Google BigQuery: A Game-Changing Data Warehousing Solution

Take Data Warehousing Skills to The Next Level With The Powerful Google BigQuery Platform | ProjectPro

Google BigQuery: A Game-Changing Data Warehousing Solution
 |  BY Badr Salah

Tired of relentlessly searching for the most effective and powerful data warehousing solutions on the internet? Search no more! This blog is your comprehensive guide to Google BigQuery, its architecture, and a beginner-friendly tutorial on how to use Google BigQuery for your data warehousing activities.


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Did you know ?

BigQuery can process upto 20 TB of data per day and has a storage limit of 1PB per table. 

Sounds exciting to learn more about Google BigQuery, read on!

BigQuery

 

With the global cloud data warehousing market likely to be worth $10.42 billion by 2026, cloud data warehousing is now more critical than ever. Cloud data warehouses offer significant benefits to organizations, including faster real-time insights, higher scalability, and lower overhead expenses. These benefits compel businesses to adopt cloud data warehousing and take their success to the next level. Some excellent cloud data warehousing platforms are available in the market- AWS Redshift, Google BigQuery, Microsoft Azure, Snowflake, etc. Google BigQuery holds a 12.78% share in the data warehouse market and has been rated a leader by Forrester Wave research in 2021, which makes it a highly popular data warehousing platform. This blog presents a detailed overview of Google BigQuery and its architecture. What’s more? It will also cover a step-by-step Google BigQuery tutorial to help you get started with your data warehousing solutions.

What is Google BigQuery?

What is Google BigQuery

 

Since its public release in 2011, BigQuery has been marketed as a unique analytics cloud data warehouse tool that requires no virtual machines or hardware resources. BigQuery is a highly scalable data warehouse platform with a built-in query engine offered by Google Cloud Platform. It provides a powerful and easy-to-use interface for large-scale data analysis, allowing users to store, query, analyze, and visualize massive datasets quickly and efficiently. With BigQuery, users can process and analyze petabytes of data in seconds and get insights from their data quickly and easily.

Moreover, BigQuery offers a variety of features to help users quickly analyze and visualize their data. It provides powerful query capabilities for running SQL queries to access and analyze data. Users can also use the BigQuery web UI to run queries, load data, stream data, etc. It is also possible to use BigQuery to directly export data from Google SaaS apps, Amazon S3, and other data warehouses, such as Teradata and Redshift. Furthermore, BigQuery supports machine learning and artificial intelligence, allowing users to use machine learning models to analyze their data.

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BigQuery Storage

BigQuery leverages a columnar storage format to efficiently store and query large amounts of data. Columnar storage is a type of data storage that stores data in columns rather than in rows. This allows BigQuery to store data more efficiently and scan it when running queries. With columnar storage, BigQuery can read only the columns needed to answer a query rather than an entire row of data, making it faster and more efficient. Additionally, columnar storage allows BigQuery to compress data more effectively, which helps to reduce storage costs. BigQuery enables users to store data in tables, allowing them to quickly and easily access their data. It supports structured and unstructured data, allowing users to work with various formats. BigQuery also supports many data sources, including Google Cloud Storage, Google Drive, and Sheets.

What is Google BigQuery Used for?

What is Google BigQuery Used For

 

BigQuery is a powerful tool for running complex analytical queries on large datasets. It offers far more capabilities than spreadsheet applications such as Excel or Google Sheets, which can only handle simple queries on smaller datasets. With BigQuery, you can carry out detailed calculations, modifications, merges, and other manipulations on massive datasets containing millions of rows of information. 

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Lets understand this with a simple example of how a retailer can use BigQuery. Retail companies have huge amounts of data about customers, inventory, and sales that are stored across various sources databases, excel sheets, data lakes, etc. BigQuery can be used to analyze customer purchase patterns or sales trends. Additionally , data from multiple sources can be combined to generate meaningful and informative business insights for better sales, marketing and inventory management strategies.

