Modernizing Data Lakes and Data Warehouses with GCP
5.0 /5- Offered byCoursera
Modernizing Data Lakes and Data Warehouses with GCP at Coursera Overview
Duration | 9 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Modernizing Data Lakes and Data Warehouses with GCP at Coursera Highlights
- Earn a shareable certificate upon completion.
- Flexible deadlines according to your schedule.
Modernizing Data Lakes and Data Warehouses with GCP at Coursera Course details
- 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.
- New! CERTIFICATE COMPLETION CHALLENGE to unlock benefits from Coursera and Google Cloud
- Enroll and complete Cloud Engineering with Google Cloud or Cloud Architecture with Google Cloud Professional Certificate or Data Engineering with Google Cloud Professional Certificate before November 8, 2020 to receive the following benefits;
- => Google Cloud t-shirt, for the first 1,000 eligible learners to complete. While supplies last. > Exclusive access to Big => Interview ($950 value) and career coaching
- => 30 days free access to Qwiklabs ($50 value) to earn Google Cloud recognized skill badges by completing challenge quests
Modernizing Data Lakes and Data Warehouses with GCP at Coursera Curriculum
Introduction
Course Introduction
Getting Started with Google Cloud and Qwiklabs
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
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
Course Summary
Modernizing Data Lakes and Data Warehouses with GCP at Coursera Admission Process
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