How to Minimize Hadoop Risk with Data Observability

Hadoop is a popular open-source computing framework for processing large datasets, created in 2005 by the Apache Foundation. It is widely used in industry for big data analytics and other data-intensive applications due to its ability to scale horizontally and handle large amounts of data. However, it also presents challenges such as complexity, cost, steep learning curve, and potential security risks. To minimize these risks, data engineering teams should keep their Hadoop clusters up-to-date, secure access to the cluster, use encryption for sensitive data, implement role-based access control, and monitor the cluster regularly. Migration away from Hadoop is another option, with alternatives such as rebuilding on-premises Hadoop clusters in the public cloud or migrating to a modern, cloud-native data warehouse. The Acceldata Data Observability platform can help manage Hadoop environments and ensure a successful migration by providing powerful performance management features and integrating with various environments.