BigQuery is designed for analytical queries beyond basic CRUD operations and offers excellent performance for these queries. However, BigQuery is not intended to replace traditional data warehouses.

The three essential functions of combining Google Analytics and BigQuery include-

1) Data Manipulation

BigQuery allows for data manipulation and transformation, such as filtering, joins, and aggregations, which helps to prepare the data for analysis and visualization.

2) Data Storage

BigQuery provides a secure, scalable, and cost-effective way to store large amounts of data, which makes it easier and more efficient to store and access data for further analysis.

3) Data Analysis

BigQuery enables powerful data analysis capabilities, such as querying, creating custom metrics, and creating dashboards, which help uncover insights and better understand the data.

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Google BigQuery Data Analysis Workflows

BigQuery supports various data analytics procedures:

1) Ad Hoc Analysis

BigQuery supports ad hoc analysis using Google Standard SQL, the SQL dialect it utilizes. Users may execute searches using the Google Cloud interface or third-party applications that include BigQuery.

2) Geospatial Analysis

Users can analyze and display geographic data with BigQuery thanks to its usage of geography data types and Google Standard SQL geography functions. 

3) Machine Learning

Machine learning (ML) models may be created and used in BigQuery ML models using Google Standard SQL queries. BigQuery ML models can be used for online prediction, personalizing recommendations, optimizing pricing, and more.

4) Business Intelligence

A quick, in-memory analysis service called BigQuery BI Engine enables users to create dynamic, rich dashboards and reports without sacrificing performance, scalability, security, or the timeliness of the data.

Google BigQuery Architecture- A Detailed Overview

BigQuery is built on Dremel technology, which has been used internally at Google since 2006. Google's Dremel is an interactive ad-hoc query solution for analyzing read-only hierarchical data. The data processing architectures of BigQuery and Dremel are slightly similar, however. BigQuery is much more than a Dremel, and Dremel is simply an execution engine for BigQuery. Indeed, Google's BigQuery service makes use of cutting-edge Google technologies like Borg, Colossus, Capacitor, and Jupiter. A BigQuery client (usually BigQuery Web UI or bg command-line tool or REST API) interacts with the Dremel engine via a client interface. Borg, Google's large-scale cluster management system, distributes computing resources for the Dremel tasks. Dremel tasks read data from Google's Colossus file systems through the Jupiter network, conduct various SQL operations, and provide results to the client.

BigQuery Architecture

Source: Big Query Architecture 

BigQuery separates compute and storage through its serverless design, enabling them to expand as independently as needed. Because clients don't have to maintain their expensive computational resources up and to run constantly, this arrangement gives them tremendous flexibility and cost management. BigQuery uses a massively parallel processing (MPP) architecture, which means that queries are broken up into smaller tasks and distributed across many computers. It can process data stored in Google Cloud Storage, Bigtable, or Cloud SQL, supporting streaming and batch data processing. BigQuery also has a powerful query optimizer, which helps to analyze and optimize queries for optimal performance. BigQuery has built-in security and encryption features, allowing users to keep their data secure.

Overview of BigQuery Architecture

Source: Overview of BigQuery Architecture 

Google BigQuery Datatypes

BigQuery supports all major data types present in Standard SQL. There are a number of functions, operations, and procedures that are specific to each data type. There are eight categories of datatypes in Google BigQuery:

  1. BOOLEAN

  2. STRING

  3. NUMERIC (including INT64, NUMERIC, BIGNUMERIC, and FLOAT64)

  4. BYTES

  5. GEOGRAPHY

  6. ARRAY

  7. TIME (including DATE, TIME, DATETIME, and TIMESTAMP)

  8. STRUCT

Let us look at each datatype category in detail-

The BOOLEAN type can be NULL as well as TRUE or FALSE (both case-insensitive). The least valuable value is NULL, followed by FALSE and TRUE. Boolean values are commonly used while exporting data from applications or other software solutions, and Boolean datatype supports logical and conditional operations.

