Data lake versus data warehouse

Data management is a big part of powering enterprise analytics and building exceptional customer experiences. You've likely heard the terms data lake and data warehouse, but it's hard to know which one fits your project. These two systems handle data in different ways. A data lake is like a large pool of raw data where you don't define the purpose right away. A data warehouse is more like a library of organized, filtered data that's ready for specific tasks. Knowing the differences helps you choose the right tool for your data needs.

Data lake and data warehouse defined

A data lake is a central place where you can store all your data at any scale. You don't need to change the data before you save it. It stores data in its raw form, whether it's structured, semi-structured, or unstructured. This makes it a great choice for teams that want to gather lots of data now and decide how to use it later.
A data warehouse is a system built for data analysis and reporting. Unlike a data lake, a data warehouse only holds data that's already been cleaned and processed. It uses a specific structure or "schema" to organize the data. This makes it very fast for running queries and creating business reports.

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

The main difference between these two systems is how they handle data structure and usage. A data lake is for raw, unstructured data, while a data warehouse is for structured, processed data.

Feature	Data lake	Data warehouse
Data type	All data (raw, structured, unstructured)	Processed, structured data
Purpose	Not yet defined	Defined and specific
Users	Data scientists, data engineers	Data analysts, business intelligence analysts
Accessibility	Highly flexible, easy to change	Harder to change, more rigid
Processing	Schema-on-read (defined when used)	Schema-on-write (defined before saving)
Benefits	Low cost for large volumes Flexible for any data type Scalable for AI and ML models	High performance SQL queries High data quality and trust Secure and easy for SQL users

Feature

Data lake

Data warehouse

Data type

All data (raw, structured, unstructured)

Processed, structured data

Purpose

Not yet defined

Defined and specific

Users

Data scientists, data engineers

Data analysts, business intelligence analysts

Accessibility

Highly flexible, easy to change

Harder to change, more rigid

Processing

Schema-on-read (defined when used)

Schema-on-write (defined before saving)

Benefits

Low cost for large volumes
Flexible for any data type
Scalable for AI and ML models

High performance SQL queries
High data quality and trust
Secure and easy for SQL users

Industry examples

Gaming

Imagine you're building a mobile game. You want to track every single button click from every user. You don't know yet which clicks are important for your next update. You can send all those raw JSON events directly into a data lake. Later, your data scientists can run a script to find patterns in that raw data.

Manufacturing

Another example is IoT sensors. If you have thousands of sensors sending temperature data every second, you can dump that raw data into a lake. You'll have a full history of everything that happened without worrying about how to format it first.

Retail

Think about a retail company that needs to track its sales. Every night, the system takes all the orders from the day, cleans up the addresses, calculates the tax, and saves them into a data warehouse. A manager can then run a report to see exactly how many blue shirts were sold in Chicago. The data is neat, tidy, and ready for a chart.

Finance

A bank might also use a data warehouse to track accounts. They need to know the exact balance of every customer at any moment. They don't want raw logs, they want a structured table that shows every transaction clearly.

Building an AI model with a data lake

Data scientists often need to build an AI model that can spot fraudulent bookings in real time. Because the data comes from many places, like website logs, mobile app events, and third-party partners,a data lake works best to train AI models.

Collecting raw data

Start by setting up a pipeline to send every single raw event into Cloud Storage. This includes messy JSON files from the website and binary logs from the mobile app. You don’t need to worry about formatting the data yet because Cloud Storage is built for this kind of scale.

Processing at scale

To make the data useful for the AI model, you need to clean it. You can use Google Cloud Service for Apache Spark to run a serverless Apache Spark job. This allows youto transform millions of raw logs into a structured format without needing to manage any servers or clusters.

Training the model

Now that the data is ready, you can feed it into a machine learning tool. Since the original raw data is still in the lake, you can always go back and look at the "hidden" details that might help make the model even better.

By using a data lake, you can store everything at low cost and only process what’s needed when it's time to build the model.

Predicting sales with a data warehouse

Now, let's look at a data science use case for retailers. You can forecast how many winter coats the company will sell next month, and since the sales data is already clean and stored in a database, it’s recommended to use a data warehouse for this task.

Accessing clean data

Start with BigQuery, which acts as a company's central data warehouse. The sales data is already organized into neat tables with columns for dates, prices, and product IDs. Because the data is already structured, you don't have to spend any time cleaning it.

Running fast queries

Write a SQL query to see the last five years of winter sales. Even though there are billions of rows of data, BigQuery finds the answer in seconds. This speed allows you to try different ideas and refine the forecast quickly.

Sharing insights

Once the forecast is ready, you can use a built-in tool to create a dashboard. The marketing team can now see exactly how many coats they need to order. Because BigQuery is serverless, the company only pays for the queries that you run, which keeps costs low.

For this use case the data warehouse is the best tool because it provides fast, reliable answers to specific business questions using data that's already in a usable format.

Solve your business challenges with Google Cloud

New customers get $300 in free credits to spend on Google Cloud.

Talk to a Google Cloud sales specialist to discuss your unique challenge in more detail.

Choosing between data lakes and data warehouses

Choosing between a data lake and a data warehouse depends on what you're trying to build. If you have lots of raw data and want to explore it with code, start with a data lake. If you have specific business questions and want fast, reliable reports, a data warehouse is likely the better choice. Many enterprise companies actually use both together to get the best of both worlds.

Related Google Cloud products and services

Most cloud providers offer tools to help you build both of these systems. You'll often find managed services that allow you to start a data lake or a warehouse with just a few clicks. Here are some examples of tools available on Google Cloud: