Apache Airflow using Google Cloud Composer: Introduction
Apache Airflow using Google Cloud Composer: Introduction, available at $49.99, has an average rating of 4.3, with 37 lectures, 5 quizzes, based on 207 reviews, and has 979 subscribers.
You will learn about Understand automation of Task workflows through Airflow Airflow Architecture – On Premise (local install), Cloud, single node, multiple node How to use connection functionality to connect to different systems to automate data pipelines What is Google cloud Big query and briefly how it can be used in Dataware housing as well as in Airflow DAG Master core functionalities such as DAGs, Operators, Tasks through hands on demonstrations Understand advanced functionalities like XCOM, Branching, Subdags through hands on demonstrations Get an overview understanding on SLAs, Kubernetes executor functionality in Apache Airflow The source files of Python DAG programs (9 .py files) used in demonstration are available for download towards practice for students This course is ideal for individuals who are People interested in Data warehousing, Big data, Data engineering or People interested in Automated tools for task workflow scheduling or Student interested to know about Airflow or Professional to wish to explore as how Apache Airflow can be used in Task scheduling and building Data pipelines It is particularly useful for People interested in Data warehousing, Big data, Data engineering or People interested in Automated tools for task workflow scheduling or Student interested to know about Airflow or Professional to wish to explore as how Apache Airflow can be used in Task scheduling and building Data pipelines.
Enroll now: Apache Airflow using Google Cloud Composer: Introduction
Summary
Title: Apache Airflow using Google Cloud Composer: Introduction
Price: $49.99
Average Rating: 4.3
Number of Lectures: 37
Number of Quizzes: 5
Number of Published Lectures: 37
Number of Published Quizzes: 5
Number of Curriculum Items: 42
Number of Published Curriculum Objects: 42
Number of Practice Tests: 1
Number of Published Practice Tests: 1
Original Price: ₹1,799
Quality Status: approved
Status: Live
What You Will Learn
- Understand automation of Task workflows through Airflow
- Airflow Architecture – On Premise (local install), Cloud, single node, multiple node
- How to use connection functionality to connect to different systems to automate data pipelines
- What is Google cloud Big query and briefly how it can be used in Dataware housing as well as in Airflow DAG
- Master core functionalities such as DAGs, Operators, Tasks through hands on demonstrations
- Understand advanced functionalities like XCOM, Branching, Subdags through hands on demonstrations
- Get an overview understanding on SLAs, Kubernetes executor functionality in Apache Airflow
- The source files of Python DAG programs (9 .py files) used in demonstration are available for download towards practice for students
Who Should Attend
- People interested in Data warehousing, Big data, Data engineering
- People interested in Automated tools for task workflow scheduling
- Student interested to know about Airflow
- Professional to wish to explore as how Apache Airflow can be used in Task scheduling and building Data pipelines
Target Audiences
- People interested in Data warehousing, Big data, Data engineering
- People interested in Automated tools for task workflow scheduling
- Student interested to know about Airflow
- Professional to wish to explore as how Apache Airflow can be used in Task scheduling and building Data pipelines
Apache Airflow is an open-source platform to programmatically author, schedule and monitor workflows.
Cloud Composer is a fully managed workflow orchestration service that empowers you to author, schedule, and monitor pipelines that span across clouds and on-premises data centers. Built on the popular Apache Airflow open source project and operated using the Python programming language, Cloud Composer is free from lock-in and easy to use.
With Apache Airflow hosted on cloud (‘Google’ Cloud composer) and hence,this will assist learner to focus on Apache Airflow product functionality and thereby learn quickly, without any hassles of having Apache Airflow installed locally on a machine.
Cloud Composer pipelines are configured as directed acyclic graphs (DAGs) using Python, making it easy for users of any experience level to author and schedule a workflow. One-click deployment yields instant access to a rich library of connectors and multiple graphical representations of your workflow in action, increasing pipeline reliability by making troubleshooting easy.
This course is designed with beginner in mind, that is first time users of cloud composer / Apache airflow. The course is structured in such a way that it has presentation to discuss the concepts initially and then provides with hands on demonstration to make the understanding better.
