Navigating cloud databases in the AI era

Key Concepts

• Cloud Databases
• Data Usage Patterns
• Relational Databases (SQL)
• NoSQL Databases
• Transactional Workloads
• Data Integrity
• Scalability
• Global Distribution
• Strong Consistency
• Document Databases
• Wide Column Store
• Real-time Analytics
• Data Warehouse
• Multicloud Analytics
• AI Integration
• Vector Databases
• Embeddings
• Contextual Search

Choosing the Right Database: A Google Cloud Perspective

The Misconception of "One Size Fits All"

The biggest misconception developers have is believing that one database can handle all their needs. Forcing data into a familiar database often leads to:

• Performance bottlenecks
• Scaling challenges
• General inconvenience

Navigating the Database Options in Google Cloud

Google Cloud offers a wide array of database options, including:

• Cloud SQL
• AlloyDB
• Spanner
• Spanner Graph
• Firestore
• Bigtable
• BigQuery

To navigate this, the speaker emphasizes starting with data usage patterns.

Relational Databases: Cloud SQL and AlloyDB

For transactional workloads and relational data, Cloud SQL and AlloyDB are suitable choices. These offer familiar relational databases in a fully managed and scalable service.

• Use Cases: E-commerce, banking, applications where data integrity is paramount.
• Cloud SQL: Supports common engines like PostgreSQL, MySQL, and SQL Server.
• AlloyDB: A fully managed PostgreSQL-compatible database built for speed and scale. A key decision factor is how fast reads and writes need to be.

Globally Distributed Applications: Cloud Spanner

For applications needing planet-scale reach and zero downtime, Cloud Spanner is the best option.

• Use Cases: Financial systems, supply chain management, applications requiring strong consistency across geographically dispersed regions.
• Spanner Graph: Supports storing data as graphs within Spanner, supercharging AI applications with graph capabilities at virtually unlimited scale. It unlocks relational, graph, full text and vector search all in one database.

NoSQL Document Database: Firestore

If data is not entirely structured, Firestore, a NoSQL document database, is a good choice. Data is stored in the form of documents organized as collections.

• Use Cases: Mobile and web applications.
• Features: Easy to use, scales automatically, provides offline support.

NoSQL Wide Column Store: Bigtable

Google Cloud Bigtable is a NoSQL wide column store that functions as a massively scalable sorted key-value map.

• Use Cases: Low-latency NoSQL database service for machine learning and real-time analytics needs.
• Features: Fully managed, easy integration with the streaming ecosystem, built-in real-time capabilities.

Data Warehouse for Analytics: BigQuery

BigQuery is a serverless data warehouse designed to handle massive datasets and complex queries.

• Use Cases: Storage, analytics, exploration, and business intelligence insights (both generative and agentless).
• Features: Multicloud analytics (query data in AWS S3 and Azure Blob Storage), federated queries (query data from Cloud SQL, AlloyDB, or Firestore).

AI Integration Across Google Cloud Databases

Google Cloud is infusing AI across its database portfolio.

• Built-in AI-powered management.
• Direct integration with Vertex AI models: Build generative AI and agentic applications directly from where data is stored.
• Vector Databases: Store, index, and query embeddings for contextual search needs.

Getting Started and Experimentation

The speaker encourages developers to:

• Start with their data.
• Experiment with different approaches.
• Understand their data model, access patterns, and scaling requirements.

Codebasa Season 11

For hands-on experience, developers can register for Codebasa Season 11, a guided instructor-led virtual session in mid-September. Registration is available at codebasa.dev.

Conclusion

Choosing the right database in Google Cloud requires understanding data usage patterns, considering the trade-offs between different database types (SQL vs. NoSQL), and leveraging the AI capabilities integrated within the platform. Experimentation and a clear understanding of application requirements are crucial for making the optimal choice.