Big Data Fundamentals: Storage, Processing, and Analytics
Big Data Fundamentals: Storage, Processing, and Analytics Big data is more than a buzzword. It describes datasets that are large, fast, or varied enough to push traditional tools beyond their limits. In practice, success depends on three pillars: storage, processing, and analytics. When these fit together well, teams move from raw data to actionable insight with confidence. Storage Storage choices shape cost, speed, and governance. A typical setup uses a data lake for large volumes of raw data and a data warehouse for clean, structured queries. Data lakes use object storage and support flexible formats like JSON, Parquet, or Avro. Data warehouses optimize for fast SQL queries and consistent schemas. A good governance layer, with metadata, lineage, and strict access controls, keeps data reliable as teams grow. In practice, teams blend these layers to balance flexibility and speed. ...