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Designing Data Warehouses in BigQuery

Jul 4, 2024

Designing Data Warehouses in BigQuery

Overview

  • Discussion on the architecture of data warehouses in BigQuery
  • Comparison with traditional data warehouse design
  • Focus on key components that influence data model design

Types of Data Store Options

  1. Relational Databases
    • Highly structured, used for transaction processing
    • Difficult to scale
  2. NoSQL Databases
    • Semi-structured data
    • Easier to scale, eventual consistency
  3. Analytical Databases
    • Highly scalable, optimized for structured data and read-intensive workloads
    • BigQuery falls into this category

Traditional Data Warehouses vs BigQuery

  • Traditional: Tightly coupled storage and compute resources
  • BigQuery: Serverless, petabyte scale, and designed for efficient scanning
  • Use of SQL but optimized for analytical workloads, not transactions

BigQuery Features

  • BigQuery ML: Machine learning within the database using SQL
  • BigQuery BI Engine: In-memory OLAP cube for low-latency operations
  • BigQuery GIS: Geographical Information Systems support

BigQuery Architecture

  • Storage (Colossus): Highly reliable, distributed global file system used for persistent storage
  • Compute (Dremel and Borg)
    • Dremel: Manages SQL query execution in a multi-tenant system
    • Borg: Predecessor to Kubernetes, manages job scheduling and resources
  • Jupiter: High throughput, petabit networking for low-latency data movement
  • Capacitor: Columnar storage format optimized for nested and repeated fields, handles semi-structured data efficiently

Best Practices for Data Modeling in BigQuery

  • BigQuery is optimized for scanning, not indexing
  • Google charges based on the amount of data scanned
  • Partitioning: Reduces data scanned by dividing large tables into smaller, ordered chunks
  • Clustered Tables: Further reduce scanning by keeping data in order
  • Denormalization: Use nested and repeated fields to avoid joins, especially for large tables

Partitioning Types

  • Ingestion Time: Data loaded into date-based partitions automatically
  • Date/Time Stamp: Partitions based on existing date/time columns
  • Integer Range: Uses integer columns for partitions; not compatible with legacy SQL

Clustered Tables

  • Data sorted based on column values
  • Improves query performance and reduces scanning
  • BigQuery automatically re-clusters data in the background

Nested and Repeated Fields

  • Optimized by Capacitor to efficiently store semi-structured data
  • Can store arrays and hierarchical data with efficient access

Design Takeaways

  • Always consider reducing the amount of data scanned
  • Use partitioning, clustering, and denormalization effectively
  • Leverage nested and repeated fields to optimize query performance

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