Google BigQuery Foundation | Analyze Data in BigQuery

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From Loading and Querying Data to Optimizing Performance for Data Analysts, Data Engineers, and Data Scientists


BigQuery is a popular data warehouse service that allows you to easily work with petabytes of data. Learn how to quickly get up to speed with BigQuery and start querying and analyzing data efficiently using the BigQuery graphical user interface, command line utilities, and even programming languages. If you are familiar with basic database concepts, like tables, you are ready to start learning one of the most important data analytics platforms available.

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 standard SQL and taking advantage of flexible pricing models across on-demand and flat-rate options.

To use BigQuery effectively though, you need to understand how BigQuery is designed. Building a data warehouse and designing data models in BigQuery is fundamentally different than building and modeling in relational databases like Oracle, SQL Server and PostgreSQL. In this course, you will learn about BigQuery’s architecture and how it influences how we structure and query data.

“BigQuery is server-less, highly scalable, and cost-effective Data warehouse designed for Google cloud Platform (GCP) to store and query petabytes of data.”

In this course we will learn:

  • Brief introduction to the set of services Google Cloud provides.
  • Bigquery Pricing models for Storage, Querying
  • Why BigQuery
  • What is Google BigQuery
  • BigQuery Features
  • BigQuery Architecture
  • Overview of BigQuery Storge
  • Pricing models
  • Hands on BigQuery
  • Use Cases
  • Case Study

We will later add on the SQL queries in BigQuery.

Who this course is for:

  • Anyone who works with data and has familiarity with database concepts and needs to work with data warehouses and other large stores of data in BigQuery, one of

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