AWS Data Processing from Coursera

AWS: Data Processing Course is the second course of AWS Certified Data Analytics Specialty Specialization. This course focuses on providing data processing solutions. The entire course is designed to teach learners the concept of EMR and Extract, Transform and Load. This course also put emphasis on ETL services and Data Processing solutions in AWS. The course is divided into three modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 3:30-4:00 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners.

Module 1: Introduction : Extract, Transform and Load Jobs

Module 2: Introduction: EMR

Module 3: ETL Services and Data Processing Solution in AWS

It is recommended that folks should have experience of working with AWS services for designing, building, securing, and maintaining analytics solutions for understanding this course. By the end of this course, learners will be able to :

-Analyze Modeling Concepts and train Machine Learning Models

-Examine performance of machine learning models

-Implement automatic model tuning by training a model

What's inside

Syllabus

Introduction : Extract, Transform and Load Jobs

Welcome to Week 1 of the AWS: Data Processing .This week, we will focus on determining the appropriate data processing solution requirements and gain an understanding of Extract, Transform, Load (ETL) jobs. We will also gain hands-on experience with implementing ETL jobs to move data between different sources and destinations. By the end of the week, we should have a good understanding of how to effectively process data using ETL jobs and the requirements needed to do so.

Introduction: EMR

Welcome to Week 2 of the AWS: Data Processing . This week, we will be introduced to Amazon EMR and its various applications such as Spark, Hudi, Hbase, TensorFlow, Flink, Presto, and Hue. We will also learn how to design a solution for transforming and preparing data for analysis using EMR. Through practical demonstrations, we will gain a solid understanding of how to effectively use EMR to process, transform, and analyze large datasets. By the end of the week, we should have a good understanding of how to leverage EMR for data processing and analytics needs.

ETL Services and Data Processing Solution in AWS

Welcome to Week 3 of the AWS: Data Processing. This week, we will focus on automating and operationalizing a data processing solution using AWS Glue and EMR. We will also compare batch and streaming ETL services to determine the most appropriate solution for our needs.By the end of the week, we should have a good understanding of how to use AWS services to automate and operationalize data processing workflows.

Good to know

Know what's good

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, and possible dealbreakers

Covers concepts and skills which are commonly used in the industry

Introduces learners to widely adopted tools and technologies

Taught by instructors with extensive expertise in the field

Course material includes assessments that test knowledge

Requires learners to have existing experience with relevant concepts

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AWS Data Processing with these activities:

Practice Querying with EMR

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Refresh your understanding of querying data with EMR before beginning the course to strengthen foundational skills and ensure success.

Browse courses on ETL

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Review the documentation for EMR Query Editor.
Set up a practice environment with Amazon EMR.
Execute basic queries on sample data.

Organize course materials for easy reference

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Enhance your learning experience by organizing your course materials, notes, and assignments in a structured manner.

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Review all provided course materials, including lecture notes, slides, and assignments.
Create a system or folder structure to organize the materials logically.
Regularly update the organization as new materials are added.

Revisit SQL basics

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Reinforce your understanding of SQL fundamentals to strengthen your foundation for data processing concepts.

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Review online tutorials or documentation on SQL syntax and commands.
Practice writing simple SQL queries to retrieve, filter, and manipulate data.
Solve online SQL challenges or exercises to test your comprehension.

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Seek guidance from experienced data professionals

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Expand your knowledge and gain valuable insights by connecting with experienced data professionals who can provide mentorship and guidance.

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Identify potential mentors through networking events, industry forums, or online platforms.
Reach out to potential mentors and express your interest in their guidance.
Establish regular communication to ask questions, discuss challenges, and receive feedback.

Practice Querying Data Using EMR

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Practice querying data using EMR to solidify your understanding of data processing techniques.

Browse courses on Querying Data

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Set up an EMR cluster.
Load data into the cluster.
Write queries to retrieve data from the cluster.
Test your queries to ensure they are working correctly.

Explore Amazon EMR tutorials

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Enhance your understanding of EMR by following step-by-step tutorials on clustering, data processing, and analytics.

Browse courses on Amazon EMR

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Identify suitable Amazon EMR tutorials from the AWS documentation or other reliable sources.
Follow the instructions in the tutorials to create and manage EMR clusters.
Run sample data processing jobs on your EMR clusters.
Troubleshoot any issues encountered during the tutorials.

Configure ELB (Elastic Load Balancer)

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Practice configuring ELB to distribute traffic across multiple EC2 instances, improving the scalability and availability of your application.

