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What is BigQuery?
BigQuery is a type of scalable data warehouse to manage and analyze data. Businesses use data warehouses to store their data and make informed decisions based on analysis.Footnote 1
An on-site data warehouse depends on servers, hardware, and software to operate effectively. Because of its limitations, scaling becomes a challenge and restricts businesses from freely uploading data into each warehouse.Footnote 2 A cloud warehouse, on the other hand, can store a larger amount of information because it exists on a virtual cloud. With no dependence on servers, hardware, or software, it reduces the need for additional resources and provides a cost-efficient way to collect and analyze data.
BigQuery is one instance of a fully managed, cloud-based data warehouse. Its serverless architecture is designed for modern businesses that need unrestricted data storage and access. It also helps streamline various types of business intelligence (BI) processes, such as business analytics, data mining, data visualization, and data analysis.
Features of BigQuery include:
Built-in machine learning: Using SQL, data scientists and data analysts can build fully functioning machine learning models within BigQuery and increase development speed.Footnote 3
Multi-cloud analytics: Storing information in multiple clouds can easily lead to disorganization. BigQuery Omni provides a centralized, cross-cloud location that enables users to access and analyze data from multiple clouds, such as Amazon S3 or Azure Blob Storage.Footnote 4
Real-time analytics: BigQuery BI Engine speeds up SQL queries by storing and remembering data that’s frequently used. This helps businesses access important insights quickly and in real time.Footnote 5
Unified data: There are three main classifications of data: structured, semi-structured, and unstructured. BigQuery allows users to query all data types in one storage engine via BigLake.Footnote 6
Geospatial analytics: Because many businesses rely on location-based data, BigQuery analyzes and visualizes geospatial information to help drive important decisions.Footnote 7
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What is covered in a BigQuery tutorial?
BigQuery courses can introduce learners to the basics of data science, helping them build foundational knowledge on topics like statistics, machine learning, and SQL, a querying language used for storing and accessing data. A more comprehensive BigQuery tutorial can dive deeper into SQL allowing learners to practice how to write its syntax, export and organize data, and navigate various features of the Google Cloud platform.
edX offers a wide range of boot camps and courses that equip learners with skills related to data science and data analysis. Explore available courses, which can include coursework on BigQuery.
Jobs that use BigQuery
BigQuery is a useful tool for various data roles because it allows users to perform deep analyses and organize data efficiently. Some data professionals who may use BigQuery include:
Data warehouse administrator
Data mining engineer
Learners can prepare for their professional future within the data field by pursuing a bachelor’s degree, a master’s degree, or a boot camp program that focuses on a specialized topic. Every job will have different requirements depending on the role and the employer. And some employers may ask job applicants to demonstrate their skills.
How to use BigQuery as a data scientist
Data scientists analyze large amounts of data, identify unresolved questions within data sets, and formulate solutions. With a well-rounded understanding of science, mathematics, and technology, they can present actionable insights to help businesses meet their goals. BigQuery enables them to do this work. BigQuery use cases include:
Data analysis: By helping data scientists gather and query large, complex data sets, BigQuery allows these professionals to identify notable trends and patterns that can affect future business decisions.
Machine learning: With BigQuery’s machine learning tools, data scientists can use SQL queries to create and train their own machine learning models. This can help them make data predictions that forecast business outcomes.
Unifying data: If a company collects data on multiple cloud platforms, data scientists can simply transfer data to BigQuery from sources like Oracle, Redshift, Teradata, or Snowflake. This provides a convenient, centralized location to access data when it’s needed for analysis.
Building marketing data warehouses: A marketing data warehouse allows businesses to collect user information and create targeted, personalized experiences. Data scientists who work in marketing can combine various data sets within BigQuery, build machine learning models for customer predictions, and assess their company’s business performance.Footnote 8
If you are interested in becoming a data scientist, a bachelor’s degree in computer science and data science can prepare you with the skills and education you need to effectively use the tools of the trade. If you are interested in a shorter course of study, a boot camp in data analytics or a coding boot camp may also teach you BigQuery.