BigQuery and Data Analytics

Data analytics is one of GCP's greatest strengths. Google pioneered technologies like MapReduce, Dremel, and Colossus that revolutionised how the world processes data. BigQuery — GCP's serverless data warehouse — is a direct descendant of these innovations.

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What Is BigQuery?

BigQuery is a serverless, petabyte-scale data warehouse designed for fast SQL analytics over large datasets.

Key Characteristics

Feature	Detail
Serverless	No infrastructure to manage — no clusters, no tuning
Petabyte scale	Analyse massive datasets in seconds
Standard SQL	Full ANSI SQL support
Columnar storage	Data stored in columns for fast analytical queries
Separation of compute and storage	Scale each independently
Built-in ML	Create machine learning models with SQL (BigQuery ML)

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Core Concepts

Datasets

A dataset is a container for tables, views, and routines. Datasets belong to a project and are located in a specific region.

Tables

Tables hold your data in rows and columns. BigQuery supports:

Table Type	Description
Native tables	Data stored in BigQuery's columnar format
External tables	Query data stored in Cloud Storage, Bigtable, or Drive
Views	Saved SQL queries that act as virtual tables
Materialised views	Precomputed views for faster queries

Schema

Each table has a schema defining columns and their types:

CREATE TABLE my_dataset.orders (
  order_id STRING,
  customer_id STRING,
  total NUMERIC,
  created_at TIMESTAMP
)
PARTITION BY DATE(created_at);

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Loading Data

You can load data into BigQuery from multiple sources:

Source	Method
Cloud Storage	Load CSV, JSON, Avro, Parquet, ORC files
Streaming	Insert rows in real time via the streaming API
Dataflow	ETL pipelines for transformation
Transfer Service	Scheduled loads from SaaS (Google Ads, YouTube, etc.)
Federated queries	Query external data in place without loading

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Querying Data

BigQuery uses standard SQL:

SELECT
  customer_id,
  COUNT(*) AS order_count,
  SUM(total) AS total_spent
FROM my_dataset.orders
WHERE created_at >= '2024-01-01'
GROUP BY customer_id
ORDER BY total_spent DESC
LIMIT 10;

Query Performance

BigQuery processes queries by scanning only the columns referenced in your query (columnar storage). To optimise performance and cost: