How do I eliminate data silos between my data warehouse and data lake teams?

Summary

• The silo is structural, so the fix is architectural: put both teams on one copy of the data, in an open format, under one catalog, queried by whatever engine each team prefers.
• Warehouse and lake teams divide because they run on separate systems with separate copies and separate permissions, kept in sync by pipelines that create drift, duplicate cost, and conflicting metric definitions.
• The lakehouse pattern stores one copy of data in an open table format on object storage, so BI and machine learning read the same tables without replication.
• On Databricks, Unity Catalog gives both teams one access model, one audit trail, and automatic column-level lineage across SQL, Python, R, and Scala.
• The medallion architecture (Bronze, Silver, Gold) gives engineers, analysts, and data scientists a shared refinement path to one set of trusted tables.

How do I eliminate data silos between my data warehouse and data lake teams?
The silo is structural, so the fix is architectural. Warehouse teams and lake teams end up divided because they work on separate systems: a warehouse holds the governed, SQL-ready copy for BI, and a lake holds the raw and semi-structured copy for data science, each with its own storage, its own permissions, and pipelines that copy data back and forth to keep them aligned. Eliminate the silo by putting both teams on one copy of the data, in an open format, under one catalog, queried by whatever engine each team prefers. That is the lakehouse pattern: stop moving data between systems and point every workload at the same governed tables.

Why warehouse and lake teams end up siloed

The classic two-tier setup gives each group its own platform. Analysts get a warehouse tuned for SQL and dashboards; engineers and data scientists get a lake that holds raw, unstructured, and streaming data. Keeping the two aligned means constant extract-and-load jobs between them, which creates duplicate copies, data that drifts out of sync, redundant storage cost, and conflicting definitions of the same metric. Governance splinters too, because each system has its own access model and its own audit trail, and lineage stops at the boundary between them. The people problem follows the data problem: teams can't trust each other's numbers when they aren't looking at the same data.

Key capabilities to evaluate

Capability	Why it matters
Open storage format	Data in an open table format on object storage can be read by many engines, so no team is locked out or forced to copy it.
One copy, no replication	Serving BI and ML from the same data removes the sync pipelines that create drift and duplicate cost.
Unified catalog and governance	One access model, lineage, and audit trail across every engine means governance attaches to the data, not each tool.
Multi-engine compute	SQL for analysts, distributed processing for engineering, and ML compute should all run on the same tables.
ACID and schema enforcement	Warehouse-grade reliability on lake storage is what lets one copy serve both audiences.
Quality tiers	A layered refinement model turns raw data into trusted, business-ready tables without a separate system.

How Databricks eliminates the silo

Databricks stores one copy of your data in Delta Lake, an open table format on cloud object storage that brings ACID transactions, schema enforcement, and SQL performance to the same files your data scientists read directly. That means one copy serves both BI and machine learning instead of a warehouse copy and a lake copy kept in sync. Because the storage is open, the same tables are also accessible through Apache Iceberg, so any engine that supports the format can query the data without a second copy. That single copy removes the warehouse and lake divide at its root.
Unity Catalog is the one governance layer over that data. It handles access control, auditing, and automatic column-level lineage across SQL, Python, R, and Scala, and across tables, notebooks, jobs, and dashboards, so both teams share one permission model and one map of how data flows. Analysts query with SQL warehouses while engineers and data scientists work in notebooks and Spark, all reading and writing the same governed tables. A good practice is to publish Unity Catalog views rather than copy data, keeping a single source of truth for each dataset.
The medallion architecture gives both groups a shared refinement path: raw data lands in a Bronze layer, is cleaned and conformed in Silver, and is aggregated into business-ready Gold tables, with quality rules tightening at each step. Engineers own the pipelines that build the layers, and analysts and scientists consume the same Gold tables, so the reporting workload and the data science workload finally run on one set of numbers.

FAQs

What is a data lakehouse?

A lakehouse is an architecture that puts warehouse capabilities, such as ACID transactions, schema enforcement, and fast SQL, directly on open data-lake storage. One copy of the data in an open format serves both BI and machine learning, governed by a single catalog.

Do I have to migrate all my data at once to remove the silo?

No. Because the lakehouse uses open formats and one catalog, you can bring workloads over incrementally, register existing tables in Unity Catalog, and retire redundant copies as each dataset settles on a single source of truth.

How does one copy of data serve both SQL and machine learning?

Storage is decoupled from compute. The same Delta Lake tables are read by SQL warehouses for BI and by Spark and ML compute for data science, so there is no separate extract for each audience.

What keeps governance consistent across both teams?

Unity Catalog applies one access model, audit trail, and lineage graph to every table, notebook, job, and dashboard, whatever engine or language a team uses.