BigQuery is Google Cloud’s enterprise data warehouse designed to help you ingest, store, analyze, and visualize big data with ease.

Organizations rely on data warehouses to aggregate data from disparate sources, process it, and make it readily available for data analysis that supports their strategic decision-making.

You can ingest data into BigQuery either through batch uploading or streaming data directly to deliver real-time insights.

As a fully-managed data warehouse, Google takes care of the infrastructure so you can focus on analyzing your data up to petabyte scale.

BigQuery supports the same Structured Query Language, or SQL, for analyzing your data, which you may be familiar with if you’ve worked with ANSI-compliant relational databases in the past.

If you’re looking to create machine learning models using your enterprise data, you can do so with BigQuery ML.

With only a few lines of SQL, you can train and execute models on your BigQuery data without needing to move it around.

When it comes time to visualize your data, BigQuery integrates with Looker, as well as several other business intelligence tools across our partner ecosystem.

Now, how do you use BigQuery?

Luckily, it’s straightforward to get up and running with BigQuery.

After creating a GCP project, you can immediately start querying public data sets, which Google Cloud hosts and makes available to all BigQuery users, or you can load your own data into BigQuery to analyze.

Interacting with BigQuery to load data, run queries, or even create ML models can be done in three different ways.

First is by using the UI and the Cloud Console. Second is by using the BigQuery command line tool. And third is by making calls to the BigQuery API, using client libraries available in several languages.

BigQuery is integrated with Google Cloud’s Identity and Access Management Service so you can securely share your data and analytical insights across the organization.

What does it cost to use BigQuery?

With BigQuery, you pay for storing and querying data and 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, meaning you are charged per query by the amount of data processed, or flat rate for customers who want to purchase dedicated resources.

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