A data catalogue is a searchable metadata repository that makes data assets discoverable across an organisation — answering questions like 'where is the authoritative player dimension table?', 'who owns the delivery fact table?', 'what downstream dashboards depend on this mart model?', and 'what changed about this table in the last deployment?'. Without a catalogue, data discovery happens through Slack messages, personal knowledge, and tribal information that disappears when team members leave. Data catalogues are the technical implementation of DAMA-DMBOK's Metadata Management knowledge area.
OpenMetadata and DataHub are the two leading open-source data catalogue platforms. OpenMetadata was founded by the creators of dbt and focuses on tight dbt integration, schema change notifications, and a rich data quality profiling interface. DataHub was originally developed at LinkedIn and focuses on automated lineage ingestion from a wide connector ecosystem (Snowflake, BigQuery, Airflow, dbt, Looker, Superset, Kafka). Both support Slack and email notifications for schema changes, data freshness alerts, and quality gate failures — enabling data consumers to be proactively notified when a dataset they depend on changes.
Analogy🏏Cricket
🏏 Think of it like cricket: OLTP is the IPL's live ticketing counter — it handles thousands of simultaneous seat reservations, each requiring a precise single-seat record update with immediate confirmation. Speed per transaction and data consistency under concurrent updates are everything. OLAP is the IPL's season statistics department — it runs complex analytical queries across every ball bowled in every match of every season to produce the published rankings, economy rates, and historical comparisons. No one books a seat through the statistics department, and no broadcaster calls the ticketing counter for Bumrah's career economy rate. The two workloads demand completely different systems. Just as the ticketing counter is built for speed and correctness on one seat at a time and would buckle if asked to tally a decade of attendance mid-sale, an OLTP row-store excels at single-record writes but chokes on full-table aggregation; and just as the statistics department pores over millions of past deliveries but would be hopeless at booking a live seat under contention, the OLAP columnar engine sweeps billions of rows yet is the wrong tool for a fast single-row update. The physical design of each — row-oriented for the counter, columnar for the stats desk — is what makes it superb at its own job and unfit for the other's.
🏏 Showing the Cricket analogy — a Cricket version isn’t available for this concept yet.