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IBM - ETL and Data Pipelines with Shell, Airflow and Kafka 

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ETL and Data Pipelines with Shell, Airflow and Kafka
 at 
Coursera 
Overview

Duration

11 hours

Start from

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Total fee

Free

Mode of learning

Online

Difficulty level

Intermediate

Official Website

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Credential

Certificate

ETL and Data Pipelines with Shell, Airflow and Kafka
 at 
Coursera 
Highlights

  • Flexible deadlines Reset deadlines in accordance to your schedule.
  • Shareable Certificate Earn a Certificate upon completion
  • 100% online Start instantly and learn at your own schedule.
  • Course 11 of 13 in the IBM Data Engineering
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ETL and Data Pipelines with Shell, Airflow and Kafka
 at 
Coursera 
Course details

More about this course
  • After taking this course, you will be able to describe two different approaches to converting raw data into analytics-ready data. One approach is the Extract, Transform, Load (ETL) process. The other contrasting approach is the Extract, Load, and Transform (ELT) process. ETL processes apply to data warehouses and data marts. ELT processes apply to data lakes, where the data is transformed on demand by the requesting/calling application.
  • Both ETL and ELT extract data from source systems, move the data through the data pipeline, and store the data in destination systems. During this course, you will experience how ELT and ETL processing differ and identify use cases for both.
  • You will identify methods and tools used for extracting the data, merging extracted data either logically or physically, and for importing data into data repositories. You will also define transformations to apply to source data to make the data credible, contextual, and accessible to data users. You will be able to outline some of the multiple methods for loading data into the destination system, verifying data quality, monitoring load failures, and the use of recovery mechanisms in case of failure.
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ETL and Data Pipelines with Shell, Airflow and Kafka
 at 
Coursera 
Curriculum

Data Processing Techniques

ETL Fundamentals

ELT Basics

Comparing ETL to ELT

Data Extraction Techniques

Introduction to Data Transformation Techniques

Data Loading Techniques

Course Introduction

Summary & Highlights

Practice Quiz: ETL and ELT Processes

Graded Quiz: ETL and ELT Processes

ETL & Data Pipelines: Tools and Techniques

ETL using Shell Scripting

Introduction to Data Pipelines

Key Data Pipeline Processes

Batch Versus Streaming Data Pipeline Use Cases

Data Pipeline Tools and Technologies

Linux Commands and Shell Scripting

ETL Techniques

Summary & Highlights

Summary & Highlights

Practice Quiz: ETL using Shell Scripts

Graded Quiz: ETL using Shell Scripts

Practice Quiz: An Introduction to Data Pipelines

Graded Quiz: An Introduction to Data Pipelines

Building Data Pipelines using Airflow

Apache Airflow Overview

Advantages of Using Data Pipelines as DAGs in Apache Airflow

Apache Airflow UI

Build DAG Using Airflow

Airflow Monitoring and Logging

Summary & Highlights

Practice Quiz: Using Apache Airflow to build Data Pipelines

Graded Quiz: Using Apache Airflow to build Data Pipelines

Building Streaming Pipelines using Kafka

Distributed Event Streaming Platform Components

Apache Kafka Overview

Building Event Streaming Pipelines using Kafka

Kafka Streaming Process

Summary & Highlights

Practice Quiz: Using Apache Kafka to build Pipelines for Streaming Data

Graded Quiz: Using Apache Kafka to build Pipelines for Streaming Data

Final Assignment

Project Overview

Congrats & Next Steps

Team & Acknowledgements

Final Quiz

ETL and Data Pipelines with Shell, Airflow and Kafka
 at 
Coursera 
Admission Process

    Important Dates

    May 25, 2024
    Course Commencement Date

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    Students Ratings & Reviews

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    Irfan khan
    ETL and Data Pipelines with Shell, Airflow and Kafka
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    5
    Learning Experience: Learning experience was good
    Faculty: Instructors taught well Curriculum was relevant and comprehensive
    Course Support: No career support provided
    Reviewed on 7 Feb 2022Read More
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    ETL and Data Pipelines with Shell, Airflow and Kafka
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