Modernizing Data Lakes and Data Warehouses with GCP
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Course Summary
Learn how to design, build, and operate data warehouses and data lakes on Google Cloud Platform (GCP).Key Learning Points
- Learn how to design and build data warehouses and data lakes on GCP
- Explore GCP tools for ingesting, transforming, and querying data
- Gain hands-on experience with real-world scenarios and case studies
Related Topics for further study
Learning Outcomes
- Design and build data warehouses and data lakes on GCP
- Ingest and transform data using GCP tools
- Query and analyze data in GCP
Prerequisites or good to have knowledge before taking this course
- Basic understanding of data modeling and SQL
- Familiarity with cloud computing concepts
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Video lectures
- Hands-on labs
- Real-world scenarios
Similar Courses
- Data Engineering, Big Data, and Machine Learning on GCP
- Data Engineering on Google Cloud Platform
Related Education Paths
- Google Cloud Certified - Professional Data Engineer
- Google Cloud Certified - Associate Cloud Engineer
Notable People in This Field
- Martin Fowler
- Ken Collier
Related Books
Description
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.
Outline
- Introduction
- Course Introduction
- Getting Started with Google Cloud and Qwiklabs
- Introduction to Data Engineering
- Explore the role of a data engineer
- Analyze data engineering challenges
- Intro to BigQuery
- Data Lakes and Data Warehouses
- Demo: Federated Queries with BigQuery
- Transactional Databases vs Data Warehouses
- Partner effectively with other data teams
- Manage data access and governance
- Demo: Finding PII in your dataset with DLP API
- Build production-ready pipelines
- Review GCP customer case study
- Recap
- Lab Intro: Using BigQuery to do Analysis
- Introduction to Data Engineering
- Building a Data Lake
- Introduction to Data Lakes
- Data Storage and ETL options on GCP
- Building a Data Lake using Cloud Storage
- Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions
- Securing Cloud Storage
- Storing All Sorts of Data Types
- Demo: Running federated queries on Parquet and ORC files in BigQuery
- Storing Relational Data in the Cloud
- Cloud SQL as a relational Data Lake
- Lab: Loading Taxi Data into Cloud SQL
- Building a Data Lake
- Building a data warehouse
- The Modern Data Warehouse
- Intro to BigQuery
- Demo: Querying TB of Data in seconds
- Getting Started
- Loading Data
- Lab Intro: Loading Data into BigQuery
- Exploring Schemas
- Demo: Exploring Schemas
- Schema Design
- Nested and Repeated Fields
- Demo: Nested and Repeated Fields
- Lab Intro: Working with JSON and Array Data in BigQuery
- Optimizing with Partitioning and Clustering
- Demo: Creating Partitioned Tables
- Demo: Partitioning and Clustering
- Preview: Transforming Batch and Streaming Data
- Recap
- Monitoring BigQuery usage and reservations
- Intro to BigQuery System Table Reports
- Demo: BigQuery System Tables with Data Studio
- BigQuery Pricing
- Building a Data Warehouse
- Summary
- Course Summary
Summary of User Reviews
Learn about data lakes and data warehouses on Google Cloud Platform (GCP) with this comprehensive course on Coursera. Students have praised the course for its practical approach to learning and comprehensive coverage of topics.Key Aspect Users Liked About This Course
Practical approach to learningPros from User Reviews
- Comprehensive coverage of topics
- Hands-on exercises help to reinforce learning
- Great for beginners to data lakes and data warehouses
- Course materials are well-structured and easy to follow
Cons from User Reviews
- Some users found the course too basic
- Lack of advanced topics for experienced professionals
- Course can be slow-paced at times
- Not enough emphasis on real-world applications
- Some users experienced technical difficulties with the platform
Google Cloud Training
4.7 Raiting
Modernizing Data Lakes and Data Warehouses with GCP
- 4.7
-
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The two key components of any data pipeline are data lakes and warehouses. Learners will get hands-on experience with data lakes and warehouses on Google Cloud Platform using QwikLabs....
Modernizing Data Lakes and Data Warehouses with GCP en Español
- 4.9
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Los dos componentes clave de cualquier canalización de datos son los data lakes y los almacenes de datos. Introducción Introducción al curso...
Modernizing Data Lakes and Data Warehouses with GCP 日本語版
- 0.0
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すべてのデータ パイプラインには、データレイクとデータ ウェアハウスという 2 つの主要コンポーネントがあります。このコースでは、それぞれのストレージのユースケースに焦点を当て、Google Cloud Platform で利用可能なデータレイクおよびデータ ウェアハウスのソリューションを技術的に詳しく説明します。 また、データ エンジニアの役割や、適切なデータ パイプラインが事業運営にもたらすメリットについて述べ、クラウド環境でデータ エンジニアリングを行うべき理由を説明します。受講者は Qwiklabs を使用して、Google Cloud Platform のデータレイクとデータ ウェアハウスを実際に使ってみることができます。 はじめに コース概要...
Modernizing Data Lakes and Data Warehouses with GCP en Français
- 0.0
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Les deux principaux composants de tout pipeline de données sont les lacs de données et les entrepôts de données. Introduction Présentation du cours...
