Understanding dbt Artifacts: What They Are and How to Use Them

When nearly half of data leaders report their pipelines fail 11 to 25 times due to data quality issues (Acceldata, 2023), it is evident that poor visibility is a business risk rather than just an inconvenience. As modern analytics workflows span hundreds of interconnected models, transformations, and sources, the need for transparency has never been greater.

This is where dbt artifacts come in. These structured, metadata-rich files—like manifest.json, run_results.json, and catalog.json—offer more than just technical documentation. By facilitating automated testing, dynamic documentation, lineage tracing, and CI/CD enforcement, they act as the data pipeline’s nervous system.

This blog unpacks the anatomy of dbt artifacts, examines their role in observability and governance, and highlights how data teams are using them to build scalable, auditable, and resilient analytics workflows.

What Are dbt Artifacts?

In dbt, artifacts are metadata-rich JSON files automatically generated after each dbt run, test, or build operation. By providing a comprehensive overview of your project’s structure, results, and history, these files enable teams to track, record, and improve their data workflows.

The most essential artifacts include:

• manifest.json – captures sources, macros, dependencies, model configurations, and the entire Directed Acyclic Graph (DAG).
• run_results.json – includes each model or test’s execution status, performance statistics, and errors.
• catalog.json – Provides column-level metadata like types, descriptions, and statistics.

These metadata artifacts serve as the fundamental elements of the dbt metadata system, which enables integration with observability tools as well as automated documentation and governance improvements. Organizations need to understand and utilize these artifacts to construct scalable along with auditable, and transparent analytics pipelines within their contemporary data infrastructure.

Why Are dbt Artifacts Important?

1. Model Lineage & Transparency

dbt artifacts like manifest.json map your entire DAG (Directed Acyclic Graph), making model relationships, dependencies, and data lineage crystal clear. Analysis workflows benefit from the transparency, which makes trust and auditing possible.

2. Debugging & CI/CD Efficiency

The execution metadata for models exists within run_results.json documents along with status information and runtime duration. CI/CD pipelines require these artifacts for debugging and continuous quality checks, so broken models cannot be deployed to production.

3. Documentation & Impact Analysis

The use of artifacts permits both documentation changes and analysis of change impacts. The model documentation and lineage visualization features on dbt Cloud and integrations operating with DataHub, Atlan, and Monte Carlo depend on these artifacts for their function.

4. Governance & Collaboration

The exposure of metadata through dbt artifacts strengthens team collaboration as well as version control while ensuring compliance, which are core elements for enterprise-grade governance.

The metadata backbone exists within dbt artifacts, which converts your data pipeline into a system that remains transparent while becoming manageable and scalable.

Core dbt Artifacts and Their Functions

During execution, dbt generates necessary artifacts that enhance both transparency and automation, and observability. The essential artifacts produced by a dbt operation include the following breakdown:

1. manifest.json – Your Metadata Blueprint

This artifact serves as the most detailed representation since it includes a full depiction of your project DAG. Along with tests and macros, it offers comprehensive details on models and sources and illustrates how they are related. The metadata file serves as an input for visualization tools found in dbt Cloud as well as external platforms, including DataHub and Amundsen.

2. run_results.json – Run Status & Diagnostics

This file documents the execution results of dbt commands and displays model executions with their status and timing alongside test outcomes. The file is essential for CI/CD pipeline automation, as well as testing automation and model performance tracking systems.

3. catalog.json – Schema Metadata Snapshot

catalog.json contains database schema metadata, which includes column names together with types and descriptions. The document serves as a crucial reference when displaying field metadata through documentation tools for achieving uniformity in the dataset.

4. sources.json – Source Freshness Reporting

dbt generates an artifact when running the source freshness command, which contains references to both freshness conditions and upstream source status updates. Data quality monitoring depends heavily on this tool for detection and warning purposes.

5. compile/ & run/ Folders – SQL Output

The SQL queries that dbt creates lead to the formation of two separate folders named compile/ and run/. Within the compile/ folder, Jinja templates appear processed into SQL code for display, while the run/ folder presents these SQL codes before running them. The generated SQL queries that appear in these folders assist both debugging processes and performance optimization.

dbt Core vs dbt Cloud

The comparison of dbt Core vs dbt Cloud highlights how the former offers customizable workflows, while the latter streamlines artifact lifecycle management

Feature	dbt Core	dbt Cloud
Artifact Location	Local target/ directory	Managed in dbt Cloud UI/API
Accessibility	Manual file access	Available via web UI and API
Automation Support	Requires custom scripting	Built-in with scheduler and webhooks
Governance Integration	Manual or community tools	Direct API access for integrations
Visualization	Requires dbt-docs or third-party	Native Explorer & lineage viewer
CI/CD Integration	Manual setup with CLI	Seamless GitHub/GitLab integration
Storage & Retention	User-managed	Automatically retained by dbt Cloud
Requires dbt-docs or a third-party	Not natively supported	Full access via dbt Cloud API

Visualizing dbt Artifacts

1. From Artifacts to Interactive Graphs,

dbt artifacts aren’t just metadata—they’re the foundation of powerful visualizations. Tools like dbt Cloud and dbt Explorer convert manifest.json and catalog.json into interactive Directed Acyclic Graphs (DAGs). These allow users to explore model dependencies, test results, and freshness in a single pane.

2. Static Docs with Rich Metadata

Open-source dbt-docs tool uses the same artifacts to automatically create static documentation, which includes lineage diagrams together with information about descriptions and column metadata. This makes understanding the pipeline easier for both engineers and business stakeholders.

