Lakehouse vs Data Warehouse vs Data Lake: Databricks Edition

As data volumes grow and use cases expand into real-time analytics and AI, many organisations struggle to choose the right data architecture. Terms like data lake, data warehouse, and lakehouse are often used together, which creates confusion. Each approach solves different problems, and choosing the wrong one can lead to slow systems, high costs, and failed analytics projects.

This blog explains Lakehouse vs Data Warehouse vs Data Lake in a clear and practical way, using Databricks as the reference platform. By the end, you will understand how each architecture works, where it fits best, and why many companies are moving toward the lakehouse model.

Why Data Architecture Matters More Than Ever

Modern businesses depend on data for:

• Operational decisions

• Customer insights

• Forecasting and planning

• Machine learning and AI

• Real-time monitoring

If data is hard to access, slow to process, or unreliable, teams lose trust and productivity. Architecture decisions made early often determine whether a data platform can scale or becomes a bottleneck.

Understanding the difference between a data lake, data warehouse, and lakehouse helps teams build systems that support both today’s needs and future growth.

What Is a Data Lake

Definition of a Data Lake

A data lake is a storage system that holds large amounts of raw data in its original format. This includes structured, semi-structured, and unstructured data.

Common data lake storage includes:

• Cloud object storage

• Files such as CSV, JSON, Parquet

• Logs, images, and sensor data

How Data Lakes Work

Data is ingested into the lake with minimal transformation. Processing happens later when data is read for analysis.

This approach supports flexibility and low storage cost.

Strengths of Data Lakes

• Can store any type of data

• Low storage cost

• Scales easily

• Good for data science experiments

Limitations of Data Lakes

• No built-in data quality checks

• Risk of inconsistent schemas

• Hard to manage governance

• Slower analytics performance

• Data can become messy over time

Without strong controls, data lakes often turn into data swamps.

What Is a Data Warehouse

Definition of a Data Warehouse

A data warehouse is a structured system designed for analytics and reporting. Data is cleaned, transformed, and organised before it is stored.

Common warehouses include Snowflake, Redshift, and BigQuery.

How Data Warehouses Work

Data is processed using ETL pipelines before loading. Tables are optimised for SQL queries and dashboards.

Warehouses focus on business reporting and metrics.

Strengths of Data Warehouses

• Fast query performance

• Strong schema enforcement

• Reliable business reporting

• Good governance and access control

Limitations of Data Warehouses

• Higher storage and compute costs

• Limited support for unstructured data

• Less flexible for machine learning

• Data duplication across systems

Warehouses work well for reporting but struggle with modern AI and data science workloads.

What Is a Lakehouse

Definition of a Lakehouse

A lakehouse combines the flexibility of a data lake with the performance and reliability of a data warehouse. It stores data in low-cost object storage while adding features like transactions, schema enforcement, and fast analytics.

Databricks is one of the leading platforms for the lakehouse architecture.

How the Lakehouse Works in Databricks

In Databricks, the lakehouse uses:

• Cloud storage for data

• Delta Lake for reliability

• Apache Spark for processing

• SQL, Python, and ML tools in one platform

Data is stored once and used for many workloads.

Key Features of the Lakehouse

• ACID transactions on data lake storage

• Schema enforcement and evolution

• Time travel and versioning

• Unified analytics and machine learning

• Lower cost compared to warehouses

Lakehouse vs Data Warehouse vs Data Lake: Core Differences

Storage

• Data Lake stores raw files with no structure

• Data Warehouse stores processed data in tables

• Lakehouse stores structured and raw data together using open formats

Performance

• Data Lake performance depends on processing tools

• Data Warehouse is optimised for SQL analytics

• Lakehouse delivers fast analytics using optimised engines like Databricks SQL

Data Types Supported

• Data Lake supports all data types

• Data Warehouse mainly supports structured data

• Lakehouse supports structured, semi-structured, and unstructured data

Cost

• Data Lake has the lowest storage cost

• Data Warehouse has higher storage and compute costs

• Lakehouse balances low storage cost with flexible compute

Governance

• Data Lake has weak governance by default

• Data Warehouse has strong governance

• Lakehouse provides strong governance using tools like Unity Catalog

Machine Learning Support

• Data Lake supports experiments but lacks reliability

• Data Warehouse is not ideal for ML workflows

• Lakehouse supports full ML lifecycle on the same data

Why Databricks Is Built for the Lakehouse

Databricks was designed to solve the problems of both data lakes and data warehouses.

