What is BigQuery?
Supported by Google’s infrastructure, BigQuery is a serverless enterprise data warehouse that allows you to control who views and queries your data. Because BigQuery is a completely managed solution, there is no need to download & install software or setup servers. This cost-effective solution is designed for business agility.
BigQuery’s goal is to democratize data insights with a secure platform that easily scales to meet the needs of an enterprise while also allowing teams to gather key insights from data across multiple cloud platforms.
Those without in-depth knowledge of SQL can use BigQuery to analyze billions of rows of data in Google Sheets by using tools such as pivot tables, charts, and formulas. You can construct a number of jobs in BigQuery, such as load, export, query, and copy.
With BigQuery you can run open source data science workloads—including Spark, TensorFlow, Dataflow, Apache Beam—with the assistance of Storage API.
Other BigQuery tools include:
- BigQuery GIS. The serverless architecture of BigQuery comes with native support for geospatial analysis, advanced analytics workflows, and location intelligence.
- BigQuery BI Engine. This in-memory analysis service helps teams to evaluate large, hard-to-analyze datasets at lightning speed—a sub-second query response time. BigQuery BI Engine integrates with a number of data analytics tools, including Data Studio.
- BigQuery Omni (private alpha). The latest BigQuery tool enables access to data across clouds using standard SQL while remaining on BigQuery’s interface. BigQuery Omni will allow developers to query data in GCP, Amazon Web Services, and Azure.
What is BigQuery used for?
BigQuery grants enterprises external access to Google’s Dremel technology while also supporting a number of Google-proprietary mechanisms, including OAuth.
Some key BigQuery features include:
- Data management. BigQuery allows you to create and delete tables, views, and other objects. It’s easy to import data in a number of formats, including CSV, Parquet, Avro, and JSON. Share insights with easy-to-read reports and dashboards.
- Querying. BigQuery uses the industry standard SQL dialect and delivers results in JSON. While reply lengths are usually limited to 128 MB, unlimited sizes are possible when you enable large query results.
- Integration. Integrate BigQuery with Google Apps Scripts and other languages that connect with client libraries and relevant REST APIs.
- Access control. With BigQuery you can easily share datasets with individuals and larger groups.
- Machine Learning (ML) & Natural Language Processing (NLP). Customize machine learning models with the help of SQL queries to predict important business outcomes. Teams can export their ML models into a Cloud AI platform as well as into their own server.
- Security. BigQuery allows you to protect sensitive data with encryption, thanks to customer-managed encryption keys. All requests must be authenticated.
- Client libraries. BigQuery allows for client libraries in languages such as Java, Python, Node.js, C#, Go, Ruby, and PHP.
- Backup and restore. BigQuery enables enterprises to automatically backup and easily restore any data, as well as replicate data. You can see 7 days worth of changes.
Some common BigQuery use cases include migrating data from Amazon RedShift to BigQuery, building an e-commerce recommendation system, predicting customer value, and migrating on-premise legacy data to an agile, cloud-based solution.
Does BigQuery use SQL?
BigQuery is a database product from Google that also uses SQL as the interface to query and manipulate data. This Platform as a Service (PaaS) enables built-in machine learning technology that supports querying with ANSI SQL and allows for scalable analysis.
Who uses BigQuery?
Data scientists and data analysts use BigQuery to build and deploy machine learning models to enhance an enterprise’s business performance.