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BigQuery

Deploying and managing Google BigQuery for enterprise data analytics

Google BigQuery is a fully-managed, serverless data warehouse that enables super-fast SQL queries using the processing power of Google's infrastructure. It allows you to analyze large datasets with high performance and cost efficiency.

Key Features

  • Serverless: No infrastructure to manage or provision

  • Petabyte Scale: Query petabytes of data with ease

  • Separation of Storage and Compute: Pay only for the storage you use and the queries you run

  • Real-time Analytics: Stream data for real-time analysis and ML

  • Geospatial Analysis: Built-in support for geographic data types

  • ML Integration: Train and run ML models directly using SQL with BigQuery ML

  • BI Engine: In-memory analysis service for sub-second query response

  • Federated Queries: Query data from external sources without copying

  • Data Governance: Column-level security and dynamic data masking

  • Cost Controls: Custom quotas and intelligent pricing models

Deploying BigQuery with Terraform

Basic Dataset and Table Creation

resource "google_bigquery_dataset" "default" {
  dataset_id                  = "example_dataset"
  friendly_name               = "test"
  description                 = "This is a test dataset"
  location                    = "US"
  default_table_expiration_ms = 3600000  # 1 hour
  
  labels = {
    env = "default"
  }
  
  access {
    role          = "OWNER"
    user_by_email = google_service_account.bq_admin.email
  }
  
  access {
    role          = "READER"
    group_by_email = "analytics-team@example.com"
  }
}

resource "google_service_account" "bq_admin" {
  account_id   = "bq-admin"
  display_name = "BigQuery Administrator"
}

resource "google_bigquery_table" "default" {
  dataset_id = google_bigquery_dataset.default.dataset_id
  table_id   = "example_table"
  
  time_partitioning {
    type = "DAY"
    field = "timestamp"
  }
  
  clustering = ["customer_id", "region"]
  
  schema = <<EOF
[
  {
    "name": "customer_id",
    "type": "STRING",
    "mode": "REQUIRED",
    "description": "Customer identifier"
  },
  {
    "name": "timestamp",
    "type": "TIMESTAMP",
    "mode": "REQUIRED",
    "description": "Event timestamp"
  },
  {
    "name": "region",
    "type": "STRING",
    "mode": "NULLABLE",
    "description": "Customer region"
  },
  {
    "name": "amount",
    "type": "FLOAT",
    "mode": "NULLABLE",
    "description": "Transaction amount"
  }
]
EOF

  deletion_protection = false
}

Advanced Configuration with Authorized Views and Row-Level Security

Managing BigQuery with gcloud CLI

Creating Datasets and Tables

Running Queries

Managing Permissions

Real-World Example: E-commerce Analytics Platform

This example demonstrates a complete data analytics platform for an e-commerce company:

Step 1: Infrastructure Setup with Terraform

Step 2: Set up Data Loading Pipelines

Create a Cloud Function to load data from various sources into BigQuery:

Step 3: Create Business Intelligence Dashboard using Looker Studio

  1. Set up a connection to BigQuery data marts

  2. Create a dashboard that pulls from the data marts:

Best Practices

  1. Data Organization

    • Structure data in datasets by function (raw, staging, production)

    • Use consistent naming conventions for datasets and tables

    • Consider data lifecycle management with table expiration

    • Implement a multi-layer architecture (bronze, silver, gold)

  2. Performance Optimization

    • Partition tables by date fields for time-series data

    • Apply clustering for frequently filtered fields

    • Use materialized views for common query patterns

    • Consider BigQuery BI Engine for dashboarding workloads

    • Write efficient SQL (avoid SELECT * and unnecessary JOINs)

  3. Cost Management

    • Set up billing alerts and quotas

    • Use flat-rate pricing for predictable workloads

    • Apply table partitioning to reduce scan sizes

    • Cache query results when possible

    • Consider reservations for consistent usage

  4. Security and Governance

    • Implement column-level security for sensitive data

    • Use authorized views to control access to underlying data

    • Apply row-level security for multi-tenant data

    • Enable column-level encryption for PII

    • Use VPC Service Controls for additional network security

  5. Operational Excellence

    • Monitor query performance with INFORMATION_SCHEMA views

    • Schedule routine maintenance jobs

    • Set up proper logging and monitoring

    • Implement data quality checks

    • Document data lineage

Common Issues and Troubleshooting

Performance Problems

  • Check for missing partitioning or clustering

  • Optimize JOIN operations (consider denormalizing)

  • Review query plans for bottlenecks

  • Use approximate aggregation functions for large datasets

  • Consider materialized views for frequent queries

Cost Issues

  • Monitor with BigQuery audit logs

  • Review largest queries by bytes processed

  • Control who can run queries (IAM roles)

  • Set user quotas to limit spending

  • Optimize storage with appropriate compression

Data Quality Problems

  • Implement data validation queries

  • Set up streaming inserts error handling

  • Create data quality dashboards

  • Use BigQuery Data Quality Services

Access Control Issues

  • Audit existing permissions with IAM Policy Analyzer

  • Use groups instead of individual permissions

  • Implement principle of least privilege

  • Document access policies

  • Regularly review and prune permissions

Further Reading

PreviousBigtableNextVPC (Virtual Private Cloud)

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