steps
Y42's context-aware orchestrator lets you call upon Y42 sources (powered by CData), Airbyte, Fivetran, dbt models and Python scripts from anywhere in your project.
Lightweight model scheduler
Run models with cron-based or event-driven jobs. Requires an external orchestrator or API usage to synchronize model execution with ingestion or downstream processes.
"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 data pipeline 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
Check the build status and freshness of each step in your data pipelines from a centralized mission control center — no more sifting through numerous job logs.
View fragmented logs across multiple platforms
Retrieve job statuses from dbt Cloud, ingestion tools and an external orchestrator, then parse the run logs to identify failures along different stages of the data pipeline.
See exactly where and why a build failed
Y42's interactive asset-level build logs lets you pinpoint the exact steps leading to failures — so you can isolate errors before it compromises your data.
Search for the needle in your data haystack
Follow a process of elimination to find parsing, database or modeling errors. For example, run dbt debug, re-compile, then cross-reference run artifacts against the database logs.
Y42 - trusted by data teams across the planet
Make changes with utmost confidence
By versioning both the code and data, Y42 evaluates the impact of your changes before they go live — so you can iterate rapidly while ensuring unwavering reliability in production.
Isolated branch environments
Create isolated development sandboxes with a single click. With Y42's branch environments, you can experiment with changes without impacting your production pipelines.
Shared development environment
Each dbt Cloud project is limited to one development environment. With personal dev credentials, you can isolate code changes but they will still overwrite the target schema.
Never let bad data enter production
If a data test fails, Y42 automatically references the asset's last successful build so your production data pipelines always remain intact.
Discover bad data after they go live
If a data test has failed, then bad data has already been materialized in production. Requires custom CI/CD tooling to catch errors via staging environments to circumvent the issue.
"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
Get automated testing out-of-the-box. If your changes were validated in a development branch environment, they are automatically "pre-cleared" for approval.
CI environments
Configure a deployment environment to only run modified nodes and their children using CLI commands. dbt Cloud creates a new schema for each pull request created.
Automated deployments
Y42 references pre-built tables from existing branch environments. Once changes are tested and approved, the materialized state is instantly available in production.
Custom CI/CD pipeline via Git host
Set up a custom build pipeline to re-deploy production after each merge. Since it's managed by the external Git provider, you might run into conflicts with dbt Cloud's scheduler.
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