Just a few years ago, an enterprise data lake was considered an essential part of big data and analytics processes. But recently, even more organizations are using data lakes to support advantages like easier inclusion of different sources and overall flexibility.

What are the differences between data lakes and data warehouses, and what advantages can a data lake provide? Let’s take a look:

Defining data lakes and warehouses

As explained by Datafloq Marketing Vice President Kelly Schupp, there are considerable differences between data warehouses and data lakes:

  • Warehouses, sometimes known as enterprise data warehouses (EDWs) accepts data from a range of different enterprise applications but requires that data be formatted according to the EDW’s specific schema. Because EDWs were created with the purpose of collecting data, ensuring quality and supporting specific enterprise data models, the questions that can be answered leveraging a data warehouse are somewhat limited. Although data warehouses have their place, this can create challenges for more in-depth or complex analysis.
  • Lakes¬†allow users to input information from different sources in the data’s native form, and don’t require preparation or processing to support a certain schema. Data lakes are characterized by their single shared data repository, orchestration and job scheduling capabilities, and the availability of applications or workflows that can consume, process and work with the data.

“By allowing data to remain in its native format, a far greater – and timelier – stream of data is available for analysis,” Schupp noted of data lakes.

Benefits of using a data lake

Compared to a data warehouse, a data lake can offer several key benefits for collection and analysis:

  • Better usability for analysts: Lakes include tools and capabilities not typically available with an enterprise data warehouse, including the aforementioned orchestration, job scheduling and other processing features.
  • Up-to-date sources: Because data doesn’t have to conform to a specific schema, analysts can input sources more quickly and leverage up-to-date information within the analysis.
  • Scalability: Data Science Central contributor Kumar Chinnakali noted that data lakes supported by Hadoop are particularly horizontally scalable and can handle considerable amounts of data growth.
  • Flexibility: This is one of the greatest benefits of working with a data lake. Users can store unlimited different types of data, including structured and unstructured sources. And because data lakes can support storage of raw data, information can be refined as users improve their insights and understanding.

Unifi works well with both data warehouses and data lakes, but to find out more about the advantages of using a data lake – including how it can support compliance – connect with the data experts at Unifi Software today.