Variable-length data that works with Unicode characters are referred to as the STRING type and must be encoded in UTF-8. Use either single (') or double (") quotation marks when quoting STRINGs. They can also be triple-quoted by grouping three single ("') or triple-double (""") quotation marks together.

Calculations, data reporting, and analysis of various kinds involve numerical data. It is the most popular datatype in BigQuery. Numeric data consists of four sub-types:

  • Integer type (INT64)

  • Numeric type (NUMERIC DECIMAL)

  • Bignumeric type (BIGNUMERIC BIGDECIMAL)

  • Floating point type (FLOAT64)

Although they work with raw bytes rather than Unicode characters, BYTES also represent variable-length data. Due to this, combining and contrasting the STRING and BYTE types is impossible. BYTES(L), where L is a positive INT64 number, indicates a sequence of bytes with a maximum of L bytes allowed in the binary string. An OUT OF RANGE error is generated if a sequence of bytes contains more bytes than L.

GEOGRAPHY type is based on OFC Simple Features standard (SFS) and describes points, lines, and polygons collections. BigQuery data types like GEOGRAPHY can be exported to Google Data Studio for visualization. The info from BigQuery tables can automatically create entire maps and routes.

The ARRAY type represents an ordered list of 0 or more identical data type items. If a query attempts to generate an array of arrays, it will return an error. Therefore, using the SELECT AS STRUCT construct, a struct must be added in its place between the arrays.

Independent of a particular date or time zone, this datatype represents time as you would imagine to see it on a clock, for instance, 07:15:46. It doesn't depend on any particular day. Hours, minutes, and seconds can be stated as single-digit and double-digit values since the standard format is [H]H:[M]M:[S]S[.DDDDDD]. "D" denotes fractional digits, which can add up to 6 digits (microseconds precision).

The STRUCT type represents an ordered collection of data of any type. Within a STRUCT, data types can be combined freely. While a field name is optional, the type must be specified. The equality operators equal (=), not equal (!= or >), and [NOT] IN can be used to compare structs directly.

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BigQuery Tutorial for Beginners: How To Use BigQuery?

BigQuery Tutorial for Beginners

 

BigQuery SQL Basics

To get started with BigQuery SQL basics, it is essential to understand the basic concepts and syntax. BigQuery uses a version of the Structured Query Language (SQL) to query and manipulate data in BigQuery tables. The syntax for BigQuery SQL is slightly different from other versions of SQL, so getting familiar with the basics to write correct queries is crucial. Here are some of the basic concepts and syntax to run BigQuery SQL queries:

  • SELECT Statement - This statement is used to select data from a table. 

  • WHERE Clause - This clause filters data from a table. 

  • GROUP BY Clause - This clause groups data from a table. 

  • ORDER BY Clause - This clause orders the data from a table. 

  • HAVING Clause - This clause filters data from a group of data.

  • JOIN Statement - This statement joins data from two or more tables.

Getting Started: BigQuery Setup

Using BigQuery is pretty straightforward, especially since it is a cloud data warehouse, so it does not require any installments. The following is a step-by-step guide on how BigQuery can be used.

1) If this is your first time using the Google Cloud Platform, you must agree to the terms prompted once logged in.

BigQuery Setup Page

 

2) Use the search bar or the left navigation menu in the Google Cloud Console to search for BigQuery

Google Cloud Console Setup Page

 

3) Create a project to activate BigQuery

Google BigQuery Activation

Name your project, then click Create,

Google BigQuery Project Creation

You're done! Now you can jump to using BigQuery.

Getting Started with Using BigQuery

1) Create a Dataset

You need data to start using BigQuery. To create one, click on the three dots next to the project's name, then click on Create data set.

Google BigQuery Dataset Creation

 

Name your dataset, then click on CREATE DATA SET.

