The "seven" in the title is a symbolic number. The database world today is even more diverse than when the book was first written. Beyond the core paradigms, we now see:
Serverless Databases: Databases that automatically scale and manage themselves, billed purely on usage (e.g., DynamoDB, Aurora Serverless, BigQuery).
Time-Series Databases (Specialized): Beyond general column-stores, dedicated time-series databases like InfluxDB, TimescaleDB, and KDB+ (as discussed previously) offer highly optimized solutions for time-stamped data.
Search Engines (as Databases): Elasticsearch and Apache Solr are often qatar phone numbers list used as primary data stores for search-driven applications.
Streaming Databases: Technologies like Apache Kafka with ksqlDB or Flink SQL allow for real-time processing and querying of data streams.
Hybrid Transactional/Analytical Processing (HTAP): Databases designed to handle both OLTP (transactional) and OLAP (analytical) workloads efficiently in a single system.
Data Lakehouses: Architectures that combine the flexibility of data lakes with the structure and governance of data warehouses.
Vector Databases: The newest frontier, crucial for AI applications that rely on similarity search with high-dimensional vector embeddings.
The principles taught in "Seven Databases in Seven Weeks" – understanding trade-offs, data models, and use cases – are precisely what enable developers to navigate this increasingly complex and specialized landscape.