Data warehouse vs data lake: What's the difference?

Data lakes and data warehouses are essential components of data storage. A data lake stores vast amounts of raw, unprocessed data from various sources. A data warehouse stores cleaned, structured data that's optimized for analytics and business intelligence.

The main difference between the two lies in how the data is stored, processed, and used. Data lakes offer more flexibility for data exploration, for example, while data warehouses support structured querying and reporting. 

Modern data environments usually require both solutions to work together to build a resilient and scalable data platform.

Let’s explore the key differences between data lakes and data warehouses, including the primary functions they perform and how best to maximize their potential for your organization.

What is a data lake?

A data lake is a centralized repository that is designed to store large volumes of raw data from multiple sources, without the need to structure it first.

Unlike traditional databases, data lakes allow businesses to collect, process, and explore data in its native format. This enables them to perform a wide range of analytics and business intelligence activities.

Modern organizations often use data lakes to unify data from operational systems, real-time data streams, cloud storage, and external datasets.

This flexible approach allows them to conduct AI and machine learning (ML) projects, predictive analytics, and exploratory data analysis.

What is the purpose of a data lake?

The primary purpose of a data lake is to provide a scalable and cost-effective way to store and explore all types of data – structured, semi-structured, and unstructured.

Data lakes enable data engineers, data scientists, and analysts to explore data sets without worrying about traditional database limitations.

They are essential for developing machine learning models using raw sensor data and running predictive analytics based on real-time social media streams.

What is the structure of a data lake?

Data lakes have a flat storage architecture, where data is stored in its original form along with descriptive metadata. Instead of organizing data into rigid tables like a relational database, a data lake stores: 

• Files (CSV, JSON, images, videos)
• Streams (IoT data, real-time logs)
• Documents (PDFs, text files)

As the cloud platform or on-premises system where the lake is built serves as the storage layer, it's designed to scale as the amount of data grows.

How do data lakes operate?

Data lakes operate on a schema-on-read model, which means the data’s structure is only defined when it's accessed – not when it gets ingested.

By improving its flexibility, schema-on-read allows businesses to analyze data types that were unknown at the time of collection. This makes it an excellent tool for analyzing data or business intelligence that constantly evolves.

Data lake vs data lakehouse

You may be familiar with the term ‘data lakehouse’, which combines the flexibility of a data lake with the structure and data governance of a warehouse.

Unlike traditional data lakes, lakehouse architectures use a storage layer like Delta Lake to add reliability, quality, and query performance to raw data.

This unified model can be beneficial for companies as it can better help them to unify data that is stored for both exploration and analytics.

What is a data warehouse?

A data warehouse is a centralized system that stores structured and processed data. This data is specifically optimized for querying, reporting, and business intelligence.

Typically, it integrates information from various sources, then cleanses it, and organizes it into a format that enables decision-makers to generate insights quickly and reliably.

Data warehouses have been an integral part of analytics and business intelligence platforms for decades. They are widely adopted by enterprises that need trusted, consistent data for regulatory reporting, executive dashboards, and forecasting.

What is the purpose of a data warehouse?

The main purpose of a data warehouse is to provide a consistent, reliable source of truth for business operations and analytics.

By storing curated, trusted data, data warehouses generate financial reports for regulatory compliance and power business dashboards that track KPIs across departments.

Essentially, they're built to improve data quality, enhance query performance, and enable predictive analytics on historical data.

What is the structure of a data warehouse?

Data warehouses use a structured, relational database model, which is organized into tables, columns, and rows. This model often follows the star or snowflake schemas to polish reporting and analytics.

This structured approach ensures that analytics queries (including complex joins and aggregations) can be executed efficiently across large data sets.

Warehouses typically store cleaned data extracted through ETL (extract, transform, load) processes from multiple sources of data. These include ERP systems, CRM platforms, and cloud data warehouses.

How do data warehouses operate?

Data warehouses operate on a schema-on-write model. This means the schema (the structure and rules governing the data) is applied at the time data is written into the system.

This approach enforces strict data integrity, which improves query performance. However, it does require meticulous planning during the data integration process.

Data warehouse vs data mart

A data mart is a smaller, more focused version of a data warehouse. It's usually built for specific business units like marketing or finance teams.

While a data warehouse covers enterprise-wide data integration, a data mart delivers targeted data sets for faster access and simpler queries.

Data warehouse vs data lake – key differences

While data lakes and data warehouses both manage data, they are utilized for different goals and architectures.

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Data lake vs data warehouse – which one should I use?

Whether you choose to use a data lake or a data warehouse depends on several factors. This includes your organization’s data sources, analytics goals, and intentions for using the data. Each offers notable strengths that are better suited to different business needs and usages.

