Google BigQuery Explained: Everything You Need to Know

If you've ever found yourself lost in the world of big data or wondered how companies manage to handle those numbers of tables, entities, datasets, you're in the right place. Today, we're diving into BigQuery - the Google’s tool that makes managing and analyzing massive amounts of data feel like a breeze - it’s cloud based and extremely easy to use.

Whether you're a data analyst, an engineer or developer, or just curious about what BigQuery can do for the business, this guide will walk you through everything you need to know to make an informed choice. Let's get started!

In this article, we will explore how BigQuery can benefit different types of users, when businesses consolidate data management and analysis tasks within the data warehouse. Additionally, we will explore BigQuery's pricing models and practical tips, as well as quickly compare it with other data warehouses.

By the end of this article, you will have a clear understanding of why Google BigQuery is one of the best choices for most of the modern businesses, who want to make the most of their data.

Google BigQuery is a cloud-based data warehouse that offers scalable, flexible, and cost-effective solutions for managing and analyzing large datasets.

It leverages Google's cloud infrastructure to provide data storage and processing capabilities, making it ideal for handling vast amounts of data efficiently without the need for infrastructure management.

Google BigQuery acts as a fully managed database service that lets users store and query data quickly.

Its architecture supports high-speed data retrieval and processing, making it suitable for handling any datasets - from simple to large-scale.

As a cloud data warehouse, BigQuery use Google server infrastructure to provide scalable and flexible data storage and analysis solutions.

You don’t need to own or rent physical hardware, letting users focus on their data without worrying about underlying infrastructure management.

Unlike traditional databases like MySQL, BigQuery is column-oriented, which means it organizes data in columns rather than rows.

This format is highly efficient for running SQL queries, enabling faster data retrieval for large datasets.

BigQuery can also be viewed as a spreadsheet-like database.

It provides a user-friendly interface that simplifies data manipulation and analysis. Its integration with tools like Google Sheets allows users to interact with data in a familiar spreadsheet format.

You can also manage your BigQuery data right from the convenient for everybody Google Sheets interface.

With OWOX: Reports, Charts & Pivots, you can transform any Google Sheet in a window to your database. Both ways: Automatically sync BigQuery tables into Sheets, or the other way around: upload data from Google Sheets into BigQuery.

BigQuery is designed to meet the needs of various professionals and business users, including:

• Data Engineers: To build and maintain data pipelines and infrastructures, ensuring data is clean, accessible, and up-to-date for all users.
• Data Analysts: To analyze large datasets and derive actionable insights, creating detailed reports and visualizations to support business decisions.
• Data Scientists: To perform complex data modelling and machine learning, leveraging BigQuery's computational power to process large datasets efficiently.
• Developers: To integrate BigQuery with applications and systems, enabling seamless data flow and real-time analytics within their software solutions.
• Business Users: To access and analyze business data without technical expertise, using intuitive tools and interfaces to make data-driven decisions.

BigQuery enables business users to access and utilize data efficiently, driving informed decision-making across the organization.

Empower your business users by making corporate data accessible through intuitive tools and interfaces.

With OWOX Reports Extension for Google Sheets, you can seamlessly integrate BigQuery data with Google Sheets. This integration allows users to analyze data without technical expertise, fostering a data-driven culture within your organization.

Google BigQuery is a powerful and scalable platform designed to meet the diverse needs of various professionals and business users. Its lightning-fast query performance, intuitive interface, and robust infrastructure ensure speed, ease of use, and reliability.

BigQuery eliminates the need for infrastructure management, allowing business users to focus on data analysis rather than hardware or server maintenance. Its fully managed nature ensures seamless operation and scalability without the requirements for manual intervention.

This reduces the overall operational overhead and allows teams to concentrate on deriving insights from data.

BigQuery enables effortless data consolidation from multiple sources, making integrating and analyzing diverse datasets simple.

This capability streamlines data workflows and enhances the ability to derive comprehensive insights from various data points. Because of this, businesses can make more informed decisions based on a centralized view of their data.

BigQuery offers an analyst-friendly interface with a SQL (Structured Query Language), making it accessible to users with varying levels of technical expertise. Its intuitive design allows analysts to quickly write and execute queries, accelerating the data analysis process.

This user-centric approach enables faster adoption and more efficient utilization of the platform.

BigQuery provides access to a wide range of public datasets, enabling users to enhance their analyses with external data.

This extensive dataset library supports more robust and informed decision-making. Leveraging these public datasets allows users to enrich their data models and gain deeper insights without additional data collection efforts.

BigQuery has a native integration with Google’s visualization tool – Looker Studio.

