"The core insight: Not all data is created equal The fundamental problem with traditional document management systems is that they treat metadata and content as a single unit. When a user searches for "all employment contracts for customer X," the system must wade through both the searchable attributes and the heavyweight file content, even though the search only needs the metadata."
"I realized that these two types of data have completely different performance characteristics. Metadata operations are classic Online Transaction Processing (OLTP) workloads: frequent, small, latency-sensitive transactions. Content operations are the opposite: infrequent, large, bandwidth-intensive transfers that can tolerate higher latency. By separating these workloads, I could optimize each independently. Metadata went into a high-performance NoSQL database - choosing from options like Amazon DynamoDB, Google Cloud Firestore or Azure Cosmos DB based on your cloud provider - configured in on-demand mode for automatic scaling."
Treat documents as two distinct workloads: metadata and content. Metadata demands OLTP characteristics—frequent, small, latency-sensitive queries—while content involves infrequent, large, bandwidth-heavy transfers tolerant of higher latency. Store metadata in a high-performance NoSQL database (examples: Amazon DynamoDB, Google Cloud Firestore, Azure Cosmos DB) configured for on-demand automatic scaling. Store document content in commodity cloud object storage (examples: Amazon S3, Google Cloud Storage, Azure Blob). Optimize each layer independently to achieve predictable sub-300ms query times, horizontal scalability, reduced operational complexity, and lower costs compared with monolithic document storage architectures.
Read at InfoWorld
Unable to calculate read time
Collection
[
|
...
]