Big data architectures for scalability
Big data architectures for scalability Big data projects grow fast. To scale effectively, many teams separate storage from compute and combine batch and streaming processing. A solid architecture supports data from raw to ready analytics while staying reliable and cost-conscious. Common patterns include a data lake for raw data, a data warehouse or lakehouse for curated analytics, and streaming services for real-time insights. Storage often uses object stores like S3 or GCS, while compute runs on scalable clusters or serverless jobs. By decoupling these layers, you can grow one side without forcing a full rewrite. ...