47 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, that lists all assets in Atlan and important attributes

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_ASSETS semantic view containing all business intelligence assets

Connect Amazon Athena to Lakehouse via AWS Glue Catalog Federation

Connect Snowflake to Lakehouse and run your first query

Reference documentation for the CUSTOM_METADATA semantic view containing information about custom metadata attributes

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

Reference documentation for the DATA_MESH_DETAILS view containing information about all data mesh-related assets in Atlan

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

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

Catalog of domain-specific semantic views in the SEMANTIC_VIEWS namespace

Step-by-step guide to enable Lakehouse for your Atlan workspace

Step-by-step tutorial to set up and run your first query on the Lakehouse

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

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

Reference documentation for the GLOSSARY_DETAILS view containing glossary, categories, and terms

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

Reference documentation for the Gold layer containing curated views 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 view containing asset and column-level lineage 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

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

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

Best practices for querying and using the Gold layer efficiently.

Reference documentation for the README view containing information about READMEs for Atlan assets

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

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

Deploy the Gold layer views in Snowflake for analytics-ready metadata

Reference documentation for the TAGS view containing information about tagged assets

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

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