Data Catalog

Ingests technical metadata from diverse sources—relational databases, data lakes, BI tools, and streaming platforms—without manual intervention. Crawlers scan schemas, table structures, and column-level statistics to populate the catalog. This automation eliminates tribal knowledge and ensures the catalog reflects the current state of the data estate.

• Connects to Snowflake, BigQuery, Redshift, and S3 data lakes
• Captures schema-on-read structures from semi-structured JSON and Parquet files
• Updates lineage graphs automatically as ETL pipelines evolve

Bridges the gap between technical asset names and business terminology. A business glossary defines canonical terms like 'Monthly Active User' or 'Net Revenue Retention' and maps them to the underlying physical columns across multiple databases. This ensures analysts and executives speak the same language.

• Resolves synonyms: 'Customer ID' vs 'client_id' vs 'cust_no'
• Associates data stewards and subject matter experts with each term
• Powers natural language search for non-technical users

Visually traces the journey of data from origin to consumption. Column-level lineage tracks transformations across SQL queries, Python scripts, and ETL tools, answering critical questions for debugging and impact analysis. If a source table changes, downstream dashboards and models are instantly identified.

• Parses SQL from dbt, stored procedures, and BI semantic layers
• Supports regulatory compliance audits for GDPR and CCPA
• Enables root-cause analysis when a KPI suddenly shifts

Automatically computes statistical profiles for every registered dataset. The catalog displays null counts, distinct value ratios, min/max ranges, and distribution histograms directly in the asset view. Users can assess fitness-for-purpose before writing a single query.

• Flags empty columns and potential PII exposure
• Tracks freshness: 'Last updated 3 hours ago'
• Integrates with Great Expectations and dbt tests for quality SLAs

Transforms the catalog from a static dictionary into a living knowledge base. Users can endorse trusted tables, leave ratings, write wiki-style documentation, and ask questions directly on asset pages. This social layer captures the institutional knowledge that never makes it into formal docs.

• Certifies 'Gold Standard' datasets for analytics
• Threaded discussions replace fragmented Slack conversations
• Usage popularity metrics surface the most relied-upon assets

Integrates with enterprise identity and access management systems to display fine-grained entitlements. Users see only the data they are permitted to access, and can request new permissions through automated workflows. Tag-based policies enforce classification rules for sensitive data.

• Redacts or masks PII, PHI, and PCI columns in search previews
• Automates access request routing to data owners
• Audits all human and machine access for compliance reporting

The automated quality snapshot. Data profiling algorithms scan datasets to generate statistical summaries—such as null counts, value distributions, and distinct patterns—directly within the catalog interface. This allows an analyst to instantly assess if a dataset is fit for purpose without writing a single SQL query, flagging columns with 90% null values as poor candidates for machine learning features.

The discovery engine of the catalog. Crawlers automatically connect to diverse data sources—Snowflake, PostgreSQL, S3 data lakes—to extract technical metadata and populate the catalog. A robust framework handles schema-on-read variability by parsing semi-structured JSON logs and inferring structure, ensuring that even ungoverned shadow IT datasets become visible and searchable assets within the enterprise inventory.