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The Path to Insights: Data Models and Pipelines

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Overview

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Design and analyze data models and databases, and learn essential extract, transform, load (ETL) processes to prepare data for business analysis and goals. Engage in hands-on activities simulating job tasks, guided by Google BI professionals. This is the second course in the Google Business Intelligence Certificate, a series designed to prepare you for an entry-level business intelligence role.

Syllabus

  • Data models and pipelines
    • Introduction to Course 2
    • Ed: Overcome imposter syndrome
    • Course 2 overview
    • Welcome to module 1
    • Data modeling, design patterns, and schemas
    • Get the facts with dimensional models
    • Dimensional models with star and snowflake schemas
    • Design efficient database systems with schemas
    • Different data types, different databases
    • Database comparison checklist
    • Practice Quiz: Test your knowledge: Data modeling, schemas, and databases
    • The shape of the data
    • Design useful database schemas
    • Four key elements of database schemas
    • Review a database schema
    • Practice Quiz: Test your knowledge: Choose the right database
    • Data pipelines and the ETL process
    • Maximize data through the ETL process
    • Choose the right tool for the job
    • Business intelligence tools and their applications
    • ETL-specific tools and their applications
    • Practice Quiz: Test your knowledge: How data moves
    • Introduction to Dataflow
    • Practice Quiz: [Optional] Activity: Create a Google Cloud account
    • Guide to Dataflow
    • Practice Quiz: [Optional] Activity: Create a streaming pipeline in Dataflow
    • Coding with Python
    • Python applications and resources
    • Gather information from stakeholders
    • Merge data from multiple sources with BigQuery
    • Practice Quiz: Activity: Set up a sandbox and query a public dataset in BigQuery
    • Unify data with target tables
    • Practice Quiz: Activity: Create a target table in BigQuery
    • Activity Exemplar: Create a target table in BigQuery
    • Case study: Wayfair - Working with stakeholders to create a pipeline
    • Wrap-up
    • Glossary terms from course 2, module 1
    • Graded Quiz: Module 1 challenge
    • Discover data formats
    • Database features and components
    • [Optional] Review Google Data Analytics Certificate content about BigQuery
    • [Optional] Review Google Data Analytics Certificate content about SQL best practices
  • Dynamic database design
    • Welcome to module 2
    • Data marts, data lakes, and the ETL process
    • ETL versus ELT
    • The five factors of database performance
    • A guide to the five factors of database performance
    • Optimize database performance
    • Indexes, partitions, and other ways to optimize
    • Practice Quiz: Activity: Partition data and create indexes in BigQuery
    • Activity Exemplar: Partition data and create indexes in BigQuery
    • Case study: Deloitte - Optimizing outdated database systems
    • The five factors in action
    • Determine the most efficient query
    • Practice Quiz: Test your knowledge: Database performance
    • Wrap-up
    • Glossary terms from course 2, module 2
    • Graded Quiz: Module 2 challenge
  • Optimize ETL processes
    • Welcome to module 3
    • The importance of quality testing
    • Seven elements of quality testing
    • Monitor data quality with SQL
    • Mana: Quality data is useful data
    • Practice Quiz: Test your knowledge: Optimize pipelines and ETL processes
    • Conformity from source to destination
    • Sample data dictionary and data lineage
    • Check your schema
    • Schema-validation checklist
    • Practice Quiz: Activity: Evaluate a schema using a validation checklist
    • Activity Exemplar: Evaluate a schema using a validation checklist
    • Practice Quiz: Test your knowledge: Data schema validation
    • Verify business rules
    • Business rules
    • Database performance testing in an ETL context
    • Defend against known issues
    • Burak: Evolving technology
    • Case study: FeatureBase, Part 2: Alternative solutions to pipeline systems
    • Practice Quiz: Test your knowledge: Business rules and performance testing
    • Wrap-up
    • Glossary terms from course 2, module 3
    • Graded Quiz: Module 3 challenge
    • Why data integrity is important
    • Demystify metadata
  • Course 2 end-of-course project
    • Welcome to module 4
    • Continue your end-of-course project
    • Explore Course 2 end-of-course project scenarios
    • Course 2 workplace scenario overview: Cyclistic
    • Cyclistic datasets
    • Observe the Cyclistic team in action
    • Practice Quiz: Activity: Create your target table for Cyclistic
    • Activity Exemplar: Create your target table for Cyclistic
    • Course 2 workplace scenario overview: Google Fiber
    • Google Fiber datasets
    • [Optional] Merge Google Fiber datasets in Tableau
    • Practice Quiz: Activity: Create your target table for Google Fiber
    • Activity Exemplar: Create your target table for Google Fiber
    • Tips for ongoing success with your end-of-course project
    • Luis: Tips for interview preparation
    • Graded Quiz: Assess your Course 2 end-of-course project
    • Course wrap-up
    • Course 2 glossary
    • Get started on Course 3
    • Course 2 resources and citations

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