As modern organizations struggle to deal with constantly growing quantities of enterprise data, many are reevaluating their to determine the optimal approach for delivering business insights and analytics at scale.
With this goal in mind, most organizations are looking for ways to analyze data without having to spend additional time and resources on moving or transforming it. As a result, we’ve seen data lake and data mesh architectures rise in popularity; these approaches promise to fulfill the accessibility, consistency, data quality, and data governance requirements organizations need to achieve data analytics at scale.
But the question remains: which of these two solutions is better? Whether keeping data distributed in a data mesh, or centralizing it within a data lake, every organization should consider a unique set of criteria to determine the best solution for their business.
The rise of Big Data and the challenges it brought to light for traditional enterprise solutions inspired James Dixon to coin the term “data lake” over a decade ago (2010). At their core, the best data lake solutions promise to eliminate data silos by serving as a single landing repository that centralizes, organizes, and protects large amounts of data from multiple sources. It follows a schema-on-read approach and can store data that is structured, semi-structured, and unstructured, typically on cloud storage platforms such as AWS S3.
These flexible storage solutions have become increasingly popular among modern enterprises, but one common misconception is that they inherently include analytic features. In order to perform indexing, transformation, querying, and analytics, the data lake must be connected to a combination of other cloud-based services and software tools. In a typical data lake architecture, a self-service data analytics engine will sit on top of a cloud-based data repository. That’s when an organization can realize the true benefits of a data lake and act on the full value of their data resources.
Until recently, data warehouses and data lakes represented the two leading solutions for enterprise data management. But a new approach has risen over the last year – the concept of a “data mesh.” In fact, it’s becoming one of the top buzz words being discussed more every day. Thoughtworks defines a data mesh as “a shift in a modern distributed architecture that applies platform thinking to create self-serve data infrastructure, treating data as the product.
