Automating Metadata Ingestion into Amundsen and DataHub

Overview

Automating metadata ingestion into Amundsen or DataHub centralizes data knowledge, increases catalog freshness, and removes human toil by scheduling repeatable extraction jobs from source systems into modern data catalogs.

Why Automate Metadata Ingestion?

Data catalogs live or die by the completeness and freshness of their metadata. Manual population does not scale when you manage hundreds of databases, thousands of tables, or rapidly-evolving ETL pipelines. Automation delivers:

• Consistency – standardized extractors guarantee the same attributes across sources.
• Freshness – scheduled pipelines (e.g., via Airflow or Dagster) keep lineage and statistics up-to-date.
• Governance – automated tagging captures compliance classifications at write-time.
• Developer Velocity – engineers discover the latest schemas without digging through code or dashboards.

Core Components

1. Extractors

Connect to data sources (e.g., Snowflake, BigQuery, Kafka, dbt) and read catalog data, usage metrics, or lineage. Both Amundsen and DataHub ship with dozens of built-in extractors.

2. Transformers

Optional modules enrich raw metadata—adding owners, cost centers, PII tags, or glossary terms.

3. Sinks

Write the canonical metadata model into the target catalog service (Neo4j/Elasticsearch for Amundsen; Kafka or Graph Service for DataHub).

4. Orchestrator

An external scheduler (Apache Airflow, Dagster, Prefect, GitHub Actions, etc.) coordinates extraction frequency, manages credentials, and handles retries.

Implementation Patterns

Pattern 1 – Airflow-Based Pipelines

This is the most common approach in production because both catalogs provide first-class Airflow operators.

1. Create a Python virtual environment with pip install amundsen-databuilder or pip install acryl-datahub[datahub-rest].
2. Author DAGs that instantiate extractor, transformer, and sink classes.
3. Package credentials (service user or OAuth token) via Airflow Connections or AWS/GCP Secrets Manager.
4. Schedule DAGs nightly or on commit events (for dbt).

Pattern 2 – GitOps with YAML Recipes (DataHub)

DataHub supports declarative ingestion recipes. Teams commit YAML into a version-controlled repo, and a CI runner executes datahub ingest -c recipe.yml. Merges to main trigger ingestion, ensuring infra as code.

Pattern 3 – Streaming Lineage (DataHub Kafka)

For real-time systems, emit MetadataChangeEvent (MCE) protobuf messages directly into the DataHub Kafka topic, achieving sub-minute freshness.

Step-by-Step Example: Automating Snowflake & dbt Metadata into Amundsen

1. Prerequisites – Amundsen deployed (Neo4j+Elastic), Snowflake service account, dbt manifest files in S3.
2. Install Databuilder – pip install amundsen-databuilder snowflake-connector-python
3. Snowflake Table Extractor – Use SnowflakeMetadataExtractor to pull columns, types, and comments.
4. dbt Lineage Extractor – DbtExtractor parses manifest.json for upstream/downstream nodes.
5. Loaders – Neo4jCsvPublisher sends nodes/relationships to Neo4j; FsNeo4jCSVLoader stages the CSVs.
6. Airflow DAG – orchestrates both jobs, then triggers an Elastic index update.

Best Practices

• Isolate ingestion in dedicated containers or Kubernetes Jobs for reproducibility.
• Propagate env tags (dev, prod) to support multi-tenant catalogs.
• Version control extractor configs and review via pull requests.
• Monitor ingestion success metrics and alert on stale datasets.
• Backfill historical usage before enabling incremental jobs.

Galaxy and Metadata Automation

While Galaxy focuses on interactive SQL editing, it becomes the producer of valuable query metadata. By exporting Galaxy’s endorsed query collections (via its API) into Amundsen or DataHub, teams surface trusted analytical logic directly in their catalog. A lightweight Airflow DAG can call Galaxy APIs nightly, transform query text into the appropriate metadata model (Dashboard/Query entity), and publish it alongside database schemas—giving engineers a single pane of glass for both tables and canonical SQL.

Common Mistakes and How to Fix Them

1. Over-Pulling Metadata

Why it’s wrong: Extracting every temp table or staging schema floods your catalog.
Fix: Use extractor allowlists/regex to limit to production schemas.

2. Ignoring Incremental Load

Why it’s wrong: Full reloads strain databases and increase Neo4j/Kafka churn.
Fix: Configure stateful ingestion (DataHub) or watermark filters (Amundsen).

3. Hard-Coding Credentials

Why it’s wrong: Secrets leak in Git and violate compliance.
Fix: Store credentials in Vault/Secrets Manager and reference via environment variables.

Conclusion

Automated metadata ingestion transforms Amundsen or DataHub from static glossaries into living, searchable maps of your data estate. By combining out-of-the-box extractors with a robust orchestrator, you ensure that tables, dashboards, and even Galaxy queries stay discoverable and trustworthy—fueling self-service analytics and data governance at scale.