Users can create interactive and insightful visualizations directly from their BigQuery data, facilitating better understanding and communication of analytical results.

This integration ensures that data visualizations are always up-to-date and easily shareable across the organization.

Note: BigQuery also seamlessly connects to a wide-range of other visualization tools.

Business users can leverage Google Sheets to interact with BigQuery data, providing an intuitive and familiar interface for data analysis.

This integration allows users to run SQL queries directly from Google Sheets, enabling seamless access to large datasets without leaving the spreadsheet environment. With features like data visualization, pivot tables, and collaborative editing, users can perform advanced analysis and share insights effortlessly.

Additionally, the ability to schedule automatic data refreshes ensures that the information in Google Sheets remains up-to-date with the latest data from BigQuery.

Enhance your data accessibility with OWOX Reports Extension for Google Sheets. This tool seamlessly integrates BigQuery data with Google Sheets, allowing business users to easily analyze and visualize corporate data, driving informed decision-making across your organization.

Understanding the pricing structure of Google BigQuery is essential for managing costs effectively.

BigQuery's pricing is designed to be flexible and scalable, catering to various use cases. Here is a breakdown of the primary cost components:

BigQuery charges $0.02 per GiB per month for active storage, $0.01 $0.01 per GiB per month for long-term logical storage, $0.04 per GiB per month for active physical storage, and $0.02 per GiB per month for long-term physical storage.

The first 10 GiB of each type of storage is free each month.

BigQuery offers on-demand pricing at $6.25 per terabyte (TiB) of data processed. First 1 TiB is free. For predictable query patterns, flat-rate pricing starts at $10,000 per month for 500 slots.

Data ingestion is generally free, but data transfer from external sources may incur some costs.

However, GA4 BigQuery Export is 100% Free. And it’s a must-have for any serious business. The data transfer from Google Ads, YouTube Ads, Search Console comes with no cost as well.

Exporting data incurs costs based on the amount of data exported. Streaming inserts are charged per GB ingested.

Using BigQuery ML for machine learning models incurs additional costs based on resources consumed.

Getting started with BigQuery involves setting up a project, enabling necessary APIs, creating a billing account, and configuring permissions to manage data access and security.

Begin by creating a BigQuery project in the Google Cloud Console. This project will serve as the foundation for your data storage and analysis.

Navigate to the Cloud Console, select or create a new project, and name it appropriately to reflect its purpose.

Enable the BigQuery API to allow your project to interact with BigQuery services and perform data operations.

In the Cloud Console, go to the API library, search for 'BigQuery API,' and enable it for your project.

Set up a billing account to manage your BigQuery costs and ensure uninterrupted service access. In the Cloud Console, navigate to the billing section, create a new billing account, and link it to your BigQuery project to monitor and control expenses.

Configure permissions and roles to control access to your BigQuery project, ensuring data security and compliance.

Assign appropriate roles to team members, such as Viewer, Editor, or Admin, to regulate who can view, edit, or manage the project's resources and data.

The BigQuery Sandbox is a free, no-commitment environment provided by Google Cloud that allows users to explore and experiment with BigQuery's capabilities without requiring payment information.

Note: This free tier option grants you 10 GB of active storage and 1 TB of processed query data per month, with tables expiring after 60 days.

It's an ideal choice for learning and small-scale projects, providing an excellent opportunity to familiarize yourself with BigQuery's powerful tools and features at no cost.

Note: Consider activating the free trial, which requires billing details and offers $300 in cloud credits for more extensive use.

Whether you're a data engineer, analyst, or business user, BigQuery provides powerful tools to gain insights from your data quickly.

To get started with BigQuery, you must go through the fundamental steps, including creating datasets and tables, importing data in various formats, and leveraging tools like OWOX BI for seamless data integration.

To begin using BigQuery, you'll need to create a dataset within your Google Cloud project. This dataset serves as the container for your tables and data.

In the Google Cloud Console, navigate to your project, select ‘Create Dataset’,

Enter a name for your dataset using only letters and numbers.

This will be your Dataset ID.

If needed, choose the geographical location where your data will be stored. You can set an optional table expiration date (up to 60 days). Now click 'Create dataset'

A new dataset is now created. You can find it by clicking the Expand node button next to your project name.

Creating a table in BigQuery is the next step after setting up your dataset. This process allows you to organize and store your data in a structured manner.

1. To create a table, navigate to your dataset and click the 'Create Table' button.

2. You have several options for table creation: you can create an empty table and fill it manually, upload a table from your device in one of the supported formats

3. Import a table from Google Cloud Storage or Google Drive (which also allows you to import Google Sheets),

4. The command line interface (CLI) can import a table from Google Cloud Bigtable.

Each method provides flexibility depending on your data sources and needs.

