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 Platform 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. Learners will get hands-on experience with data lakes and warehouses on Google Cloud Platform using QwikLabs.
Offered By
Google Cloud
About this Course
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Intermediate Level
Approx. 11 hours to complete
Suggested: 22 hours/week
English
Subtitles: English
Syllabus - What you will learn from this course
5 minutes to complete
Introduction
This module gives an introduction of the Data Engineering specialization and the course.
5 minutes to complete
2 videos (Total 5 min)
2 hours to complete
Introduction to Data Engineering
This module describes the role of a data engineer and motivates the claim why data engineering should be done in the Cloud
2 hours to complete
13 videos (Total 55 min)
13 videos
Analyze data engineering challenges8m
Intro to BigQuery3m
Data Lakes and Data Warehouses5m
Demo: Federated Queries with BigQuery6m
Transactional Databases vs Data Warehouses4m
Partner effectively with other data teams6m
Manage data access and governance2m
Demo: Finding PII in your dataset with DLP API1m
Build production-ready pipelines2m
Review GCP customer case study3m
Recap1m
Lab Intro: Using BigQuery to do Analysis17s
1 practice exercise
Introduction to Data Engineering30m
2 hours to complete
Building a Data Lake
In this module, we describe what data lake is and how to use Google Cloud Storage as you data lake on GCP
2 hours to complete
10 videos (Total 58 min)
10 videos
Data Storage and ETL options on GCP4m
Building a Data Lake using Cloud Storage10m
Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions7m
Securing Cloud Storage5m
Storing All Sorts of Data Types5m
Demo: Running federated queries on Parquet and ORC files in BigQuery4m
Storing Relational Data in the Cloud1m
Cloud SQL as a relational Data Lake7m
Lab: Loading Taxi Data into Cloud SQL27s
1 practice exercise
Building a Data Lake4m
4 hours to complete
Building a data warehouse
In this module, we talk about BigQuery as a data warehousing option on GCP
4 hours to complete
17 videos (Total 93 min)
17 videos
Intro to BigQuery1m
Demo: Querying TB of Data in seconds7m
Getting Started9m
Loading Data11m
Lab Intro: Loading Data into BigQuery21s
Exploring Schemas24s
Demo: Exploring Schemas10m
Schema Design3m
Nested and Repeated Fields8m
Demo: Nested and Repeated Fields15m
Lab Intro: Working with JSON and Array Data in BigQuery13s
Optimizing with Partitioning and Clustering5m
Demo: Creating Partitioned Tables7m
Demo: Partitioning and Clustering6m
Preview: Transforming Batch and Streaming Data2m
Recap1m
1 practice exercise
Building a Data Warehouse4m
2 minutes to complete
Summary
This module reviews all the topics covered in the course
2 minutes to complete
1 video (Total 2 min)
1 video
4.6
23 ReviewsTop reviews from Modernizing Data Lakes and Data Warehouses with GCP
Good introduction and overview of the field of data engineering, data lakes and modern data warehouses and a hands-on walkthrough of all the technologies related to solving these problems on GCP.
The course offers a nice description of what modern DWH means, which are the differences between classic DWH and cloud DWH. You have the chance to work with the new cloud concept on GCP.
Offered by
Google Cloud
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
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