Delta Lake

Delta Lake adds reliability to data lakes by providing:

• Transactions

• Schema checks

• Data versioning

• Efficient reads and writes

This removes the risk of messy data.

Unified Analytics and AI

Databricks supports:

• Data engineering

• Business analytics

• Machine learning

• Streaming workloads

All using the same data and platform.

Medallion Architecture

Databricks encourages the Medallion pattern:

• Bronze for raw data

• Silver for clean data

• Gold for business-ready data

This structure keeps data organised and trusted.

When to Use Each Architecture

When a Data Lake Is Enough

• Storing large volumes of raw data

• Low-cost archival storage

• Simple data science experiments

When a Data Warehouse Is the Right Choice

• Heavy BI reporting

• Stable schemas

• Limited unstructured data

• SQL-first analytics

When a Lakehouse Is the Best Option

• Combining analytics and machine learning

• Handling large and diverse data types

• Reducing data duplication

• Supporting real-time and batch workloads

• Scaling data and AI together

For many modern companies, the lakehouse provides the most flexibility.

Common Mistakes When Choosing Architecture

Many teams make mistakes such as:

• Building a data lake without governance

• Using a warehouse for machine learning

• Duplicating data across systems

• Ignoring future AI needs

• Over-engineering too early

Architecture should support long-term goals, not just current reporting needs.

How to Migrate Toward a Lakehouse

A gradual approach works best.

Steps include:

1. Keep existing warehouse for reporting

2. Introduce a lakehouse for new use cases

3. Ingest raw data into the lakehouse

4. Apply Medallion architecture

5. Move advanced analytics and ML workloads

6. Optimise BI queries on the lakehouse

Databricks supports this hybrid approach well.

Lakehouse vs Data Warehouse vs Data Lake: Final Thoughts

The debate around Lakehouse vs Data Warehouse vs Data Lake is not about which one is better in all cases. It is about choosing the right architecture for modern data needs.

Data lakes offer flexibility but lack control.
Data warehouses offer performance but lack flexibility.
Lakehouses combine the strengths of both.

With platforms like Databricks, organisations no longer need separate systems for storage, analytics, and AI.

Conclusion: How Tenplus Helps You Choose and Build the Right Architecture

Choosing the right architecture is not just a technical decision. It affects cost, speed, scalability, and future AI adoption.

Tenplus helps organisations:

• Assess current data platforms

• Compare lakehouse, warehouse, and lake architectures

• Design Databricks-based lakehouse solutions

• Implement Medallion architecture

• Build reliable pipelines and governance

• Optimise cost and performance

• Deliver working Proof of Concepts in 15 days

If your team wants clarity and confidence in choosing the right data architecture, Tenplus provides the expertise and speed to move forward without risk.

Book a free PoC with Tenplus today!

FAQs

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

A data lake stores raw data with little structure. A data warehouse stores clean, structured data for reporting. A lakehouse combines both by offering low-cost storage with strong performance and data reliability.

Why do companies choose a lakehouse over a data warehouse?

Companies choose a lakehouse because it supports analytics, real-time data, and machine learning on the same platform. It reduces data duplication and works better for modern AI use cases.

Is Databricks a data lake, data warehouse, or lakehouse?

Databricks is a lakehouse platform. It uses cloud storage with Delta Lake to provide data reliability, fast analytics, and support for machine learning in one system.