Google BigQuery Dataset Name

To view your dataset, click on the Expand node button.

Google BigQuery Dataset Viewing

 

2) Create a table in the dataset

BigQuery tables are easy to create and add data to. To create one, click on CREATE TABLE.

Google BigQuery Table Creation

 

There are a few ways to add data to a BigQuery table:

  • Create an empty table and manually fill it.

  • Upload a table from your device in one of the formats supported (CSV, JSONL, Avro, Parquet, ORC, Google Sheets, Cloud Datastore Backup)

  • Import a table from Google Drive or Google Cloud Storage (this option allows you to import Google Sheets)

  • Using the CLI, import a table from Google Cloud Bigtable

  • Importing a table from Amazon S3 or Azure Blob Storage

Google BigQuery Table Source

 

Click on CREATE TABLE once you choose a source.

That's it! Now you can start exploring BigQuery's built-in capabilities, some of which are:

  • Running concurrent queries

  • Real-time table update by Cloud Logging

  • Export tables to Google Cloud Storage

  • Ingest data from any managed storage

  • Copy data from tables/datasets

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Google BigQuery: From Theory to Practice

It’s time for you to implement all the theoretical knowledge about Google BigQuery into some actual project ideas. 

BigQuery Fraud Detection System

 

In today's environment, detecting fraud is becoming increasingly vital. With the development of online transactions and digital payments, there has never been a greater need to detect and prevent fraudulent conduct. BigQuery is a powerful tool with a machine learning capability for creating effective, efficient, and secure fraud detection systems.

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To build a fraud detection system using BigQuery, you need to:

- Collect and store data from various sources using BigQuery's loading and streaming built-in capabilities and BigQuery's Data Transfer Service to pull data from external sources. Once the data is stored, you can run BigQuery queries to analyze the data and get insights.

- Develop and train a machine-learning model using BigQuery ML, which allows you to build and train machine-learning models directly in BigQuery. You can use existing models or create your custom model.

- Deploy and monitor the model through BigQuery ML's deployment options, including batch predictions, online predictions, and batch training.

- Detect fraudulent activity in real-time using BigQuery's search capabilities to detect data anomalies.

Check out Google's blog for a more extensive explanation and code for building a BigQuery fraud detection system.

This GCP project idea will show you how to use GCP BigQuery for data exploration and preparation before analysis and dataset transformation. Working on this project will help you better understand Managed and External Tables and the different supported File Formats in BigQuery. You will also learn how to utilize BQ CLI commands to create External BigQuery Tables using GCS Buckets and to load BigQuery Tables using Client API.

Source Code: GCP Project to Learn Using BigQuery for Exploring Data

By using predictive analytics, companies can make better decisions about allocating resources, creating new products, and better targeting their marketing efforts. Additionally, they can help businesses identify potential opportunities or risks before they arise, allowing them to take proactive measures to avoid or capitalize on them.

To use BigQuery to analyze large datasets and build a predictive model to forecast future trends, you will need to load the data into BigQuery. Once you load the data in BigQuery, you can run queries to analyze the data and create visualizations. After analyzing the data, you can use machine learning algorithms to develop a predictive model. To build the model, you can use BigQuery ML, which provides a variety of algorithms that can be used to build predictive models. Finally, you can use the model to predict future trends in the data.

Steps:

  • Load the data into BigQuery.

  • Use SQL queries to analyze the data and create visualizations.

  • Use machine learning algorithms to develop a predictive model.

  • Use BigQuery ML to create and execute the model.

  • Use the model to make predictions about future trends in the data.

  • Evaluate the accuracy of the model and make necessary modifications.

  • Deploy the model and monitor its performance.

Check Google's templates for predictive analytics using BigQuery.

