Data Mesh vs Data Lake: Understanding the Differences

Data mesh and data lake are two distinct approaches to data management, with data mesh decentralizing data ownership and responsibilities across individual domains, while data lakes serve as centralized repositories for vast amounts of raw data. Data mesh promotes a collaborative and scalable environment by allowing domains to independently manage their data with federated computational governance, enhancing accessibility and compliance. In contrast, data lakes face challenges related to governance, scalability, and reliance on centralized teams, which can hinder timely responses to business needs. Although data lakes facilitate enterprise-grade analytics and support complex data science workflows, they often lead to unwieldy and costly pipelines as they grow. Starburst's implementation helps address these challenges by enabling efficient data querying and reducing ETL processing costs, thus empowering analysts and data scientists with quick access to valuable insights.