Hadoop-Based Big Data Governance Platform for Multi-Source Heterogeneous Systems: Architecture Design and Cross-Platform Integration

The fast growth of big data in various industries has resulted in data ecosystems made up of many different and spread-out sources.Sufficient governance in such surroundings is needed to maintain the accuracy of data, follow regulations, ensure security, and improve operations.This study explains the process of creating, deploying, and assessing a Hadoop-based Big Data Governance Platform meant for use in multi-source and heterogeneous systems.The suggested architecture relies on the modular components of Hadoop, such as HDFS, Hive, Spark, Atlas, and Ranger, to manage metadata, enforce rules, follow data lineage, and integrate data with different platforms.Hadoop allows data to be streamed in real time and in batches from relational, NoSQL, and cloud databases using Kafka, Flume, and Sqoop.Testing with ten nodes in a Hadoop cluster and a variety of datasets shows that the platform provides thorough metadata, follows all policies consistently, is easy to track data lineage, and performs queries quickly without causing much overhead.What's more, the use of visual analytics and profiling greatly supports data quality and ensures that Sisense works with PostgreSQL, MongoDB, and S3.Evidence shows that Hadoop-based management ensures that companies can handle the challenges and variety involved in big data governance.