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Modernizing Data Lakes and Data Warehouses with Google Cloud

  • CMDBID 1087
  • Course Code GCPMDLDW
  • Duration 1 Days
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Google Cloud Course

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Overview

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.

Outline

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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