What is Data Lake Architecture?

The Core Problem: Traditional data warehouses force you to model data upfront. When you're dealing with hundreds of data sources generating terabytes per day, this creates bottlenecks. You can't load semi structured logs, JSON events from mobile apps, or third party data dumps without extensive transformation first. That transformation work becomes a blocker for every new data source. Data lake architecture solves this by separating storage from compute. Storage is object storage (like Amazon S3, Google Cloud Storage, or Azure Blob Storage) costing just a few dollars per terabyte per month. Data lands in raw form first, then multiple compute engines can process it independently. How It Works: Think of it as three distinct layers working together: First, the storage layer holds everything as files in object storage. A clickstream event might be a compressed JSON file partitioned by date. There's no database managing this, just files organized in folders like /raw/clickstream/2024-01-15/. Second, the compute layer runs processing jobs. These are temporary clusters (Spark, Presto, or similar) that spin up, read files from the lake, process them, write results back, then shut down. You pay only for compute time. Third, the catalog layer tracks metadata. Services like AWS Glue, Databricks Unity Catalog, or Google Data Catalog maintain a registry of what datasets exist, their schemas, locations, and owners. This makes the lake discoverable instead of a chaotic dump. The Key Innovation: The pattern decouples who produces data from who consumes it. A mobile team can drop raw logs without knowing what analytics queries will run. An ML team can train models on years of historical data without copying it to a specialized store. All because storage is cheap and shared, while compute scales independently.