This article was translated using AI.

Takeaways from Google Cloud’s Big Series event focused on BigQuery, BigLake, and modern data platforms.


BigQuery Highlights

  • Enterprise data warehouse with petabyte-scale storage/queries (exabyte scale coming).
  • Standard SQL interface; easy for analysts to adopt.
  • Fully managed, serverless (slot-based execution).
  • Zero-ops, high durability, encrypted at rest and in transit.
  • Streaming ingestion enables near real-time analytics.

Evolution:

  • 1990s: Data Warehouses → 2000s: BI Foundations → Today: Data Analytics → Future: AI foundation.

Fully Managed

Google handles infrastructure; users focus on analytics. Default quota is 2,000 slots, and you can request more. Pricing remains competitive—BigQuery is often called Google’s “killer service.”

Sharing, Not Copying

GCP promotes secure data sharing rather than duplication. BigQuery supports simultaneous access without replicas.

Managed Storage

  • Data is replicated across multiple zones (3–4 by default).
  • Compression available, but billing uses uncompressed size (negotiable at PB scale).
  • Regional service—cross-region transfers incur charges.

Why Dynamic Slot Allocation Works

Compute and storage are decoupled, yet tightly integrated for performance.

Pricing

  • Analysis: based on data scanned.
  • Storage: active storage (<90 days) vs. long-term (>90 days, cheaper).
  • Loading: free for ETL ingest.
  • Free tiers: trial, monthly free quotas (10 GB storage, 1 TB query), free operations.

Workload Management

  • Slot = bundle of CPU, RAM, network.
  • On-demand pricing auto-assigns slots per query; flat-rate reservations guarantee capacity.
  • Idle reserved slots are auto-shared (disable via CLI if needed).
  • Use flex slots for temporary peaks.

Monitoring

BigQuery resource charts, Cloud Monitoring, and INFORMATION_SCHEMA tables help track usage/costs.

Machine Learning

BigQuery continues to expand built-in ML capabilities—easy ML atop warehouse data.

Data Mesh & Modern Platforms

Traditional centralized teams can’t keep pace; data needs outstrip capacity, leading to “data chaos.” Solution: treat data as a product and empower domain teams.

Key traits:

  • Domain-oriented decentralization.
  • Self-serve data platforms.
  • Federated governance.
  • High-quality datasets discoverable and trusted.

BigLake + DataPlex

  • BigLake: unified lakehouse (warehouse + lake) supporting structured and semi-structured data, fine-grained security, multi-cloud governance, performance acceleration.
  • DataPlex: centralized management and governance for distributed data (AI-powered cataloging, policy enforcement).

BigLake integrates tightly with BigQuery and GCS, enabling modern lakehouse architectures on GCP.


Bottom line: Modernize your data platform with BigQuery + BigLake + DataPlex to empower domain teams while maintaining centralized governance.***