Data lake

A data lake is best suited for scenarios where flexibility, scale, and support are required for diverse data types. They might include:

• Storing multiple sources of raw data: Ideal for collecting semi-structured and unstructured data from various sources.
• Exploring data with AI and ML models: Necessary for training and improving predictive analytics.
• Handling real-time data streams: Excellent for real-time ingestion from IoT devices or social media.
• Supporting future unknown use cases: Store data securely now, even if you don’t yet know how you'll use it.
• Building modern data platforms: Integrate with cloud storage, cloud platforms, and data fabric solutions.

Data warehouse

In contrast, a data warehouse is better suited for cases where the business needs the data for structured reporting, consistency, and fast querying is critical. Typically, this may take the form of:

• Running business intelligence reports: Perfect for structured analytics and business dashboards.
• Maintaining data integrity and quality: Ensure enterprise data is trusted and standardized.
• Optimizing query performance: Essential for complex SQL queries across relational databases.
• Integrating data securely from multiple sources: Supports strong data governance frameworks.
• Analyzing historical trends: Efficiently store cleaned data for long-term analysis.

Key implementation considerations

Regardless of whether your business wants to implement a data lake or data warehouse, it will need to ensure the chosen system is scalable, secure, and reliable. For this reason, to maximize the value of its data assets, it is important to focus on the following core areas.

Data governance

It is important to establish clear policies for managing who can access, modify, and use data stored within your platform.

At the same time, it's wise to implement metadata management, and access controls, and conduct regular auditing. Doing this will better help to maintain trusted data and ensure regulatory compliance.

Data integration

The more robust your data pipelines are, the more they'll be able to integrate data from multiple sources. These may include operational databases, cloud storage, and real-time data streams.

If you prioritize flexibility and automation, it may also help to simplify the ongoing processes of data ingestion, transformation, and enrichment.

Scalability

Companies that want to scale their data will need to choose storage and computer architectures that can easily adapt to their needs.

For instance, cloud platforms, cloud data warehouses, and hybrid storage solutions all offer the ability to scale resources up or down depending on the processing requirements and the amount of data an organization requires.

Data quality

You will want to focus some of your attention on improving data integrity and quality at every stage of the data flow.

To enable you to do this, it is worth applying validation rules, deduplication processes, and anomaly detection early. Doing this should help to ensure that your raw data becomes even more reliable information for your analytics and business intelligence activities.

Future trends in data storage

The future of data storage is moving towards convergence and intelligence, because organizations no longer want to, nor see value in, managing separate systems for flexible storage and structured analytics.

Instead, as they continue to be refined, hybrid solutions like lakehouse architectures will combine the best features of data lakes and data warehouses. This will enable them to offer unified platforms that support both raw and curated data.

Meanwhile, generative AI will continue to change how companies manage metadata, automate data cataloging, and uncover hidden patterns within their repositories. This means that as the amount of real-time data increases, modern platforms will need to natively support data streaming and low-latency analytics.

Additionally, cloud-native and multi-cloud strategies will continue to be essential tools that give businesses the freedom to store, process, and govern data across various sources seamlessly.

All this means that the most forward-looking companies will probably invest in storage solutions that prioritize agility, trust, and unified access, and not just volume. Doing this will help them stay competitive in a new era that will be defined by data-driven decision-making.

Summing up

Data lakes offer flexible, scalable storage for raw data, while data warehouses deliver trusted, structured data for business insights. Both are essential parts of a modern data strategy.

It seems clear that the more you understand the key differences between the two, the more it will help your organization choose the right solution, or combination, for its needs.

And RecordPoint can help you decide. With a range of advanced technologies that assist with everything from data categorization and discovery to minimization and AI governance, our entire platform is designed to make your data more accessible, secure, and compliant.

Contact us today if you need help choosing between a data lake and a data warehouse.

Or, take a RecordPoint platform tour to see how we can assist your teams in driving business growth and innovation.

FAQs

Can a data lake replace a data warehouse?

No. A data lake and a data warehouse serve different purposes. However, they often complement each other in modern data architecture.

What types of data are stored in a data lake?

Data lakes store structured, semi-structured, and unstructured data. This includes raw files, logs, videos, and sensor data.

What is schema-on-read vs schema-on-write?

Schema-on-read means the data structure is applied during querying (common in data lakes). With schema-on-write, the structure is defined when storing the data (used in data warehouses).

Is a cloud data warehouse better than an on-premises one?

Cloud data warehouses typically offer better scalability, lower upfront costs, and faster innovation when compared to on-premises systems.

What is a data fabric?

A data fabric is an architecture that unifies data management across multiple cloud and on-premises environments. It is designed to improve data access, governance, and security.