51 docs tagged with "metadata-lakehouse"

Use Lakehouse to analyze which Atlan features users are adopting and how deeply they engage with each feature area.

Understand the core technologies and architectural components of the Lakehouse platform

Use Lakehouse to measure how deeply users engage with Atlan through pageview rates, session depth, duration, and user tier analysis.

Reference documentation for the ASSETS table, a unified asset registry that serves as the central lookup for all Gold namespace queries

Open, interoperable data lakehouse platform that makes all of your Atlan metadata instantly accessible to power reporting and AI use cases

Understand how automated table maintenance optimizes query performance and storage utilization

Reference documentation for the BI_ASSET_DETAILS table containing BI-specific attributes across PowerBI, Tableau, Looker, and Sigma

Analyse glossary content, including terms, categories, and assigned assets.

Connect Amazon Athena to Atlan's Lakehouse using AWS Glue Catalog Federation. Setup registers Atlan's Iceberg REST Catalog as a federated data source in Lake Formation, making Lakehouse tables available to Athena and other AWS query engines.

Connect BigQuery to Atlan's Lakehouse to query catalog metadata from BigQuery SQL. Setup requires a GCP Cloud Resource connection, a service account grant from Atlan Support, and a Python script that creates the external tables.

Connect Databricks to Atlan's Lakehouse using foreign Iceberg tables in Unity Catalog to query metadata. Setup creates storage credentials and an external location pointing to Atlan's Lakehouse data in your cloud storage.

Connect PySpark to Atlan's Lakehouse through the Iceberg REST catalog with Polaris credential vending. Credential vending automatically issues short-lived cloud storage credentials for each request without requiring hardcoded storage access keys.

Get your Lakehouse connection details and connect your preferred Iceberg REST–compatible client

Connect Snowflake to Atlan's Lakehouse using Snowflake's catalog-linked database feature to query metadata in standard SQL. Setup creates a catalog integration that links Atlan's Iceberg REST Catalog to Snowflake.

Overview of namespaces, schemas, and tables available in the Lakehouse

Reference documentation for the DATA_MESH_DETAILS table containing data domain and data product metadata

Reference documentation for the DATA_QUALITY_DETAILS table containing all data quality rules and checks

Use Lakehouse to analyze database usage, optimize query performance, and manage storage and compute costs

Reference documentation for the ENTITY_HISTORY namespace containing historical snapshots of asset metadata

Reference documentation for the ENTITY_METADATA namespace containing raw metadata tables for all Atlan assets

Get your Lakehouse connection details from Atlan and configure any Iceberg REST–compatible client to start querying your metadata. Setup requires credentials from the Lakehouse Marketplace view in Atlan.

Reference documentation for the GLOSSARY_DETAILS table containing glossary, category, and term details

Understand the Gold namespace and how it makes Lakehouse metadata ready for analytics and AI.

Reference documentation for the GOLD namespace containing curated tables for human users and AI tools

Use Lakehouse to run advanced lineage analysis across systems and connectors.

Export complete lineage information from your metadata lakehouse for reuse across impact, dashboard, and root cause analysis.

Use Lakehouse to find all downstream dashboards impacted by upstream data changes.

Use Lakehouse to understand downstream impact before making changes to data assets.

Use Lakehouse to trace upstream dependencies and find the source of data issues.

Use Lakehouse to measure and improve tag coverage and propagation across systems.

Reference documentation for the LINEAGE_ADJACENCY_LIST table containing directed lineage edges between assets and processes

Use Lakehouse to measure user activation, churn, reactivation, and cohort retention for a customer domain.

Use Lakehouse to track metadata enrichment coverage across assets and systems.

Reference guide for building metadata enrichment dashboards using Lakehouse data

Use Lakehouse to export rich metadata for AI applications, data marketplaces, and syncing context back to source systems.

Use Lakehouse to identify and consolidate duplicate metrics and glossary terms to keep reporting consistent and maintain a single source of truth

Reference documentation for the OBSERVABILITY namespace containing job execution metrics, data quality scores, and app execution logs in the Atlan Lakehouse

Explore practical Lakehouse use cases across metadata quality, lineage, cost optimization, and glossary analysis.

Reference documentation for the PIPELINE_DETAILS table containing data pipeline and orchestration assets

Query Lakehouse metadata using natural language in AI coding agents like Claude Code. Install the atlan-lakehouse skill, which detects your platform and generates appropriate SQL queries.

Best practices for querying and using the Gold namespace efficiently.

Reference documentation for the RELATIONAL_ASSET_DETAILS table containing all relational database and data warehouse assets

Frequently asked questions about security controls and access protections for Lakehouse.

Create helper lineage tables in your warehouse for advanced lineage queries that include direction (upstream/downstream), hop level, and asset names. These tables work with Lakehouse's native LINEAGE_ADJACENCY_LIST table.

Use Lakehouse to measure daily, weekly, and monthly active users from the usage_analytics namespace.

Resolve common issues when querying Lakehouse Iceberg tables from Amazon Athena via the AWS Glue Data Catalog.

Resolve common BigQuery errors when querying Lakehouse Iceberg tables using external Iceberg tables.

Resolve common Databricks errors when creating and querying foreign Iceberg tables in Unity Catalog.

Resolve common Snowflake errors when querying Lakehouse Iceberg tables and Polaris catalog-linked databases.

Reference for Segment-derived usage analytics tables available in the Lakehouse

Understand the Lakehouse platform and its role as a single source of truth for metadata across your data estate