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BigQuery for Data Analysts online training

BigQuery for Data Analysts


BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you have no infrastructure to manage and don't need a database administrator, use familiar SQL and can take advantage of pay-as-you-go model.


  • Google Cloud Platform Fundamentals
  • Experience with data analysis using SQL
  • Experience with business intelligence, reporting, and data visualization
  • Familiarity with extract, transform, and load (ETL) activities
  • Familiarity with data modeling


  • It is a 12 days program and extends up to 2hrs each.
  • The format is 40% theory, 60% Hands-on.

  • It is a 3 days program and extends up to 8hrs each.
  • The format is 40% theory, 60% Hands-on.
    Private Classroom arranged on request and minimum attendies for batch is 4.

course content

  • Introduction to BigQuery
    • Examine the current state big data landscape
    • Exploit the cloud for big data
    • Define what is BigQuery
    • Explore BigQuery use cases
  • BigQuery Functional Overview
    • Organize BigQuery projects
    • Store data in BigQuery
    • Leverage BigQuery architecture
    • Interact with BigQuery from the web console and command API
  • BigQuery Fundamentals
    • Define BigQuery table schemas
    • De-normalize relational data structures for efficient processing
    • Execute BigQuery jobs
    • Maximize performance and reduce cost with destination tables and caching
  • Transform, Stage, and Load Data
    • Prepare and transform data for upload into BigQuery
    • Stage and ingest data
    • Load data
  • Pricing and Quotas
    • Understand the BigQuery pricing model
    • Calculate the cost of queries
    • Avoid quota limitations
  • Clauses and Functions
    • Leverage user-defined functions
  • Nested and Repeated Fields
    • Model big data schemas to optimize performance and cost
    • Define nested, repeated, and nested repeated fields using JSON
    • Query nested and repeated fields in BigQuery
  • Tuning Query Performance
    • Explore how the BigQuery architecture impacts query processing
    • Analyze the effects of Join and Group By statements on performance
    • Optimize reads with table decorators and table ranges
    • Diagnose and resolve query performance issues
  • Troubleshooting Errors
    • Recognize common BigQuery SQL errors
    • Prevent resource errors
    • Diagnose HTTP errors
  • Access Control
    • Define access control lists (ACLs) to protect data
    • Understand project and dataset access controls
    • Apply views for row-level security
  • Exporting Data
    • Export data for backups and for use with third-party tools
    • Run export jobs
  • Interfacing with External Tools
    • Integrate BigQuery with Google Sheets
    • Analyze BigQuery data with R
  • Working with Google Analytics Premium Data
    • Mine Google Analytics and Google AdSense data with BigQuery
  • Data Visualization
    • Visualize data using Google Cloud Datalab


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