The book predates the widespread adoption and maturity of major cloud-native database services like:
Amazon DynamoDB (Key-Value/Document): Hugely popular, serverless, highly scalable.
Google Cloud Datastore/Firestore (NoSQL Document): Serverless document databases.
Azure Cosmos DB (Multi-model): Microsoft's global-scale, multi-model database.
Amazon Aurora (Relational/Cloud-Native): Cloud-native relational database compatible with MySQL and PostgreSQL.
Snowflake, Google BigQuery, Amazon Redshift (Cloud Data panama phone number list Warehouses): Modern analytical powerhouses.
Absence of Vector Databases: A critical emerging paradigm for AI and machine learning, used for similarity search on high-dimensional data (e.g., Pinecone, Weaviate, Milvus). These were non-existent or nascent at the time of publication.
Absence of Distributed SQL / NewSQL: Databases like CockroachDB, YugabyteDB, and TiDB, which combine horizontal scalability with SQL and ACID guarantees, have gained significant traction.