BigQuery supports various file formats for data import, ensuring flexibility and ease of integration. You can load tabular data into BigQuery using formats such as

• CSV
• JSONL (JSON lines)
• Avro
• Parquet
• ORC
• Google Sheets (for Google Drive only)
• Cloud Datastore Backup (for Google Cloud Storage only)

Ensuring your files are properly formatted according to these supported types will facilitate smooth data ingestion, allowing BigQuery to process and store your data efficiently. This compatibility with multiple file formats makes integrating data from diverse sources easier, enhancing the overall efficiency of your data workflows.

To upload CSV data into BigQuery, follow these steps:

Step 1: In your dataset, click ‘Create Table’. In the Create table window, select 'Upload' as your data source. Click 'Browse' to select the CSV file from your computer, Google Cloud Storage, or Google Drive.

Step 2: Ensure the file format is set to CSV.

Specify the name of the project and the dataset where the data will be stored. Enter a meaningful name for your table.

Step 3: Choose between native and external table types. Native tables are stored within BigQuery, while external tables reference data stored outside BigQuery.

Step 4: BigQuery will automatically determine the table structure, but you can manually add fields using the text revision function or the '+ Add field' button.

Step 5: Click 'Create Table' to upload your data. Once the upload is complete, a confirmation message will appear, and your new table will be in the left navigation pane under tables within your dataset.

OWOX BI simplifies the process of importing data from Google Sheets into BigQuery. Connecting your Google Sheets and BigQuery accounts lets you automate the data transfer, ensuring your data is always up-to-date for real-time analysis.

To import data from Google Sheets into BigQuery using OWOX BI,

1. The first setup is to connect OWOX BI to your Google Sheets from the Workspace Marketplace. You can type in BigQuery or OWOX and just select the first one:

2. Install the extension from the Workspace Marketplace.

3. Open a Google Sheet Spreadsheet you want to upload to BigQuery. (or upload a CSV to Google Drive)

4. Go to ‘Extensions' and select ‘OWOX BI BigQuery Reports' — ‘Upload data to BigQuery':

5. A new pop-up window opens. In the destination section - you need to select the project ID, and the dataset ID, and come up with a name for the table in which you want to load your single spreadsheet.

6. Then, check the boxes for the columns you want to import and check the data type for the fields. By default, every column has a ‘STRING', so you have to replace the types of data according to your business context. For example, for numeric identifiers, the type is ‘INTEGER' data type, for prices use the 'NUMERIC' data type or 'FLOAT', for dates - the 'DATE' data type, etc.

7. Click Start Upload, and your document will be uploaded to BigQuery.

8. If successful, you will see a message with the loading status "Success. Show table in BigQuery" and a link to the table. Your Data is now Uploaded from Google Sheets to BigQuery table, and you can click the link to check the ingested data in BigQuery UI right away. Success.

This integration ensures your data is consistently updated and readily available for analysis in BigQuery.

BigQuery empowers business users to access and utilize corporate data efficiently, fostering informed decision-making and strategic planning.

Enhance this capability with OWOX BI BigQuery Reports, which seamlessly integrates BigQuery data with Google Sheets for easy analysis and visualization.

A job in BigQuery is an operation performed on queries or tables, such as executing a query, copying data, or exporting results. BigQuery jobs can handle large datasets, run complex queries, and generate reports efficiently. Each job type has configurable options to optimize performance and resource usage.

Jobs are tracked and monitored for progress, errors, and completion, ensuring transparency and manageability. This robust job management system allows users to schedule and automate data processing tasks, enhancing productivity and operational efficiency.

Google BigQuery's architecture is designed to handle large-scale data processing and analytics with high performance and scalability. It leverages a combination of proprietary technologies to provide robust data storage, efficient query execution, and seamless data transfer.

Colossus is Google's distributed file system that underpins BigQuery, providing scalable and reliable data storage.

It enables BigQuery to handle vast amounts of data with high availability and fault tolerance, ensuring that your data is always accessible and secure.

Colossus also supports advanced features such as erasure coding and data replication, enhancing data durability and performance.

Dremel is the query execution engine that powers BigQuery, enabling fast and efficient data processing and analysis.

It uses a multi-level tree architecture to execute queries in parallel, delivering rapid results even for complex analytical queries on massive datasets.

Dremel's columnar storage format and optimized data paths significantly improve query performance.

Jupiter is Google's network infrastructure that facilitates high-speed data transfer and communication within BigQuery.

This network backbone ensures low-latency access to data and efficient communication between different components of BigQuery, contributing to its overall performance.

