What is BigQuery?

Organizations rely on data warehouses to aggregate data from disparate sources, process it, and make it available for data analysis to support strategic and tactic decision-making. BigQuery is the Google Cloud enterprise data warehouse designed to help organizations run large-scale analytics efficiently and quickly unlock actionable insights. 

Google Cloud BigLake allows organizations to unify data lakes and warehouses on a single data set. This simplifies data governance and lifecycle management and reduces duplicate copies of data while simplifying and accelerating ingest and data transformation.

You can ingest data into BigQuery through batch uploading or streaming data directly to unlock real-time insights. As a fully-managed data warehouse, BigQuery takes care of the infrastructure so you can focus on analyzing your data up to a petabyte scale. BigQuery supports SQL (Structured Query Language), which you're likely already familiar with if you've worked with ANSI-compliant relational databases.

Google Cloud 

BigQuery unique features

BI Engine

BigQuery BI Engine is a fast, in-memory analysis service that provides subsecond query response times with high concurrency. BI Engine integrates with Google Data Studio and Looker for visualizing query results and enables integration with other popular business intelligence (BI) tools.

BigQuery ML

BigQuery ML is unlocking machine learning for millions of data analysts. It enables data analysts and data scientists to build and operationalize machine learning models directly within BigQuery, using simple SQL.

BigQuery Omni

BigQuery Omni is a flexible, multi-cloud analytics solution powered by Anthos that lets you cost-effectively access and securely analyze data across Google Cloud, Amazon Web Services (AWS), and Azure, without leaving the BigQuery user interface (UI). Using standard SQL and familiar BigQuery APIs, you can break down data silos and gain critical business insights from a single pane of glass.

Data QnA

Data QnA enables self-service analytics for business users on BigQuery data as well as federated data from Cloud Storage, Bigtable, Cloud SQL, or Google Drive. It uses Dialogflow and enables users to formulate free-form text analytical questions, with auto-suggested entities while users type a question

Connected Sheets

The native integration between Sheets and BigQuery makes it possible for all business stakeholders, who are already quite familiar with spreadsheet tools, to get their own up-to-date insights at any time.

Geospatial data

BigQuery offers accurate and scalable geospatial analysis with geography data types. It supports core GIS functions – measurements, transforms, constructors, and more – using standard SQL.

What about security?

BigQuery offers built-in data protection at scale. It provides security and governance tools to efficiently govern data and democratize insights within your organization.
Within BigQuery, users can assign dataset-level and project-level permissions to help govern data access. Secure data sharing ensures you can collaborate and operate your business with trust.

Data has automatically encrypted both while in transit and at rest, ensuring that your data is protected from intrusions, theft, and attacks.
Cloud DLP helps you discover and classify sensitive data assets.
Cloud IAM provides access control and visibility into security policies.
Data Catalog helps you discover and manage data.

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BigQuery Sandbox

The BigQuery sandbox lets you explore BigQuery capabilities at no cost and confirm that BigQuery fits your needs. With BigQuery you get predictable price-performance: you pay for storing and querying data, and for streaming inserts. Loading and exporting data are free of charge. Storage costs are based on the amount of data stored and have two rates based on how often the data is changing. Query costs can be either:

  • On-demand – you are charged per query by the amount of data processed
  • Flat-rate – if you prefer to purchase dedicated resources

You can start with the pay-as-you-go, on-demand option and later move to flat-rate if that better suits your usage. Or, start with flat-rate, get a better understanding of your usage, and move to the pay-as-you-go models for additional workloads.

To explore BigQuery and its capabilities a bit more, check out the sandbox; and when you’re ready to modernize your data warehouse with BigQuery then check out the documentation to streamline your migration process here.

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