NoSQL for modern apps and AI: The future of Memorystore, Firestore, and Bigtable

Key Concepts

• Memorystore: A fully managed in-memory data store (Redis/Valkey) for microsecond latency.
• Bigtable: A high-performance, wide-column NoSQL database designed for massive scale.
• Firestore: A serverless document database with MongoDB compatibility.
• Valkey: An open-source Redis fork optimized for high throughput.
• Data Boost: A feature allowing analytical jobs to read directly from storage without impacting transactional performance.
• In-Memory Tier (Bigtable): A new caching layer that provides sub-millisecond latency and cost-efficient row lookups.
• Enterprise Plus Edition: A premium tier for Bigtable offering advanced features like cluster-level backups and increased storage limits.
• Agentic AI: A development paradigm where AI agents interact with databases to create, modify, and query data dynamically.

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1. Google Cloud NoSQL Portfolio Overview

Google’s non-relational database portfolio is designed to act as a bridge between applications and foundational AI models. The strategy focuses on three pillars:

• AI Integration: Native support for vector search, embeddings, and integration with Gemini Enterprise Agent platforms.
• Ecosystem Compatibility: Support for AI orchestration frameworks like LangChain and LlamaIndex.
• Managed Services: Providing fully managed, scalable, and highly available infrastructure.

2. Memorystore Innovations

Memorystore has seen significant updates, particularly with the adoption of Valkey 9.0:

• Performance: Valkey 9.0 offers 40% higher throughput via memory prefetching and 20% via zero-copy responses.
• New Node Types: Six new node types were introduced to cater to:
  • Dev/Micro workloads: Small-footprint, cost-effective shards.
  • Compute-intensive AI: High-CPU nodes for feature stores.
  • Large-scale: Double XL nodes supporting up to 27.5 TB per cluster.
• Migration: A new native online migration tool (in preview) uses cross-region replication to automate the cutover from self-managed Redis/Valkey to Memorystore.
• Security: New support for flexible CA management, token-based authentication, and upcoming ACL support.

3. Bigtable: Scaling and Intelligence

Bigtable is positioned as a "fast and flexible" store capable of handling exabyte-scale data.

• Key Statistics: Supports up to 7 billion queries per second (QPS) and 1.6 quadrillion rows in a single table.
• In-Memory Tier: A major launch that allows for 10x cheaper row lookups and up to 120k QPS per row, maintaining consistency with the transactional database without requiring application changes.
• Enterprise Plus Features: Includes cluster-level automatic backups, expanded Data Boost capabilities (now supporting SQL queries), and increased SSD tier limits (64 per node).
• Real-time Intelligence: New features include distributed counters, continuous materialized views, and asynchronous secondary indices.

4. Firestore: Serverless Document Database

Firestore is highlighted for its versatility, serving both weekend projects and enterprise-grade applications with five-nines availability.

• MongoDB Compatibility: Now GA, allowing users to migrate MongoDB workloads with minimal code changes.
• AI-Ready: Integrated into Google AI Studio, allowing natural language querying of database contents.
• New Capabilities: Full-text search (with phoneme/synonym support), change streams for ETL, and support for large documents (up to 16 MB).

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5. Case Study: Palo Alto Networks

Chetna Sulgiye detailed two major migrations:

A. Prisma SD-WAN (Firestore Migration)

• Challenge: High operational overhead from a self-hosted MongoDB cluster and fragmented data across GCP and external clouds.
• Solution: Migrated to Firestore (MongoDB compatibility mode).
• Outcome: 64% cost optimization, zero infrastructure management, and unified operational/analytical data architecture.

B. Advanced WildFire (Bigtable & Memorystore Migration)

• Challenge: Cassandra clusters suffered from high latency and performance degradation during compaction processes.
• Solution: Migrated to Bigtable for core metadata and Memorystore for high-velocity caching of security signatures.
• Outcome: 50% reduction in infrastructure costs and an 80–90% reduction in latency-related production issues.

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6. Synthesis and Conclusion

The session emphasizes that Google’s NoSQL portfolio is evolving to meet the demands of the "AI Age." By introducing features like the Bigtable In-Memory Tier and Firestore’s MongoDB compatibility, Google is lowering the barrier to entry for high-scale, low-latency applications. The transition from self-managed systems (like Cassandra or self-hosted MongoDB) to managed GCP services is presented as a low-risk, high-reward strategy that reduces operational overhead while significantly improving performance and cost-efficiency.