Jupiter's high-bandwidth, low-latency connections are crucial for the rapid movement of large datasets.

Borg is the cluster management system that orchestrates BigQuery's compute resources, ensuring efficient resource allocation and job execution.

It manages the scheduling, monitoring, and optimization of compute resources, enabling BigQuery to handle numerous queries and tasks simultaneously with optimal performance.

Borg's robust orchestration capabilities include automatic scaling and resource balancing.

BigQuery's strength lies in its ability to quickly and efficiently query large datasets using the standard SQL dialect, which is recommended for its robustness and compatibility.

While legacy SQL is still supported, standard SQL fully utilizes BigQuery's advanced features, enabling comprehensive data analysis and powerful insights.

Querying data in BigQuery is straightforward and powerful, allowing you to extract meaningful insights from large datasets efficiently. Here’s how you can get started with querying data in BigQuery.

To begin querying data in BigQuery, click the 'Query table' Button. This will open the query editor, where you can write and execute your SQL queries.

Basic Query Syntax

When you start a new query, you'll see a basic query boilerplate:

To retrieve all columns from the specified table, modify the query by adding * after SELECT

This query will return all available columns from the specified table, but no more than 1,000 rows. Click 'Run' to execute the query and view the results.

Example: Query Specific Fields and Order Data

Instead of selecting all columns, you can specify the fields you want to query. You can find the field names in the Schema tab or from your previous queries. Here’s an example where we query specific fields and order the results by a specified column:

Here:

• SELECT string_field_4, string_field_13, string_field_19: Specifies the fields to retrieve from the table.
• FROM owox-analytics.dataset.test_query: Indicates the table to query.
• ORDER BY string_field_19 DESC: Sorts the results in descending order based on string_field_19.

Querying data in BigQuery is a powerful way to analyze large datasets using SQL. You can efficiently retrieve and analyze your data by specifying the fields you need and applying sorting, filtering, and other SQL operations.

Optimize your queries by configuring query priority and caching settings to enhance performance

If you click 'More'

and select 'Query Settings’, you can configure the destination for your query results and adjust other settings.

In this section, you can also set up batch queries, which are queued and executed as soon as idle resources become available in the BigQuery shared resource pool. This allows for efficient resource management and optimized query execution.

Save your frequently used queries for easy access and reuse in future analyses. BigQuery allows you to save queries directly within the console, ensuring you can quickly retrieve and execute them without rewriting.

1. To save a query, click on the 'Save' button located at the top of the query editor. You can choose to save the query as a 'Saved Query.'

2. In the next window, name your query and choose its visibility: personal, project, or public. After configuring these settings, click 'Save.'

This feature enhances efficiency and helps maintain consistency across analyses by allowing you to reuse and share queries easily.

Scheduling queries in BigQuery allows you to automate the execution of your queries, ensuring data is updated and ready for analysis at specified intervals.

Step 1: Next to the 'Save' button, there is a 'Schedule' button under which there is 'Enable scheduled queries', which enables you to set up scheduled queries.

There are at least two reasons to run queries on a schedule:

Efficiency: Large queries can take a long time to run, so it is better to prepare data in advance.

Cost Management: Google charges for data queries, so scheduling daily updates can help manage costs by using prepared views for ad-hoc querying.

Step 2: To enable scheduled queries, click the Schedule button, and enable the BigQuery Data Transfer API by clicking 'Enable API'.

Step 3: Wait for the process to complete, then click the Schedule button again and select 'Create new scheduled query'.

Define the following parameters for your scheduled query:

• Name for Scheduled Query: Give your scheduled query a descriptive name.
• Schedule Options: Set the frequency of the query (e.g., daily, weekly).
• Repeats: Specify how often the query should repeat.
• Start Date and Run Time: Set the start date and the time when the query should run.
• End Date: Define when the query should stop running (optional).
• Destination: Select the table where the query results will be stored.
• Table Name: Enter the name of the destination table.
• Write Preference: Choose between 'overwrite' (query results will overwrite existing data) or 'append' (query results will be appended to existing data).

You can optionally set up advanced options and notification preferences. Once you’ve configured all the settings, click 'Schedule.'

After setting up the scheduled query, select the preferred Google account and continue to the BigQuery Data Transfer Service.

If you forgot to save an advanced query and need to restore it, BigQuery has you covered. It provides logs of all the queries and jobs you have executed. You can access these logs through the 'Jobs history' and 'Query history' tabs in the BigQuery console.

There is also 'Personal History' and 'Project History' under 'Job History'. These logs allow you to review, rerun, and manage your past queries, ensuring you never lose important work.

