Data Lake vs. Data Warehouse vs. Data Lakehouse: Understanding the Differences

Originally posted on May 3, 2024

You're swimming in data.

From all the product management, marketing, and myriad other software tools you and your team use every day, to all the visits, clicks, and engagements of your customers, there's data all around you.

But to make that deluge of data useful, you need a data storage solution: a data lake, a data warehouse, or a data lakehouse. These help companies organize and analyze the massive amounts of information they generate so they can put it to work making smarter business decisions.

So which storage solution should you pick? Each approach handles different data types and serves distinct business needs, though. The best choice for you depends on your data volume, performance requirements, and your specific use cases.

That flood of data keeps rising, too: IDC projected the global DataSphere to more than double from 2022 to 2026, with enterprise data growing more than twice as fast as consumer data, per IDC's 2022–2026 Global DataSphere forecast.

What is a data lake?

Data lakes store large volumes of data in its native format—structured, semi-structured, and unstructured. If you think of data as water, then when you dump a bunch of it all in one place, you get a lake. Data lakes work well with other infrastructure that supports machine learning, predictive analytics, and other "big data" initiatives.

Data lakes are common for streaming, machine learning, and data science scenarios. For example, a media company could store and analyze viewing habits, preferences, and engagement metrics.

Key benefits:

• Scalability: Handle petabytes of data with storage that scales up or down as needed.
• Cost-effectiveness: Lower storage costs compared to traditional databases.
• Flexibility: Store any data type without a predefined structure.

Common challenges:

• Data governance: Mixed data types can create integrity issues without solid data governance best practices.
• Performance: Poor organization can slow queries and reduce performance with such a large volume of data.

What is a data warehouse?

Data warehouses store structured data like a digital filing system. Instead of dumping all your data into a lake, a data warehouse organizes data into tables, rows, and columns. Cloud data warehouses like Snowflake, Google BigQuery, and Amazon Redshift are common examples.

To do that organization, a data warehouse runs data through an ingestion process called ETL:

1. Extract: Collect data from business sources.
2. Transform: Clean and convert data into the required format.
3. Load: Store the processed data in the warehouse structure.

Because of their improved data quality and consistency, data warehouses are commonly used in scenarios with structured data, like business intelligence or for reporting purposes. For example, an ecommerce company could store and analyze its store sales along with marketing-related data like acquisition channels, purchases, and campaign performance.

Key benefits:

• Streamlining: Implementing a data warehouse can help improve data processing practices.
• Analysis: With cleaner data in the warehouse, it's easier to run high-quality analysis and reports.
• Integration: Warehouses integrate well with other tools like business intelligence software.

Common challenges:

• Costs: Setting up a data warehouse's ingestion process and continuing to maintain it can be complex, taking up time, budget, or both.
• Delays: Handling unstructured data often needs extra preprocessing, leading to longer wait times until the data is usable.

What is a data lakehouse?

Data lakehouses attempt to combine the best features of data lakes and data warehouses. Like a data lake, they offer a unified storage platform for diverse data types—and like a warehouse, they offer powerful data processing and analytics capabilities. Databricks and Snowflake are widely used to build lakehouse architectures, often on open formats like Delta Lake or Apache Iceberg.

Many teams use a lakehouse to handle data storage, retrieval, and analysis simultaneously. For example, a healthcare organization could use a lakehouse to store patient records, real-time sensor data, and clinical trial data, querying it all together if they need to.

Key benefits:

• Flexible storage: Store all data types like a data lake.
• Structured organization: Query quickly like a data warehouse with consistent structures and validation controls.
• Cost-effectiveness: Pair low-cost storage with strong analytics.

Common challenges:

• Complexity: Implementing and managing a data lakehouse takes significant technical expertise, more so than a data lake or even a warehouse.

Comparing storage solutions

From the basics of each data storage solution above, there are two key areas that set them apart: their data structure and their querying performance.

Data structure, or schema, refers to how data is organized and stored within a system, including its format and any rules and limits applied to the data fields. Querying performance is how quickly and efficiently the storage system processes and retrieves data for analytical tasks.

Not surprisingly, how a storage platform handles its structure directly impacts its performance:

• Data lakes are schema-on-read, which means their data structure is applied when you access data. It's a more flexible way to store data, but it can lead to slower queries because of the on-demand structure interpretation.
• Data warehouses are schema-on-write, which means their data structure is applied before storage. Though less flexible and more time-consuming for loading data in, it allows for faster queries thanks to that pre-organization.
• Data lakehouses use a hybrid schema that lets you store unstructured data and apply on read, but also set up and maintain structured formats. This leads to faster data loading and faster queries.

Which data storage option is best for you?

Data storage is a complicated topic. Ultimately, the best choice for your company will depend on the types and amount of data you deal with, your query speed needs, your budget, and your team expertise.

Use a data lake when:

• Dealing with raw, unstructured data storage (server logs, sensor data, machine learning or data science)
• Slower query speed isn't a problem
• You need cost-effective storage for massive data volumes

Use a data warehouse when:

• Dealing with structured, historical data (business intelligence, reporting)
• You need answers to queries fast
• Reliable performance is a must for regular analytics

Use a data lakehouse when:

• You need flexibility storing for multiple data types in one platform
• You still need high query performance
• Your team has the technical expertise to implement

Plenty of companies run more than one of these at once. A common pattern is to pull raw data into a lake, then load structured subsets into a warehouse for reporting, while a lakehouse can serve both jobs from a single platform. The lakehouse is gaining ground here: in September 2025, Gartner described it as the architecture most organizations will seek to standardize on, a sign of where the market is heading.

Whatever mix a team runs, an analytics layer on top turns that stored data into action. Amplitude integrates with any storage solution, so your choice of lake, warehouse, or lakehouse doesn't limit what you can learn from your data.

Data storage and management is a considerable part of your overall data infrastructure. Even if you aren't responsible for managing that infrastructure, understanding the basics will increase your data literacy and help you make better data-driven decisions.

Frequently asked questions

Is Snowflake a data warehouse or a data lake?

Snowflake started as a cloud data warehouse, so it stores structured data with schema-on-write for fast queries. It has since added lakehouse-style features that handle more data types in one place. The best label depends on how your team sets it up and uses it.

Is Databricks a data lake or a data lakehouse?

Databricks is built around the lakehouse approach. It pairs flexible, lake-style storage for all data types with the structure and fast queries of a warehouse. It often runs on open formats like Delta Lake or Apache Iceberg.

Can a data lake replace a data warehouse?

A data lake can hold the same raw data, but its schema-on-read design tends to make queries slower than a warehouse built for structured reporting. When a team wants both flexible storage and fast queries, a data lakehouse is usually the better fit than a lake on its own.

Do you need both a data lake and a data warehouse?

Many companies run both. They ingest raw data into a lake, then load structured subsets into a warehouse for reporting and business intelligence. A data lakehouse can cover both jobs from one platform if a team prefers to consolidate.

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

A data lake stores large volumes of raw, structured, semi-structured, and unstructured data, with a schema applied on read. A data lakehouse keeps that flexible storage but adds structure, validation, and faster queries on top, closer to a warehouse. In short, a lakehouse layers warehouse-style organization over lake-style storage.

Incorporate Amplitude into your data stack

Whether you choose a data lake, data warehouse, or data lakehouse, storage is just one part of the modern data stack that supports data analytics at your company. Different analytics tools can enable data collection, analysis, and reporting.

Amplitude's digital analytics platform integrates with any storage solution to help you understand customer behavior across the journey and act on it. Try Amplitude for free today.