The python DAG programs used in demonstration source file (9 Python files) are available for download toward further practice by students.
Happy learning!!!
Course Curriculum
Chapter 1: Course Overview
Lecture 1: Course Overview – Topics of coverage
Chapter 2: Introduction
Lecture 1: Data pipe lines & Uses cases for Apache Airflow
Lecture 2: What is Task and why Orchestration needed?
Lecture 3: What is Apache Airflow & environment options?
Chapter 3: What is Airflow – Directed Acyclic Graph (DAG) & operators?
Lecture 1: What is Airflow – Directed Acyclic Graph
Chapter 4: Apache Airflow architecture
Lecture 1: Apache Airflow architecture
Lecture 2: Apache Airflow – Single Node vs Multinode
Chapter 5: Google Cloud Platform: Cloud composer used as Apache Airflow
Lecture 1: Provisioning Google Composer – Apache Airflow environment – Part 1
Lecture 2: Provisoning Google Composer – Apache Airflow environment – Part 2
Lecture 3: Navigation – Cloud composer(Apache airflow) Web UI navigation
Chapter 6: Understanding Apache Airflow program structure
Lecture 1: Understanding Apache Airflow program structure
Chapter 7: Activity 1 : Create and submit Apache airflow DAG program
Lecture 1: Activity 1 : Create and submit Apache airflow DAG program
Chapter 8: Activity 2: Using Template functionality in Apache Airflow program
Lecture 1: Activity 2: Using Templating functionality in Apache Airflow program
Lecture 2: Activity 2: Using Templating functionality in Apache Airflow program – Part 2
Chapter 9: Using Variables in Apache Airflow
Lecture 1: What is variable in Apache Airflow and when to use them?
Lecture 2: Activity 3: Variables usage in DAG python program
Chapter 10: Activity 4: Calling Bash script in different folder / different machine.
Lecture 1: Activity 4: Calling Bash script in different folder / different machine – Part1
Lecture 2: Activity 4: Calling Bash script in different folder / different machine – Part 2
Chapter 11: Creating connections in Apache Airflow
Lecture 1: Why connections are required in Apache Airflow
Lecture 2: Navigation and creating connection steps in Apache Airflow
Lecture 3: Activity 5: Creating and testing connection in Apache Airflow – Part 1
Lecture 4: Activity 5: Creating and testing connection in Apache Airflow – Part 2
Chapter 12: Using Google's cloud Bigquery with Apache Airflow Datapipelines
Lecture 1: What is Google Cloud BigQuery?
Lecture 2: Creation of custom Bigquery table
Lecture 3: BigQuery data upload from Excel sheet (CSV file)
Lecture 4: Activity 6 : Apache Airflow DAG Data pipeline for BigQuery
Chapter 13: Cross communication between tasks – XCOM
Lecture 1: What is xcom?
Lecture 2: Activity 7: xcom demonstration pipeline
Chapter 14: Branching based on conditions
Lecture 1: Overview about Branching Functionality
Lecture 2: Activity 8: Tasks Branching demonstration
Chapter 15: SUBDAGS
Lecture 1: What is a Subdag?
Lecture 2: Activity 9: SubDAGs demonstration
Chapter 16: Other functionalities
Lecture 1: Service Level Agreement with Airflow
Lecture 2: Airflow now support Kubernetes
Lecture 3: Sensors
Chapter 17: Practice test , common interview questions – To test your knowledge
Chapter 18: Apache Airflow Vs Apache Beam and Spark – Quick comparison
Lecture 1: Apache Airflow Vs Apache Beam and Spark – Quick comparison
Chapter 19: Bonus
Lecture 1: Concluding remarks
Instructors
-
Guha Rajan M., B.Engg, MBA
Founder and CEO – Capstone Solutions, Trainer
Rating Distribution
- 1 stars: 13 votes
- 2 stars: 15 votes
- 3 stars: 43 votes
- 4 stars: 72 votes
- 5 stars: 64 votes
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