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 Googles 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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