Analyze feature adoption
Use Lakehouse to analyze which Atlan features users are adopting and how deeply they engage with each feature area.
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
Connect Amazon Athena to Lakehouse via AWS Glue Catalog Federation
Connect BigQuery to Lakehouse using external Iceberg tables and run your first query
Connect Databricks to Lakehouse using foreign Iceberg tables in Unity Catalog and run your first query
Connect PySpark to Lakehouse and run your first query
Get your Lakehouse connection details and connect your preferred Iceberg REST–compatible client
Connect Snowflake to Lakehouse and run your first query
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 and connect your preferred Iceberg REST–compatible client
Analyse glossary content, including terms, categories, and assigned assets.
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
Set up the atlan-lakehouse agent skill to query Lakehouse using natural language in AI agents like Claude Code.
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 a multi-hop LINEAGE view in your warehouse to enable advanced lineage queries with direction, level, and asset names.
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