If you’ve heard the debate among IT professionals about data lakes versus data warehouses, you might be wondering which is better for your organization. You might even be wondering how these two approaches are different at all.

When you’re first learning about data lakes, you may initially feel like you’ve been down this path before. There are, however, major differences to each approach.

What is a data lake?

A data lake is a method of data storage. What makes this approach unique is that all of the data is stored in its native format. This means that data in the lake might include everything from highly structured files to completely unstructured data such as videos, emails and images.

What is a data warehouse?

A data warehouse is a place where data is stored in a structured format. To build on the metaphor, think of this as a warehouse for storing bottled water. The data is prepared and formatted for easy use. This also means information usually needs to be reformatted before it enters the warehouse.

Benefits of a data lake

Simplified data access. With a well-built data lake, you can extend access to more users, including not only data scientists but also line-of-business users and application developers. User-defined access enables them to work with data from multiple sources across the organization, on premises or in the cloud.

Enhanced agility for data users. A data lake equipped with the proper tools can enable ad hoc queries and real-time analysis while eliminating the time and costs involved with IT assistance.

Reduced costs. Data lakes use commodity hardware, enabling users to scale them cost effectively without excessive capital expenditures. A data lake can even serve as a repository for older data that would otherwise take up capacity in more expensive warehouses. By providing users direct access to data, data lakes can also help users avoid the cost of IT assistance. In addition, implementing proper data governance capabilities for a data lake helps users avoid costs associated with correcting data quality issues.

Improved decision making. Analyzing data drawn from more sources lets a user increase the depth of insights and enhance the accuracy of results. Governance features that help ensure data is relevant and trustworthy.

Why a data lake might not work

Despite the benefits, there are a few cases where a data lake might not work as planned:

No business case. Without clearly articulating and understanding how a data lake will benefit the business, a user might fail to acquire the approvals and buy-in needed to move forward.

Poor integration. A data lake can supplement or, in some cases, replace a data warehouse. But unless there is a plan for integrated data management, an organization might not achieve the full value a data lake can deliver.

Technology choices that don’t fit. Selecting the wrong platform or tools can add significant complexity and cost to implementation and ongoing management.

Inadequate governance and security. Enterprise-grade governance and security strategies are critical for protecting sensitive information, maintaining compliance and enabling users to take full advantage of data.

No long-term vision. A data lake requires a long-term commitment plus planning to accommodate continued data growth.

Data lakes are not simply data warehouses revisited. They represent a unique approach for organizations to achieve major business goals if implemented properly.