BigQuery is Google Cloud's fully managed, petabyte-scale, and cost-effective analytics data warehouse that lets you run analytics over vast amounts of data in near real time.
BigQuery interfaces include Google Cloud console interface and the BigQuery command-line tool. Developers and data scientists can use client libraries with familiar programming including Python, Java, JavaScript, and Go, as well as BigQuery's REST API and RPC API to transform and manage data.
Use the BigQuery sandbox to query a public dataset and visualize the results, learn about the BigQuery sandbox limitations, and learn how to upgrade from the BigQuery sandbox.
Shows how to use the Google Cloud console to work with BigQuery projects, display resources (such as datasets and tables), compose and run SQL queries, and view query and job histories.
BigQuery offers a capacity-based compute pricing model for customers who need additional capacity or prefer a predictable cost for query workloads rather than the on-demand price (per TiB of data processed).
For information about how to run a SQL query in BigQuery, see Running interactive and batch query jobs. For more information about query optimization in general, see Introduction to optimizing query performance.
In this first post, we will look at how data warehouses change business decision making, how BigQuery solves problems with traditional data warehouses, and dive into a high-level overview of BigQuery architecture and how to quickly get started with BigQuery.
Gives an overview of BigQuery storage, including descriptions of tables, table clones, views, snapshots, and datasets, and strategies for performance optimizations such as partitioning and clustering.
With these jobs, the query runs continuously, letting you analyze incoming data in BigQuery in real time and then write the results to a BigQuery table, or export the results to Bigtable or Pub/Sub.
