The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment.
This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.
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Introduction to Data Engineering
This module discusses the role of data engineering and motivates the claim why data engineering should be done in the Cloud
  • Module introduction
  • The role of a data engineer
  • Data engineering challenges
  • Introduction to BigQuery
  • Data lakes and data warehouses
  • Transactional databases versus data warehouses
  • Partner effectively with other data teams
  • Manage data access and governance
  • Demo: Finding PII in your dataset with the DLP API
  • Build production-ready pipelines
  • Google Cloud customer case study
  • Lab Intro: Using BigQuery to do Analysis
  • LAB: Using BigQuery to do Analysis: In this lab, you analyze 2 different public datasets, run queries on them, separately and then combined, to derive interesting insights.
  • QUIZ
Building a Data Lake
In this module, we describe what data lake is and how to use Cloud Storage as your data lake on Google Cloud.
  • Module Introduction
  • Introduction to data lakes
  • Data storage and ETL options on Google Cloud
  • Build a data lake using Cloud Storage
  • Secure Cloud Storage
  • Store all sorts of data types
  • Cloud SQL as a relational data lake
  • Lab Intro: Loading Taxi Data into Google Cloud SQL
  • LAB: Loading Taxi Data into Google Cloud SQL 2.5:In this lab you will import data from CSV text files into Cloud SQL and then carry out some basic data analysis using simple queries.
  • QUIZ
Building a Data Warehouse
In this module, we talk about BigQuery as a data warehousing option on Google Cloud
  • Module Introduction
  • The modern data warehouse
  • Introduction to BigQuery
  • Demo: Querying TB of data in seconds
  • Get started with BigQuery
  • Load data into BigQuery
  • Lab Intro: Loading Data into BigQuery
  • LAB: Loading data into BigQuery: This lab focuses on how to ingest data into tables inside of BigQuery.
  • Explore schemas
  • Demo: Exploring Schemas
  • Schema design
  • Nested and repeated fields
  • Demo: Nested and repeated fields
  • Design the optimal schema for BigQuery
  • Lab Intro: Working with JSON and Array data in BigQuery
  • LAB: Working with JSON and Array data in BigQuery 2.5: In this lab you will work with semi-structured data (ingesting JSON, Array data types) inside of BigQuery. You will practice loading, querying, troubleshooting, and unnesting various semi-structured datasets.
  • Optimize with partitioning and clustering
  • Lab Intro: Partitioned Tables in BigQuery
  • LAB: Partitioned Tables in Google BigQuery:This lab focuses on how to query partitioned datasets and how to create your own dataset partitions to improve query performance, which reduces cost.
  • Review
  • QUIZ
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