3. Accelerated Impact Analysis

These metadata-driven UIs allow teams to visually trace data flows, identify upstream/downstream dependencies, and debug issues without digging into SQL files. Visualizations improve pipeline transparency and speed up impact analysis, making them critical for collaboration and governance.

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How dbt Artifacts Power Automation

Triggering Automation from Artifacts

dbt artifacts, such as run_results.json and manifest.json, are essential triggers for automating data workflows. JSON files provide detailed information about models and tests, and executions, so orchestration tools can develop actions from pipeline execution outcomes.

Driving CI/CD Pipelines

In modern DevOps environments, artifacts are integrated with CI/CD tools like GitHub Actions, GitLab CI, and CircleCI. For instance, a CI pipeline can be configured to automatically deploy only if all dbt tests pass, as indicated in run_results.json.

Example Use Case

A GitHub Action might parse run_results.json to check for test failures before proceeding with production deployment:

The pipeline system will suspend operation when it identifies any failed status to protect the deployment of verified data transformations. The automation system minimizes errors made by humans while simultaneously building analytical delivery trust among users.

dbt Artifacts and Data Governance

Governance Area	How dbt Artifacts Support It
Auditability	run_results.json records model execution outcomes and test results to generate auditable logs.
Data Lineage	manifest.json maps relationships across models, macros, and sources—essential for lineage tools.
Schema Visibility	catalog.json exposes detailed schema info, helping governance platforms classify data assets.
Compliance Monitoring	Snapshots and source metadata aid in tracking historical changes and source freshness for audits.

The data artifacts become simple to manage on large scales when they enter cataloging and governance systems, including Atlan, Collibra, or Alation. The visualization of lineage from manifest.json functions in these platforms thus diminish the need for manual documentation.

Challenges with dbt Artifacts

Projects at scale and with diverse tools face challenges when working with dbt artifacts because they deliver great visibility but also bring specific operational challenges. Analyzing the areas where issues arise plays an essential role in protecting artifact functionality and operational effectiveness.

Challenge	Description	Mitigation Strategy
File Size & Storage Overhead	Artifacts like manifest.json can grow large in complex DAGs, affecting loading and parsing.	Use selective model inclusion or state comparison to reduce output.
Version Compatibility	dbt artifacts may change schema across dbt versions, breaking integrations with external tools.	Pin dbt versions in production and use schema validators.
Versioning in External Systems	External tools may rely on outdated artifact formats or inconsistent updates.	Maintain artifact schema documentation and CI checks.
Parsing Performance	Parsing large JSON files can slow CI/CD or documentation generation pipelines.	Leverage tools with incremental metadata parsing support.

Addressing technical challenges during the first stage allows teams to create dbt workflows that are both strong and sustainable and ready for analytics expansion.

Best Practices for Managing dbt Artifacts

Control of dbt artifacts establishes the foundation for scalable data operations, particularly in enterprise environments, because reliability, security, and performance represent critical priorities. The following practices represent guidelines to maintain stable and governed artifacts.

1. Implement Artifact Retention Policies

Storage bloat and repetitive file duplicates can be managed through policies dictating artifact retention periods. The storage should retain only fresh versions or archived snapshots that serve auditing or CI/CD rollback requirements. Utilize cloud lifecycle rules and scripting capabilities to automate storage cleanup functions.

2. Store Artifacts Securely

Using encryption on object storage solutions like AWS S3 or GCP Cloud Storage will protect artifacts or saved files under version control system operations through Git. Access control systems need to be implemented properly when storing artifacts that support governance operations.

3. Automate Parsing and Visualization

Automate data parsing from manifest.json and run_results.json through dbt-docs or custom Python parsers. These tools enable real-time viewing of lineage data alongside status dashboard displays.

4. Leverage dbt Cloud APIs and Community Tools

Programming access to metadata occurs through the APIs that dbt Cloud implements. By using community tools together with dbt-meta-testing or dbt-artifacts-parser, organizations can automate validation procedures and CI/CD, and governance workflow automation.

By following these practices, teams can ensure their dbt artifact strategy remains clean, secure, and integrated into broader data operations.

Use Cases of dbt Artifacts in Real-World Workflows

1. QA & Testing Automation

The dbt artifacts ensure continuous testing capability through change detection, which identifies dependency-breaking behaviors. The CI/CD pipeline enables automatic failure of pull requests when tests break or models experience damaging changes to stop improper data from reaching the production environment.

2. Dynamic Documentation

Artifacts manifest.json and catalog.json enable automatic generation of lineage graphs together with model descriptions and column metadata information. The documentation stays current when models evolve through the integration of dbt-docs.

3. Impact Analysis & Debugging

The visual representation of model dependencies in dbt artifacts enables analysts and engineers to instantly identify impacted models and dashboards, leading to minimized data-related errors.

Conclusion

Data engineering visibility represents the basic requirement for building trust. The information stored in dbt artifacts defines the vital components of analytics system functionality. The artifacts enable pipeline development by streamlining both document creation and testing processes. They support lineage map creation and ensure all tasks follow defined standards in automated testing sequences.

Mastering how to understand and utilize dbt artifacts provides strategic advantages since data stacks continue to scale and enforce governance requirements. Teams that master these artifacts construct data systems that provide high resilience, together with transparency, alongside intelligence for seamless scaling and decreased breakage.

Take your observability and data governance efforts to the next level by integrating dbt artifacts with a robust data platform like Hevo. As an end-to-end data pipeline solution, Hevo offers native support for dbt Core through Hevo Transformer. Whether you’re working with run results, catalog metadata, or lineage graphs, Hevo bridges the gap between metadata and action by automating and operationalizing your data workflows.

Master the metadata. Automate the governance. Trust your pipelines.

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