Exporting queries from BigQuery is essential for sharing and further analyzing your data outside the platform. BigQuery allows you to export query results to various formats and destinations, enabling seamless integration with other tools and workflows.

Knowing certain limitations when using Connected Sheets to export data from BigQuery is important. Connected Sheets allows you to work with BigQuery data directly within Google Sheets, providing a familiar spreadsheet interface for data analysis. However, there are constraints on the amount of data you can export, the frequency of data updates, and the complexity of the queries you run.

To access the native BigQuery export data options, click the 'Save Results' button and here are the data export options:

• CSV file: Download up to 16K rows to your device or up to 1GB to Google Drive.
• JSON file: Download up to 16K rows to your device or up to 1GB to Google Drive.
• BigQuery table: You can export up to 16K rows to Google Sheets or copy up to 16K rows to the clipboard.

Let's consider a practical example of exporting a query from BigQuery to Google Sheets. Suppose you have a dataset of sales transactions and you want to analyze monthly sales performance. Here are the steps to achieve this:

Step 1: Open BigQuery and navigate to your project.

Step 2: Write an SQL query to aggregate sales data by month. For example:

Step 3: Run the query to get the aggregated sales data.

Step 4: Once the query results are displayed, click on the 'Explore Data' button and select 'Connected Sheets.'

Step 5: Follow the prompts to connect the query results to a new or existing Google Sheet.

Step 6: You can now see your query results in the Google Sheet. Use the available tools to create charts and pivot tables and perform further analysis as needed.

Step 7: Save and share your Google Sheet for collaborative analysis and reporting.

Automating the export of queries from BigQuery to Google Sheets can save significant time and effort, especially for recurring reports.

By setting up an automated reporting workflow, you can schedule regular data exports, ensuring your Google Sheets are always up-to-date with the latest data from BigQuery.

Business users can simplify and automate their corporate reporting with OWOX BI, using BigQuery for efficient data analysis and reporting.

This tool lets you create visually appealing reports and dashboards directly in Google Sheets, combining data from various sources for a complete analysis. You can automate data collection and transformation, ensuring you always have the latest information for decision-making. Plus, OWOX BI supports scheduled data updates and custom SQL queries, improving the accuracy and timeliness of your business insights.

When compared with competitors like Amazon Redshift, Microsoft Azure SQL Data Warehouse, and Snowflake, Google BigQuery emerges as the preferred choice for data warehousing.

Although powerful, Redshift is costlier and more complex to manage. Azure SQL Data Warehouse is suitable for medium workloads but falls short in scalability and performance compared to BigQuery.

Snowflake offers excellent scalability but can become expensive with larger datasets. BigQuery’s pay-as-you-go and preemptive pricing models eliminate the need for upfront server capacity or storage provisioning, offering cost efficiency and flexibility.

BigQuery also excels in query performance due to its columnar storage system, which retrieves only relevant columns instead of entire rows, significantly speeding up queries.

Additionally, BigQuery boasts advanced security features, including Field-Level Encryption, Full Scan Encryption, and Column-Level Access Control, ensuring robust data protection.

With an availability SLA of up to 99%, BigQuery offers unparalleled reliability, making it a superior choice for organizations seeking a scalable, high-performance, and secure data warehousing solution.

Expand your knowledge with these essential BigQuery functions that simplify and enhance your data analysis capabilities.

• Data Manipulation Language (DML): Allows you to update, insert, and delete data in BigQuery tables, simplifying data management tasks​.
• Date Functions: Includes functions like DATE_ADD, DATE_SUB, and DATE_DIFF to manipulate and compare dates for effective date-based data analysis​.
• Conversion Functions: Convert data from one type to another, such as CAST and SAFE_CAST, ensuring data compatibility and correct type usage.
• Array Functions: Manage arrays with functions like ARRAY_AGG, which aggregates data into arrays, and UNNEST, which flattens arrays for easier data processing​​.
• Aggregate Functions: Calculate multiple rows of data, such as SUM, COUNT, and AVG, to generate summary statistics​.
• Conditional Expressions: Use CASE, IF, and COALESCE to create conditional logic for dynamic data manipulation in your queries.

Leverage OWOX Reports to enhance your corporate reporting capabilities, providing valuable insights and driving business growth. By integrating BigQuery and OWOX BI into your analytics tech setup, you can create comprehensive reports that streamline data analysis and visualization, making it easier for stakeholders to make informed decisions.

In conclusion, Google BigQuery is a robust, scalable, and user-friendly cloud data warehouse that offers a wide range of features and benefits. Whether you are a data engineer, analyst, or business user, BigQuery can help you manage and analyze your data more effectively.