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Google BigQuery for Data Analysts

  • COURSE INTRODUCTION: The training course on Google BigQuery for Data Analysts offered by SFJ, assist students to build on the concepts and expertise through the Google BigQuery. Through instructor-led online classrooms, demonstrations, and hands-on labs, you’ll learn to store, transform, analyze, and visualize data using Google BigQuery.
  • PRE-REQUISITES: Basics Computer Knowledge. Experience using a SQL-like query language to analyze data is required
  • DURATION: 6 Days (48 HRS)
  • CERTIFICATON: At the end of the course you will get the certificate for completing the training.

Course Content:

Module 1: Introducing Google BigQuery

  • Understand the purpose of and use cases for Google BigQuery
  • Describe ways in which customers have used Google BigQuery to improve their businesses

Module 2: BigQuery Functional Overview

  • Describe the components of a BigQuery project
  • Identify how BigQuery stores data and list the advantages of the storage model
  • Understand the architecture of BigQuery and how queries are processed
  • Describe the methods of interacting with BigQuery

Module 3: BigQuery Fundamentals

  • Describe the purpose of denormalizing data
  • Identify the purpose and structure of BigQuery schemas and data types
  • Explain the types of actions available in BigQuery jobs
  • Understand the purpose of and advantages of BigQuery destinations tables and caching

Module 4: Ingesting, Transforming, and Storing Data

  • Describe the methods for ingesting data, transforming data, and storing data using BigQuery
  • Explain the function of BigQuery federated queries

Module 5: Pricing and Quotas

  • Explain the advantages of the BigQuery pricing model
  • Use the pricing calculator to calculate storage and query costs
  • Identify the quotas that apply to BigQuery projects

Module 6: Clauses and Functions

  • Explain the differences between BigQuery SQL and ANSI SQL
  • Identify the purpose of and use cases for user-defined functions
  • Explain the purpose of various BigQuery functions

Module 7: Nested and Repeated Fields

  • Identify the purpose and structure of BigQuery nested, repeated, and nested repeated fields
  • Describe the use cases for nested, repeated, and nested repeated fields

Module 8: Query Performance

  • Explain the impact of the following in query performance: JOIN and GROUP BY, table wildcards,
  • and table decorators
  • Identify various best practices for optimizing query performance

Module 9: Troubleshooting Errors

  • Describe how to handle the most common BigQuery errors: request encoding errors, resource errors, and HTTP errors

Module 10: Access Control

  • Describe the purpose of access control lists in BigQuery
  • List and explain the project and dataset roles available in BigQuery
  • Apply views for row-level security
  • Manage access to datasets using dataset-level ACLs
  • Set row-level permissions using views

Module 11: Exporting Data

  • List the methods of exporting data from BigQuery and the data formats available
  • Describe the process of creating a job to export data from BigQuery
  • Explain the purpose of wildcard exports to partition export data

Module 12: Interfacing with External Tools

  • Describe how to use external tools to interface with BigQuery, including: spreadsheets, ODBC and JDBC drivers, the BigQuery encrypted client, and R

Module 13: Working with Google Analytics Premium Data

  • Describe the schema of the Google Analytics Premium and AdSense data exported to BigQuery

Module 14: Data Visualization

  • Describe the options available for visualizing BigQuery data

Use Cases and Case Study