What is a data warehouse vs. a data lake, and how do they fit into system design?

If you're preparing for a tech interview – especially a system design interview – you might hear terms like data warehouse and data lake. Knowing the difference between them is important for any data-focused role. In this guide, we'll explain each concept, highlight their differences, and show how they fit into system design. Along the way, we'll include real-world examples, tech interview tips, and best practices to help you discuss data lakes and warehouses with confidence.

What Is a Data Warehouse?

A data warehouse is a centralized database for structured data. Data from various sources is cleaned and organized into a predefined schema before storage (schema-on-write). This makes it easy to run SQL queries and generate consistent reports. Businesses use data warehouses for business intelligence – such as creating reports and dashboards. Popular data warehouse solutions include Amazon Redshift, Google BigQuery, and Snowflake. (Snowflake is a modern cloud data warehouse – see how difficult is Snowflake to learn more about it.)

What Is a Data Lake?

A data lake is a large storage repository that holds raw data in its native format (structured, semi-structured, or unstructured). Unlike a warehouse, a data lake stores data as-is without a predefined schema – structure is applied only when the data is read (schema-on-read). This flexible approach lets you ingest all types of data without upfront modeling.

Data lakes excel at big data analytics, machine learning, and streaming data. Because the data stays in its original form, it’s ideal for experimentation and building machine learning models. Data lakes are often built on low-cost cloud storage that can scale (for example, using Amazon S3 or Azure Data Lake Storage to hold the data).

Data Lake vs. Data Warehouse: Key Differences

Both data lakes and data warehouses store large volumes of data, but they serve different purposes. Here are some key differences to remember:

• Data structure & schema: A data warehouse stores structured, processed data in a predefined schema (schema-on-write). In contrast, a data lake can hold raw, unprocessed data of any type, and the schema is applied only when reading the data (schema-on-read).
• Usage & users: Data warehouses are used for business analytics and reporting by analysts and managers. Data lakes are used by data engineers and data scientists for big data analytics, exploratory analysis, and machine learning, where they need access to detailed raw data.
• Performance & cost: Queries on a data warehouse are fast because the data is pre-structured, but that performance can come at a higher cost. Data lakes use low-cost storage to hold huge datasets and can scale easily, though querying raw data is typically slower since it requires extra processing.

How Do They Fit into System Design?

In modern system design, data lakes and data warehouses often work together. Here's a typical data architecture:

1. Data ingestion: All incoming data (structured or unstructured) is collected in the data lake (e.g. Amazon S3).
2. Processing & loading: Data engineers clean and transform parts of the raw data as needed, then load the structured results into the data warehouse for fast querying (e.g. into Snowflake).
3. Analytics & usage: Business analysts use the warehouse for reports and dashboards (such as daily sales metrics), while data scientists use the lake to explore raw data (like detailed click logs) for deeper insights.

Best Practices for Data Lakes and Warehouses

Designing a system with both a lake and a warehouse requires careful planning. Here are some best practices (and technical interview tips) to keep in mind:

• Define clear roles: Decide what data goes into the lake versus the warehouse. Use the lake as a staging area for all incoming data, and use the warehouse for the refined data used in analytics.
• Data governance: Apply data cataloging, access controls, and quality checks. This prevents the lake from turning into a disorganized "data swamp" and keeps both the lake and warehouse reliable and secure.
• Practice and prepare: Design sample systems that include both components. Do some mock interview practice by explaining how you'd integrate a data lake and a data warehouse – this will solidify your understanding and improve your communication under pressure.

Conclusion: Key Takeaways and Next Steps

Understanding the data lake vs. data warehouse question is crucial for designing modern data systems. Remember these key takeaways:

• A data warehouse stores structured, processed data and is ideal for fast analytics and business intelligence.
• A data lake stores raw, unprocessed data and offers flexibility for big data analysis and experimentation.
• Used together, they complement each other – the lake feeds the warehouse, and the warehouse delivers quick insights from curated data.

To continue learning, check out resources on DesignGurus.io. The Grokking SQL for Tech Interviews course will help sharpen your SQL skills. Aspiring data engineers should also review the top interview questions for data engineers to see these concepts in action. For more, read our Q&A on understanding data lakes vs. data warehouses for interviews.

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