BigQuery for Data Analysts

CloudLabs

Projects

Assignment

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Lifetime Access

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Course Overview

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.

At the end of the training, participants will be able to:

Pre-requisite

  1. Google Cloud Platform Fundamentals
  2. Experience with data analysis using SQL
  3. Experience with business intelligence, reporting, and data visualization
  4. Familiarity with extract, transform, and load (ETL) activities
  5. Familiarity with data modeling

Duarion

4 days

Course Outline

  1. Examine the current state big data landscape
  2. Exploit the cloud for big data
  3. Define what is BigQuery
  4. Explore BigQuery use cases
  1. Organize BigQuery projects
  2. Store data in BigQuery
  3. Leverage BigQuery architecture
  4. Interact with BigQuery from the web console and command API
  1. Define BigQuery table schemas
  2. De-normalize relational data structures for efficient processing
  3. Execute BigQuery jobs
  4. Maximize performance and reduce cost with destination tables and caching
  1. Prepare and transform data for upload into BigQuery
  2. Stage and ingest data
  3. Load data
  1. Understand the BigQuery pricing model
  2. Calculate the cost of queries
  3. Avoid quota limitations
  1. Leverage user-defined functions
  1. Model big data schemas to optimize performance and cost
  2. Define nested, repeated, and nested repeated fields using JSON
  3. Query nested and repeated fields in BigQuery
  1. Explore how the BigQuery architecture impacts query processing
  2. Analyze the effects of Join and Group By statements on performance
  3. Optimize reads with table decorators and table ranges
  4. Diagnose and resolve query performance issues
  1. Recognize common BigQuery SQL errors
  2. Prevent resource errors
  3. Diagnose HTTP errors
  1. Define access control lists (ACLs) to protect data
  2. Understand project and dataset access controls
  3. Apply views for row-level security
  1. Export data for backups and for use with third-party tools
  2. Run export jobs
  1. Integrate BigQuery with Google Sheets
  2. Analyze BigQuery data with R
  1. Mine Google Analytics and Google AdSense data with BigQuery
  1. Visualize data using Google Cloud Datala

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