Data Storage & Retrieval at Scale

AI workloads place unusual demands on storage systems. Training datasets can be enormous - hundreds of terabytes or more - and need to be read sequentially at high throughput. Feature serving requires low-latency random access to individual records. Vector databases need to support similarity search across millions of high-dimensional embeddings. No single storage system handles all of these requirements well, so most AI architectures use multiple specialised stores. Object storage like Amazon S3 or Google Cloud Storage handles bulk training data cheaply and durably. Relational databases or key-value stores serve structured features with low latency. Vector databases like Pinecone, Weaviate, or pgvector support the similarity searches that power retrieval-augmented generation and semantic search. The rise of the data lakehouse - combining the flexibility of a data lake with the structure of a data warehouse - has simplified some of this, letting organisations store raw and processed data in one place while supporting both analytical queries and AI workloads. Choosing the right storage architecture involves balancing cost, performance, and operational complexity. Over-engineering is a real risk for smaller teams, but under-investing in storage infrastructure will bottleneck your AI efforts as data volumes grow.