Data lake vs. data mesh: Which one is right for you?

Organizations seeking to analyze enterprise data face challenges such as data quality governance, ETL pipeline dependencies, and a shortage of data science and engineering talent.

A solution is to distribute data in a mesh architecture instead of a centralized data lake.

The data mesh approach promotes self-service and treating data as a product, where the data consumer is responsible for domain-driven data pipelines.

However, a mix of distributed and centralized approaches may be ideal, and it's important to have standard contracts for data producers and consumers within the mesh architecture.

Cloud-based modern data platforms like ChaosSearch can enable organizations to ingest, index and analyze large volumes of data in real-time without the need for a complex ETL pipeline.