How do I consolidate multiple point solutions like a warehouse, a lake, and an ML platform onto one architecture?

Summary

• The architecture that consolidates a warehouse, a data lake, and an ML platform is the lakehouse: one governed copy of data in open table formats that serves SQL analytics, BI, machine learning, and streaming from the same place.
• What makes consolidation real is a single storage layer in open formats plus one governance layer over every workload, so a policy written once applies to SQL, ML, and streaming alike.
• Consolidation removes the copies and sync pipelines a fragmented stack depends on. When every engine reads one copy of data, the code whose only job is moving data between systems goes away.
• Evaluate a target on open formats, a unified governance model, warehouse-grade SQL performance, native ML and model serving, and one engine for batch and streaming.
• On Databricks that architecture is the Data Intelligence Platform: Delta Lake and Apache Iceberg on cloud object storage, governed by Unity Catalog, with Databricks SQL for warehousing, Lakeflow for ingestion and ETL, and integrated ML, MLflow, and Model Serving.

How do I consolidate multiple point solutions like a warehouse, a lake, and an ML platform onto one architecture?

Consolidate them onto a lakehouse: one architecture that keeps a single governed copy of your data in open table formats and serves SQL analytics, business intelligence, machine learning, and streaming from that same copy. Separate warehouses, lakes, and ML platforms are expensive less for their licenses than for the plumbing between them: each keeps its own copy of the data, its own sync pipelines, and its own security model, and that duplication is where cost and risk accumulate. A lakehouse collapses those layers into one, so data is stored once, governed once, and read by every engine in place.

What is a lakehouse?

A lakehouse is a data architecture that pairs the low-cost, open storage of a data lake with the reliability and performance of a data warehouse, on one platform. Data lives in open table formats on cheap cloud object storage, and those formats add ACID transactions, schema enforcement, schema evolution, and time travel to plain files. Because the formats are open standards, many engines can read and write the same tables without a proprietary copy, so warehousing, data science, and machine learning all operate on shared data rather than on extracts. How the Databricks lakehouse works covers the layered design.

Key capabilities to evaluate

Judge a platform you plan to consolidate onto by whether it can genuinely replace all three point solutions rather than sit beside them:

Capability	Why it matters
Open storage formats	One copy of data in open formats such as Delta Lake and Apache Iceberg lets every engine read it, so you avoid a new silo and vendor lock-in.
Unified governance	Access control, lineage, and audit defined once and enforced across SQL, ML, and streaming remove the need to reconcile several security tools.
Warehouse-grade SQL	The platform has to deliver the query performance and reliability analysts expect, directly on lake storage, so the warehouse is truly absorbed.
Native ML and serving	Model training, tracking, and deployment should run on the same governed data, so the ML platform is part of the architecture, not a bolt-on.
One engine for batch and streaming	Shared processing for batch and real-time work avoids maintaining parallel systems for each.
Open interfaces	Standard SQL, open APIs, and open formats keep future workloads and tools compatible without re-platforming.

The test is simple. If a candidate still requires you to copy data into it or out of it for a major workload, it is another point solution, not a consolidation.

How Databricks approaches this

The Databricks Data Intelligence Platform is built as that single architecture, so each former point solution maps to a capability on one governed foundation.

• One copy of data in open formats. Data is stored as Delta Lake and Apache Iceberg tables in your own cloud object storage, with ACID transactions and time travel, so analytics, ML, and BI read the same tables. See what makes a lakehouse open.
• One governance model. Unity Catalog applies access control, lineage, and auditing once, across every data and AI asset, so a policy is written a single time and enforced for every engine.
• The warehouse. Databricks SQL and serverless SQL warehouses run analytics directly on lakehouse tables, with the Photon engine for query performance, so warehouse workloads move onto the same storage.
• Ingestion and ETL. Lakeflow ingests through Lakeflow Connect and Auto Loader and builds declarative, quality-checked pipelines with Lakeflow Pipelines, which retires standalone ingestion and orchestration tools.
• The ML platform. Model training, experiment tracking, and deployment run on the same data, with the open-source MLflow for the model lifecycle and Model Serving for CPU, GPU, and LLM endpoints, so models draw on governed data under the same policies as analysts.

Because everything sits on Apache Spark and Photon with Unity Catalog on top, you can consolidate in phases and retire point solutions as workloads move.

Use cases

A data engineering team folds batch and streaming pipelines onto one engine and drops the separate stream-processing cluster. An analytics team points BI tools at Databricks SQL and reads the same governed tables the data scientists build features from and serve models on. The shared copy of data is what removes the sync work.

FAQs

What is the difference between a data lake, a data warehouse, and a lakehouse?

A data lake stores raw files cheaply but lacks transactions and strong governance. A data warehouse adds reliability and fast SQL, but on closed, structured storage. A lakehouse combines both: open, low-cost storage with warehouse-grade transactions, performance, and governance, so one system covers both roles.

Do I have to migrate everything at once?

No. Because data sits in open formats under one catalog, you can move workloads in phases, retiring each point solution as its jobs shift onto the lakehouse. That keeps consolidation incremental rather than a single risky cutover.

How does consolidation cut cost and risk?

Most of the hidden cost in a fragmented stack is the duplicated data and the pipelines that keep copies in sync. Storing one governed copy that every engine reads removes much of that plumbing and the reconciliation of separate security models, which lowers maintenance effort and compliance risk together.