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BigQuery can store and analyze data from Google Ads, such as clicks, impressions, cost per click, and other metrics. This data might then be used to build reports and visualizations, such as comparing the cost per click of ad campaigns or the click-through rate of various ad kinds. BigQuery may also be used to execute complicated queries to gain deeper insights into the efficacy of your campaigns, such as which keywords are most effective or which audiences are most likely to convert.

To use BigQuery with Google Ads, you must set up a data transfer to import data from your Google Ads account into BigQuery. Once you have set up the transfer, you can write and run SQL queries to analyze the data from ads. You can also generate reports and visualizations to understand the performance of your ad campaigns. Additionally, you can use complex queries to uncover deeper insights about your campaigns, such as which keywords are most effective or which audiences are most likely to convert. Finally, you can use the insights you uncover to optimize your campaigns and get the most out of your ad budget.

Steps:

  1. Create a BigQuery dataset to store data from Google Ads.

  2. Set up a data transfer to import data from your Google Ads account into BigQuery.

  3. Write and run SQL queries to analyze the data from ads.

  4. Generate reports and visualizations to understand the performance of your ad campaigns.

  5. Use complex queries to uncover deeper insights about your campaigns.

  6. Optimize your campaigns based on the insights you uncover

Explore diverse data warehousing projects to put your fundamental knowledge of the platform into practice. ProjectPro offers over 250 Big Data and Data Science projects leveraging Google BigQuery and various other modern data engineering tools and technologies.  

Discover The Potential of Google BigQuery

BigQuery is a powerful and versatile data warehouse that can be used for various tasks, from analyzing large datasets to creating and executing complex data processing pipelines. BigQuery makes it easy for users to access and analyze data with minimal effort due to its scalable cloud data warehouse and easy-to-use interface. From making data-driven decisions to building innovative data-driven applications, BigQuery can unlock insights from data and help organizations make better decisions. With the ability to easily create complex data pipelines, BigQuery is an ideal choice for organizations looking to take advantage of the power of data.

FAQs on Google BigQuery

Q: Is BigQuery Free?

A: BigQuery is not free, but it does offer a free tier with 10 GB of BigQuery storage and up to 1 TB of queries per month. BigQuery also has a sandbox mode that allows users to experiment with the service at no cost. Check this link for the BigQuery pricing.

Q: Is BigQuery better than SQL?

A: It depends on the context and the specific use case. BigQuery is a powerful cloud-based data warehouse solution that can be used for a wide range of big data analytics. It supports various SQL-like query languages and is optimized for large-scale data analytics. BigQuery is an excellent choice for organizations that need to store and analyze large amounts of data quickly and efficiently. However, the standard SQL dialect is a more traditional approach and may be better suited for smaller, more straightforward datasets. Ultimately, it depends on the size and complexity of the data set and the organization's specific needs.

Q: Is BigQuery SQL or NoSQL?

A: BigQuery is a hybrid system between SQL and NoSQL. It supports a standard SQL dialect that is ANSI-compliant and based on Google's internal column-based data processing. BigQuery also offers a range of NoSQL capabilities, such as the ability to query semi-structured data and use dynamic schemas.

Q: Which pattern describes source data moved into a BigQuery table in a single operation?

A: Bulk loading is a pattern that describes source data moved into a BigQuery table in a single operation. Bulk loading is a process that allows large amounts of data to be moved from a source into a BigQuery table in a single operation. It is an efficient way to move data into BigQuery as it reduces the time and effort needed to move all the data.

Q: Which two services does BigQuery provide?

A: BigQuery provides two primary services: data storage and data analysis. BigQuery also offers powerful analytics capabilities, such as BigQuery SQL queries, user-defined functions, scripts, and machine learning models. With BigQuery, you can quickly analyze data to gain valuable insights and make informed decisions.

 

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About the Author

BadrSalah

A computer science graduate with over four years of writing experience in various fields. His passion for technology and knack for clear communication enables him to simplify complex topics for readers. Fun fact: Badr has a mixed-breed dog named

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