What is Google BigQuery?

Learn the basics of Google BigQuery, including its features, functionality, SQL examples, architecture, and more.

Team Ablaze Collective

September 19, 2024. 10 MIN READ

Contents

1. Overview

2. Architecture

3. BigQuery vs. MapReduce

4. Federated Querying

5. BigQuery Features

6. Functionality

7. Partitioning & Clustering

8. Geospatial Data

9. BigQuery UDFs

10. Arrays

11. Information Schema

12. Table Sampling

13. BigQuery Security

14. BigQuery Pricing

15. Expert Help

16. FAQ

Overview

Google BigQuery is a fully managed, serverless data warehouse with built-in features like machine learning, geospatial analysis, and business intelligence tools. Its scalable, distributed analysis engine supports efficient querying and data manipulation, with minimal warehouse management required.

BigQuery uses a columnar data format optimized for analytical queries and adheres to database transaction semantics (ACID). It also provides centralized data management secured through Google’s Identity and Access Management (IAM), offering flexible security options, whether following Google’s best practices or applying a more granular approach.

💡 Learn how Ablaze Collective can connect BigQuery to analytics platforms such as Tableau through our Ablaze BaaS (Back-End as a Service) app. Learn more here.

Architecture

Externalization of Dremel

Google BigQuery is built on Dremel, a distributed system developed by Google for interactively querying large datasets. BigQuery brings Dremel’s core features to third-party developers via a REST API, command-line tool, and Web UI. With BigQuery, billions of rows can be scanned in seconds without indexing.

The system’s columnar storage and tree architecture enable hyper-efficient, parallel processing, making BigQuery an incredibly scalable and powerful solution for managing large-scale data.

Columnar Storage

BigQuery’s column-based storage optimizes query performance by reading only the columns needed for specific queries. This columnar approach, combined with partitioning and clustering, allows for faster, more cost-efficient data analysis.

Tree Architecture

BigQuery uses a tree architecture to distribute query execution across thousands of machines, enabling massively parallel processing. This design allows BigQuery to process large datasets at high speeds, aggregating results from multiple nodes for efficient querying.

BigQuery vs. MapReduce

BigQuery and MapReduce (known through its open-source implementation, Hadoop) are both distributed computing technologies. However, they serve different purposes. BigQuery is an interactive tool for analyzing structured data using SQL, while MapReduce is a programming framework better suited for batch processing and handling unstructured data.

Federated Querying

BigQuery allows federated querying, enabling SQL queries to run directly against external data stored in Google Cloud Storage (e.g., CSV or JSON files). This reduces the need for complex data pipelines and allows on-the-fly transformations. While federated querying is flexible, it has limitations related to regions, quotas, and performance.

💡 Explore how Ablaze BaaS simplifies data pipelines by integrating your data sources for seamless analysis.

BigQuery Features

BI Engine

BigQuery BI Engine provides fast, in-memory analysis with subsecond response times and high concurrency. It integrates with Google tools like Data Studio and Looker, as well as popular third-party platforms.

BigQuery ML

BigQuery ML brings machine learning to SQL, allowing users to create and execute ML models directly within BigQuery. This feature makes machine learning accessible to data analysts without extensive programming knowledge.

Connected Sheets

BigQuery integrates with Google Sheets, making data accessible to users who prefer spreadsheet-based analysis. This connection simplifies data manipulation and visualization for financial analysts and stakeholders.

Functionality

BigQuery supports several table types, including managed tables, external tables, standard views, and materialized views. Each table type offers unique features for optimizing data storage and querying.

Partitioning & Clustering

Partitioning and clustering are critical for optimizing query performance in BigQuery.

Partitioning

BigQuery supports partitioning based on date or integer fields, allowing queries to scan only the relevant partitions. This optimization reduces both costs and query time.

Clustering

Clustering organizes rows by grouping similar values in clustered columns. Clustering works well when data is frequently filtered or grouped by certain attributes, further improving query performance.

Geospatial Data

BigQuery stands out for its ability to handle geospatial data. Using the GEOGRAPHY data type, BigQuery can store, manipulate, and analyze spatial data, making it ideal for businesses dealing with maps, mobile devices, location sensors, and satellite data.

BigQuery UDFs (User-Defined Functions)

BigQuery supports both SQL and JavaScript in user-defined functions (UDFs), allowing users to create reusable logic for complex queries. UDFs can be temporary or persistent and are a powerful tool for organizing and simplifying query logic.

Arrays

BigQuery’s array functions allow users to store ordered lists of values in a single row. Arrays are useful for storing repeated values and generating data. BigQuery also offers array manipulation functions, which can improve performance and simplify queries.

Information Schema

BigQuery’s Information Schema provides metadata about datasets, tables, and queries. It helps users track resource usage, session data, and table metadata, offering valuable insights into data management and usage.

Table Sampling

Table sampling in BigQuery allows users to query random subsets of data from large tables. This feature reduces query costs and improves speed for ad-hoc analysis. Sampling is particularly useful when analyzing large datasets that don’t require a full table scan.

BigQuery Security

BigQuery is built on Google’s Identity and Access Management (IAM) framework, which ensures robust security. Access to data is controlled via identity, role, and resource permissions, allowing administrators to manage access at a granular level.

💬
“Google Cloud Platform has made significant progress in data security, offering both column-level and row-level access control in BigQuery.” — Jeffrey Breen, Chief Product Officer, Protegrity

BigQuery Pricing

Analysis Pricing

BigQuery offers two pricing models:

1. On-Demand: $5 per TB of data scanned (the first TB per month is free).

2. Flat-Rate: Subscription-based pricing for users with large or enterprise-level data needs.

Storage Pricing

BigQuery charges for active storage ($0.20/GB) and long-term storage ($0.10/GB). The first 10 GB per month is free.

Expert Help

For organizations looking to leverage Google BigQuery as part of their data strategy, Ablaze Collective can provide fully managed data pipelines through Ablaze BaaS, integrating data from multiple sources into analytics platforms like Tableau. We simplify data migration, ETL/ELT processes, and analysis, making BigQuery even more powerful.

FAQ

When should I use BigQuery?
Use BigQuery as a cloud data warehouse for large-scale structured data storage and analysis.

Is Google BigQuery a database?
No, BigQuery is a managed cloud data warehouse with more functionality than a traditional database.

What’s the difference between BigQuery and MapReduce?
BigQuery is optimized for SQL-based analytics and structured data, while MapReduce is a framework for batch processing unstructured data.

Is BigQuery free?
BigQuery offers a free trial and limited free usage, but it is a paid service that scales with usage.