Guide to Data Lake vs. Data Warehouse
Data lakes store raw, diverse data for analysis, whilst data warehouses store structured data for reporting and BI. Learn about the key differences between data lakes and data warehouses.
Data lakes store raw, diverse data for analysis, whilst data warehouses store structured data for reporting and BI. Learn about the key differences between data lakes and data warehouses.
Managing data is like running a growing city. In one part of town, you have carefully planned neighbourhoods with homes, offices, and utilities neatly organised for specific purposes. In another, you have open land—free to develop and adapt as needed, but less structured. Data lakes act as the open landscapes of the data world, storing unstructured and structured data, whilst data warehouses are like well-ordered districts designed for fast, efficient use.
What does this mean for data storage? Should you organise your data in a system like a data warehouse or keep it in a data lake? Both serve critical roles in storing, managing, and analysing data; the key is knowing which fits your needs—or if you need both. Let's explore data lake vs. data warehouse storage solutions and what they can do for your business.
Imagine a vast, natural lake. It collects everything that flows into it—streams, rain, and runoff—without filtering or organising the contents. In the data world, a data lake functions the same way, serving as a repository for raw, unprocessed data from a variety of sources. Unlike traditional storage systems, it doesn't require data to be formatted or organised before being added.
You might hear of a data lakehouse and assume it's the same thing as a data lake, but there is a key difference. A data warehouse, in simple terms, is a repository of data—it houses large amounts of data that have already been processed. (More on that soon!) While a data lake stores raw data, a data lakehouse combines the flexibility of a data lake with the structured capabilities of a data warehouse—it's a hybrid solution.
Data lakes are perfect if you need to store diverse types of data—structured data such as customer records and unstructured data such as videos, IoT sensor streams, or social media posts. They're also optimised for advanced analytics, such as machine learning and predictive modelling, since analysts can draw directly from raw data without a lot of preprocessing.
As centralised storage hubs, data lakes are built for flexibility and scale. Here's how their adaptability can support your business:
Let's look at how data lakes are used in different industries.
Think of a data warehouse as a highly organised distribution centre. Just like a warehouse holds products in designated locations for easy retrieval, a data warehouse stores structured, processed data that are ready for analysis. Everything is labelled, categorised, and optimised for efficiency so that you can access the information you need at the drop of a hat.
Data warehouses are purpose-built for analysing large volumes of historical data. They support tasks such as tracking financial trends, monitoring performance metrics, and generating business forecasts. Unlike data lakes, which are built for flexibility, warehouses are ideal for accuracy and speed. Data warehouses have long been a cornerstone of enterprise data strategies, but modern solutions such as cloud-based warehouses have expanded their capabilities.
Need high-speed access to your data? Many businesses do, which is why data warehouses are so helpful. You will also enjoy perks such as:
Some of the key uses of data warehousing include:
When deciding between a data lake vs a data warehouse (or perhaps both), it helps to compare their characteristics side by side. Below is a breakdown of their key differences.
| Feature | Data Lake | Data Warehouse |
| Data Type | Stores raw, unstructured, and semi-structured data (e.g., IoT data, images). | Stores processed, structured data (e.g., sales records, customer addresses). |
| Users | Data scientists, engineers, and advanced analysts who want access to raw data. | Business users and analysts who need quick, reliable access to reports. |
| Schema Design | Schema on read—data is organised only when it's used for analysis. | Schema on write—data is cleaned and structured before entering. |
| Processing | Supports batch and real-time processing. | Primarily optimised for structured, batch processing. |
| Cost and Scalability | Lower storage costs; scales easily for massive datasets. | Higher costs due to processing and storage optimisation. |
| Security and Governance | Requires strong data governance to manage unstructured data access. | Security is usually built in. |
Each system has its strengths, and the choice depends on your business needs. In some cases, combining both—through solutions such as Salesforce Data 360—provides the best of both worlds.
Modern businesses no longer need to choose between a data lake and a data warehouse. Combining the strengths of data lakes and data warehouses allows you to create a powerful, unified approach to data storage and analysis. Together, they help you handle both real-time data needs and long-term strategic planning.
Platforms like Data 360 make this integration seamless by connecting to your data wherever it resides—whether in a lake, a warehouse, or a legacy system. Data 360 turns information into action by bringing together all your data into a single, usable model, and activating it across apps, AI agents, and experiences.
A data lake stores raw, unprocessed data, while a data warehouse organises and processes data before storing it. Data lakes are flexible and ideal for unstructured or semi-structured data, such as IoT streams or social media posts. Data warehouses are optimised for fast querying of structured data, so they tend to be most useful for reporting and analytics.
Not entirely. Data lakes excel at storing large, diverse datasets but lack the structure and speed that warehouses provide for operational reporting and BI. Many businesses find that combining both systems offers the best results.
Data warehouses remain essential, but hybrid systems like data lakehouses and platforms like Data 360 are gaining traction. These solutions combine the flexibility of a data lake with the structure of a warehouse.
If your organisation relies on unstructured data or needs to preserve information for machine learning and AI workflows, a data lake might be a better choice. It provides cost-effective storage and supports advanced analytics, which equips your data science teams with the tools they need.
Data warehouses are costlier because of the processing involved in cleaning and organising data before storage. This upfront investment gives you more speed and accuracy during analysis. Data lakes, on the other hand, store raw data, which makes them less expensive to scale but requires a bit more effort to extract insights.
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