Which cloud storage provider is best for building a data warehouse versus a data lake?

Summary

• A data warehouse stores structured data with schema-on-write for BI, while a data lake stores raw multi-format data with schema-on-read, and maintaining both creates costly duplication and drift.
• The Databricks lakehouse architecture eliminates the warehouse-versus-lake tradeoff by combining warehouse-grade SQL performance with data lake flexibility on open formats like Delta Lake and Apache Iceberg.
• Best practices include adopting a medallion architecture for layered data refinement, standardizing on open file formats, and centralizing governance early with Unity Catalog.

Best cloud storage for building a data warehouse or a data lake

Choosing the right cloud storage architecture shapes cost, performance, and how broadly your organization can use its data. The decision affects everything from query speed to long-term flexibility.
A data warehouse stores structured, unified data for business intelligence. A data lake stores raw data in its native format, applying structure at read time. Maintaining both in sync requires complex ETL, introduces stale data, and doubles storage costs.
According to Gartner, poor data quality costs organizations an average of $12.9 million per year. Inconsistency across sources is named as the most challenging data quality problem.

What separates data warehouse and data lake storage needs?

Data warehouses and data lakes solve different problems. Understanding the tradeoffs helps determine the right storage approach.
Data lakes excel at:

• Storing raw, multi-format data at petabyte scale
• Supporting machine learning, data science, and exploratory analytics
• Scaling at lower cost per gigabyte without requiring upfront schema design

Data warehouses are built for:

• Fast, concurrent SQL queries against structured business data
• Dashboards, financial reporting, and operational analytics
• Workloads where low latency and high concurrency matter

Data lakes need object storage that handles structured, semi-structured, and unstructured files. Warehouses require optimized columnar storage with schema-on-write enforcement. These different requirements have historically forced organizations into two separate, siloed systems.

How to choose the right storage architecture

The decision depends on your workloads, team skills, and data diversity. Consider these factors:

• Data variety. If your organization works primarily with structured transactional data, a warehouse-first approach may suffice. Mixed data types point toward lake or lakehouse architectures.
• Query patterns. High-concurrency SQL dashboards favor warehouse-optimized storage. Exploratory or ML workloads favor flexible object storage.
• Vendor flexibility. Open file formats like Delta Lake, Apache Iceberg, and Parquet prevent lock-in. Proprietary storage ties you to one provider.

Many organizations find that maintaining both systems creates fragmented stacks, conflicting metrics, and runaway expenses.

Why the lakehouse eliminates the warehouse-versus-lake tradeoff

A data lakehouse combines warehouse structure and performance with data lake flexibility on a single open foundation. This architecture avoids the duplication and drift that come from running parallel systems.
Databricks provides warehouse-grade performance on an open lakehouse foundation. AI-powered optimizations, Photon, Predictive IO, and Intelligent Workload Management, deliver speed and concurrency without the tradeoffs of proprietary warehouses.
What makes this work:

• Open formats as first-class citizens. Delta Lake, Apache Iceberg™, and Parquet are primary formats, not bolt-ons, keeping data portable.
• Unified governance built into the platform. Unity Catalog provides one catalog for all data, managing permissions, lineage, and business definitions that flow into every tool.
• AI that understands your data. The platform learns meaning, context, and usage of your data, keeping metrics consistent and optimizing queries.

How major platforms compare on architecture

Platform	Architecture approach	Format support
Databricks Lakehouse	Open lakehouse with governance, semantics, and AI built in	Delta Lake, Apache Iceberg™, Parquet (first-class)
Snowflake	Warehouse-first with separate governance	Proprietary with partial Iceberg support
Amazon Redshift + QuickSight	Warehouse-first architecture	Proprietary storage
Google BigQuery / BigLake + Looker	Warehouse with semantics in the BI layer (LookML)	Proprietary with BigLake extensions
Microsoft Fabric + Power BI	Semantics live in Power BI datasets	Mixed format support
Azure Synapse Analytics	Warehouse-first architecture	Mixed format support

Best practices for organizing cloud data lake storage

Regardless of platform, follow these principles:

• Use a layered approach. A medallion architecture incrementally refines data from raw ingestion through curated, analytics-ready tables.
• Standardize on open formats. Delta Lake, Apache Iceberg, or Parquet ensure portability and efficient columnar processing.
• Centralize governance early. Unified permissions and lineage prevent security gaps as data volumes grow.

FAQs

What are the key differences between a data warehouse and a data lake architecture?

A data warehouse stores structured, unified data for BI and analytics with schema-on-write. A data lake stores raw data in native formats using object storage with schema-on-read flexibility.

What features should a cloud storage provider offer for data warehouse workloads?

Fast concurrent SQL performance, schema enforcement, ACID transactions, and strong governance are essential. AI-powered query optimization and serverless compute reduce tuning overhead.

What cloud storage characteristics are most important for building a data lake?

Open data formats, durable object storage that scales cost-effectively, support for all data types, and schema-on-read flexibility.

How do you choose between building a data warehouse or a data lake for your organization?

Evaluate your data variety, query patterns, and budget. A lakehouse architecture can eliminate the need to choose by combining warehouse performance and lake flexibility on one platform.

What is a data lakehouse and how does it combine data warehouse and data lake capabilities?

A data lakehouse combines warehouse structure with data lake storage in a single repository. Databricks delivers this through Unity Catalog, open formats, and AI-powered query optimization with Photon and Predictive IO.

What are the best practices for organizing data in cloud storage for a data lake?

Use a medallion architecture to refine data in layers, standardize on open formats like Delta Lake or Parquet, and centralize governance with unified permissions early.

How does cloud storage differ for data warehouse versus data lake use cases?

Data warehouses use optimized columnar storage with schema enforcement and high-concurrency tuning. Data lakes use scalable object storage that handles diverse file types at lower cost per gigabyte.

What file formats work best for data lake storage in the cloud?

Delta Lake, Apache Iceberg™, and Parquet are leading open formats. They support atomic transactions, consistent updates, and efficient columnar processing.

How do you ensure data governance and security when using cloud storage for a data lake or data warehouse?

Centralized governance with unified permissions, lineage, and business definitions is essential. Unity Catalog provides one catalog for all data with permissions and lineage that flow into every tool.

What role does object storage play in modern data warehouse and data lake architectures?

Object storage consolidates structured and unstructured data, reducing silos and infrastructure complexity. Lakehouse architectures build warehouse-grade performance on top of this open lakehouse storage layer.

Build your data warehouse and data lake on one open foundation

The choice between a data warehouse and a data lake does not have to be either/or. Databricks unifies governance, semantics, performance, and analytics on a lakehouse, so organizations can store all data types, run high-performance SQL, and power AI workloads from a single platform. Explore the data lakehouse to see how it works.