Ad Copy Framework — FY27 Q1

P&G has invested heavily in an AI factory running Databricks and Airflow pipelines across a global data estate. The problem: without data contracts enforced at pipeline runtime, models train on whatever data arrives — degraded schemas, stale records, missing fields. Real-time metadata from DataHub streams lineage from every pipeline execution, so AI teams catch data quality issues in seconds rather than discovering them after a bad model run.

Parker Hannifin has absorbed two major acquisitions — Filtration Group and Curtis Instruments — each bringing its own data stack. The result is three separate data environments (Databricks, Airflow, Azure Synapse) with no unified lineage layer across them. DataHub's OSS foundation maps column-level lineage across all three stacks simultaneously, and it's portable — so when the next acquisition arrives, the metadata layer moves with it.

J&J's Population Analytics team manages 15 petabytes of clinical trial data flowing through Airflow, dbt, Snowflake, and Databricks. FDA submissions require full data provenance — and today that traceability is reconstructed manually. DataHub streams column-level lineage from clinical source systems through every dbt transformation in real time, so the chain of custody is always current when auditors ask.

Verizon's Frontier acquisition doubled their data estate to 200PB across two separate telecom stacks. Alation is deployed but relies on scheduled batch scans — meaning lineage across Kafka, Airflow, and BigQuery reflects yesterday, not now. DataHub streams metadata in real time as pipelines run, giving teams accurate cross-stack lineage immediately — not after the nightly scan cycle completes.

IQVIA's Data Architecture team is executing a major cloud modernization — migrating to Snowflake with dbt and Airflow powering transformation pipelines. The pattern: infrastructure moves fast, governance arrives six months later. DataHub inverts this by attaching lineage metadata to every new pipeline at ingestion, so governance isn't retrofitted after the migration settles — it's built in from the start.

State Street's Alpha Data Platform is onboarding new institutional mandates at record pace — $380B in new AUM requiring data workflows that compliance teams can actually trace. Without real-time lineage, custody and compliance teams cannot verify which pipelines touch which mandate's data. DataHub streams column-level lineage through Snowflake, Airflow, and dbt as each mandate's data moves — so investment SLAs are traceable in real time, not reconstructed after the nightly batch.

Schwab runs three separate data warehouses — Snowflake, BigQuery, and Redshift — plus Pub/Sub and Airflow for streaming and orchestration. The TD Ameritrade (Forge) integration adds new data flows across all three. No single catalog can see across all three warehouses simultaneously. Snowflake Horizon covers Snowflake. Collibra covers documentation. DataHub is the only platform that maps real-time lineage across all three warehouses at once — live, not scheduled.

Intel is running Airflow, Snowflake, Databricks, and Kafka simultaneously — each generating metadata that no team can trace across platforms. An active efficiency mandate requires consolidation. DataHub's OSS foundation ingests lineage from all four platforms simultaneously without custom scripts or manual documentation, and because it's open source, the metadata layer is owned by Intel — not locked to any vendor that might itself be consolidated.

Nike runs Airflow orchestration, Databricks Lakehouse, Snowflake analytics, and dbt transformations simultaneously — but without a unified metadata layer, lineage lives in four separate silos. When a pipeline breaks, engineers trace it manually across platforms. The "Win Now" turnaround and $5B marketing investment demand data that's reliable at speed. DataHub's native connectors for all four platforms — plus real-time streaming metadata — unify lineage without migration or retraining. The OSS pilot path (2–3 weeks) leverages 128 existing developer champions already evaluating the platform.

Adobe runs DataHub OSS across a 90-team Databricks environment at DEPLOY stage — the upgrade conversation is already open. The Semrush acquisition (April 2026, $1.9B) adds 3,000+ new data sources that need immediate governance. DataHub Cloud closes the gap the OSS deployment leaves: managed ingestion, automated data contracts, proactive quality detection. CEO Narayen stepping down creates a decision window before new leadership freezes platform decisions. Motion: validate OSS deployment against Cloud value, not land-and-replace.