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

Think about the myriad of tools people in your company use to do their jobs.

You and your colleagues depend on product management software, marketing tools, and countless other software solutions daily. Most of those tools generate valuable data. Your customers also generate data when they use your product, visit your website, or perform other trackable actions.

What should you do with all that data? You can store the information to extract insights, and there are a couple of options for how and where to do it. Data lakes, data warehouses, and data lakehouses are three of the most common data storage options.

Each has advantages and disadvantages, and the best choice depends on data volumes, the level of scalability you need, and other factors.

What is a data lake?

Data lakes store large volumes of data in its native format, whether structured, semi-structured, or unstructured. They deliver the most value when they’re implemented alongside other infrastructure that supports machine learning, predictive analytics, and other “big data” initiatives.

The three main benefits of data lakes are scalability, cost-effectiveness, and flexibility. They reliably handle petabytes of data, which is a massive volume—one petabyte equals one million gigabytes. This enables you to scale storage up and down as needed affordably. Since data lakes store all kinds of data, they can also adapt to ever-changing business requirements.

Two potential downsides to using a data lake are data governance issues and performance.

Data lakes store many different types of data, which can lead to data integrity issues. When using a data lake, it’s important to follow data governance best practices to ensure data accuracy, consistency, and overall data quality.

The sheer volume of data in a lake can also be problematic—if data is poorly organized, redundant, or siloed, querying speeds and overall performance also suffer.

When to use a data lake: Data lakes are most frequently used in streaming, machine learning, and data science scenarios. For example, a media company could use a data lake to store and analyze user interaction data, including viewing habits, preferences, and engagement metrics.

What is a data warehouse?

Think of a data warehouse as a well-organized storage room. Data warehouses store structured data from a variety of different sources. Data is stored in a relational structure, meaning that data inside the warehouse is neatly organized into rows, columns, and tables.

Data undergoes a process called data ingestion before getting stored in a data warehouse. Data ingestion involves collecting, processing, and preparing data for storage. Here’s how it works:

1. Extract data from various sources.
2. Transform the data by cleaning, processing, and converting it into the desired format.
3. Load the newly transformed data into your data warehouse.

The data ingestion process enhances data quality and consistency. The benefits of data warehouses include streamlined data processing practices, improved data analysis, and reporting abilities. Many of the most popular data warehouses also integrate with various business intelligence tools, making it easier to use data to drive decision-making.

Data warehouses aren’t without their downsides. Implementing and maintaining a data warehouse can be costly. Ingesting and integrating data from various sources is sometimes complex and time consuming, especially when you’re dealing with different data formats and structures. Additionally, handling unstructured data sometimes requires additional preprocessing, leading to potential delays in data processing.

When to use a data warehouse: Data warehouses are well suited for analyzing structured data for business intelligence and reporting purposes. For example, an ecommerce company could use a data warehouse to store sales and marketing-related data about acquisition channels, purchases, and campaign performance.

What is a data lakehouse?

Data lakehouses combine features of data lakes and data warehouses. Like a data lake, they offer a unified storage platform for diverse data types. However, they also offer powerful data processing and analytics capabilities like a data warehouse.

Essentially, data lakehouses use the data structure and management principles found in data warehousing and combine them with a data lake's flexibility and low-cost storage.

Lakehouses address some common challenges associated with data lakes, including data governance and the potential for slower retrieval speeds.

On the governance side, data lakehouses include elements of data warehousing, like consistent data structures and validation controls. These tactics help maintain data integrity. Since the data has some structure and organization, data retrieval and querying are also faster.

The main disadvantage of a data lakehouse is the complexity. Implementing and managing a data lakehouse involves a high degree of technical expertise, even more than implementing and managing a data lake or warehouse.

When to use a data lakehouse: Many companies use a data lakehouse to simultaneously execute multiple data processing operations, such as retrieval, storage, and analysis. For example, a healthcare company could use a data lakehouse to store patient records, real-time sensor data, and clinical trial data.

Comparing data lakes vs. data warehouses vs. data lakehouses

Now that you understand the basics of each data storage solution, it’s time to look at how each one handles two key areas:

• Data structure and schema refer to how data is organized and stored within the system. Schema defines the organization and structure of the data itself, including its format and any rules and limits applied to the data fields.
• Querying and performance encompass how the storage system processes and retrieves data. This includes how quickly and efficiently the system executes queries, generates reports, and performs various analytical tasks.

Data lakes use a schema-on-read approach for data structure. That means the structure and requirements for organizing data are applied when it’s being read, not when it’s initially stored. A schema-on-read approach gives you more flexibility when handling diverse and unstructured data.

Data warehouses use a schema-on-write approach, meaning data must adhere to a predefined structure when it’s loaded into the warehouse. This provides data consistency and enables for more efficient data processing and analysis.

Data lakehouses strike a balance between structured and unstructured data needs. They enable you to store unstructured data and then apply structure when it’s retrieved. Lakehouses also support schema evolution by enabling changes to data structures as business needs evolve, like adding new data formats and modifying existing formats.

Not surprisingly, how a data storage platform handles data structure and schema directly impacts speed and performance.

While data lakes excel at handling massive volumes of diverse data, the system interprets the structure of your data every time it's accessed. This can lead to processing delays, a problem for companies needing real-time analytics capabilities.

Data warehouses boast faster querying response speeds thanks to their data's well-organized and highly structured nature. But there’s a cost to this upside—you’ll spend more time preparing data, which limits how quickly you can load data into your warehouse.

Data lakehouses are the best of both worlds. Like a data lake, they support various ingestion types, which results in quick loading speeds. They also incorporate structured data organization similar to a warehouse without the same degree of rigidity. It’s a combination that delivers fast query response times plus efficient processing and analysis.

Which data storage option is best for you?

Here are some general guidelines when selecting between a data lake, data warehouse, and data lakehouse:

• Use a data lake for raw, unstructured data, like server logs and other system-generated data.
• Use a data warehouse to analyze structured, historical data, including customer information, transaction records, and inventory data.
• Use a data lakehouse to collect and analyze various data types and formats.

Data storage is a complicated topic. It’s not uncommon for a company to use several types of data storage for different purposes. Ultimately, the best choice for your company will depend on scalability, performance, and other technical considerations.

Incorporate Amplitude into your data stack

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.

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 fits into any existing data environment and gives you insights into the full customer journey. Use it to optimize customer acquisition, improve retention, and increase your bottom line.