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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. With BigQuery, there's no infrastructure to set up or manage, letting you focus on finding meaningful insights using GoogleSQL and taking advantage of flexible pricing models across on-demand and flat-rate options. Learn more

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Documentation resources

Find quickstarts and guides, review key references, and get help with common issues.
Explore self-paced training from Google Cloud Skills Boost, use cases, reference architectures, and code samples with examples of how to use and connect Google Cloud services.
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Data Warehouse with BigQuery Jump Start Solution

Deploy and use a sample data warehouse with BigQuery.

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BigQuery for Data Warehousing

Learn best practices for extracting, transforming, and loading your data into Google Cloud with BigQuery.

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Training and tutorials

Preprocessing BigQuery Data with PySpark on Dataproc

Learn to create a data processing pipeline using Apache Spark with Dataproc on Google Cloud. It is a common use case in data science and data engineering to read data from one storage location, perform transformations on it and write it into another storage location.

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BigQuery For Data Analysis

Learn how to query, ingest, optimize, visualize, and even build machine learning models in SQL inside of BigQuery.

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BigQuery for Marketing Analysts

Get repeatable, scalable, and valuable insights into your data by learning how to query it using BigQuery.

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BigQuery for Machine Learning

Experiment with different model types in BigQuery Machine Learning, and learn what makes a good model.

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Performance optimizations for Java applications

Applications aren’t always as performant as we’d like, especially as usage and business grows. There are several well-known optimizations used to address performance issues. What’s less well known is when and where to apply each one. In this session,

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