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Y42 vs dbt Cloud

A head-to-head comparison between Y42, a data orchestration platform and dbt Cloud, a point solution for data transformations.

Futuristic landscape

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.

Ingestion
Ingestion
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.

Ingestion
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.

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Transformation
Transformation
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.

Transformation
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.

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Orchestration
Orchestration
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.

Orchestration
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.

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"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."

Max Pelz
Max PelzBusiness Intelligence LeadKranus Health
Futuristic landscape

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.

Health Monitoring
Health Monitoring
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.

Health Monitoring
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.

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Debugging
Debugging
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.

Debugging
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.

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Y42 - trusted by data teams across the planet

Futuristic landscape

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.

Environment management
Environment management
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.

Environment management
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.

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Data Quality Assurance
Data Quality Assurance
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.

Data Quality Assurance
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.

vs

"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"

Pierre Zaplet-Brouillard
Pierre Zaplet-BrouillardData & Analytics LeadZigzag App
Continuous integration
Continuous integration
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.

Continuous integration
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.

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Continuous deployment
Continuous deployment
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.

Continuous deployment
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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dbt Cloud
Build data pipelines
Ingestion sources
Data transformation
Run Python scriptsTransformation only
End-to-end orchestrationLimited
Data testing
Enhanced web IDE
Monitor data pipelines
Centralized asset monitoring
View historical data
Asset-level build history
Inspect failed rowsLimited
Stale dependencies detection
Anomaly detection (beta)
Make changes with confidence
Development environmentsLimited to 1
Pull requests
DataDiffs to compare data changes
Continuous integration
Continuous deploymentCustom setup required
Instant rollbacksRevert code and dataRevert code only
Build data pipelines
Ingestion sources
Data transformation
Run Python scripts
End-to-end orchestration
Data testing
Enhanced web IDE
Monitor data pipelines
Centralized asset monitoring
View historical data
Asset-level build history
Inspect failed rows
Stale dependencies detection
Anomaly detection (beta)
Make changes with confidence
Development environments
Pull requests
DataDiffs to compare data changes
Continuous integration
Continuous deployment
Instant rollbacksRevert code and data