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Create an ELB
Configure health checks
Add instances to the ELB
Test the ELB configuration

Build a Data Pipeline with EMR

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Solidify your understanding of data processing by building a data pipeline with EMR during the course.

Browse courses on Data Processing

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Design the data pipeline architecture.
Set up the EMR cluster and necessary services.
Develop the ETL logic using PySpark.
Deploy and monitor the data pipeline.

Design an EMR cluster for data processing

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Create a detailed design document for an EMR cluster, specifying instance types, storage options, and security configurations to meet specific data processing requirements.

Browse courses on EMR

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Determine cluster requirements
Select instance types
Configure storage options
Implement security measures
Document the design

Design a Data Processing Solution for a Real-World Scenario

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Create a design for a data processing solution for a real-world scenario to apply your knowledge of data processing principles.

Browse courses on AWS

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Identify a real-world problem that requires data processing.
Gather requirements for the data processing solution.
Design a solution architecture for the data processing solution.
Develop a plan for implementing the data processing solution.
Present your design to a group of peers for feedback.

Design an ETL pipeline using AWS Glue

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Apply your knowledge by creating a functional ETL pipeline using AWS Glue, showcasing your ability to extract, transform, and load data.

Browse courses on Data Pipelines

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Define the source and destination data sources for your ETL pipeline.
Create an AWS Glue job using the AWS Glue console or API.
Configure the job to extract data from the source, transform it, and load it into the destination.
Test and debug the ETL pipeline to ensure accurate data transfer.

Collaborate on a data processing project using AWS Glue

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Join a peer group to work on a real-world data processing project using AWS Glue, gaining hands-on experience in data integration, transformation, and analysis.

Browse courses on AWS Glue

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Form a peer group
Identify a project topic
Collect and prepare data
Develop ETL pipelines using AWS Glue
Analyze and visualize results

Participate in data analytics hackathons

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Test your skills and gain practical experience by participating in data analytics hackathons, where you can solve real-world data processing challenges.

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Identify relevant data analytics hackathons that align with your interests and skill level.
Form a team or participate individually in the hackathon.
Develop innovative data processing solutions within the given time frame.
Present your solution to a panel of judges and receive feedback.

Share your knowledge by mentoring junior data professionals

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Solidify your understanding by sharing your knowledge and helping others learn about data processing concepts.

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Identify opportunities to mentor junior data professionals through programs or initiatives.
Provide guidance on data processing techniques, tools, and best practices.
Share your experiences and insights to help mentees develop their skills.

Build a data pipeline using Amazon Kinesis

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Design and implement a data pipeline using Amazon Kinesis to capture, process, and analyze real-time data, providing valuable insights for decision-making.

Browse courses on Amazon Kinesis

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Define data sources
Create Kinesis streams and applications
Develop data processing logic
Deploy and monitor the pipeline

Career center

Learners who complete AWS Data Processing will develop knowledge and skills that may be useful to these careers:

ETL Developer

ETL Developers are responsible for designing and developing data pipelines for data integration and transformation. The AWS: Data Processing course can greatly benefit ETL Developers by providing them with hands-on experience in implementing ETL jobs using AWS services. The course covers various techniques and best practices for efficient data processing, making it a valuable resource for professionals in this field.

See salaries and explore the career path for ETL Developer

Data Integration Engineer

Data Integration Engineers are responsible for integrating data from multiple sources into a single, cohesive system. The AWS: Data Processing course can be highly beneficial for Data Integration Engineers as it provides hands-on experience in implementing data integration pipelines using AWS services. The course covers various techniques and best practices for efficient data integration, making it a valuable resource for professionals in this field.

See salaries and explore the career path for Data Integration Engineer

Big Data Architect

Big Data Architects are responsible for designing and managing data systems that can handle large volumes of data. The AWS: Data Processing course can provide Big Data Architects with a deep understanding of the various tools and services offered by AWS, enabling them to design efficient and scalable data processing solutions for big data projects.

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Data Warehouse Engineer

Data Warehouse Engineers are responsible for designing, building, and maintaining data warehouses. The AWS: Data Processing course can provide Data Warehouse Engineers with knowledge of AWS services and best practices for data processing, enabling them to build and manage efficient and scalable data warehouses in the cloud.

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Data Analyst

Data Analysts are responsible for analyzing and interpreting data to extract meaningful insights, which involves gathering and preparing data for analysis. The AWS: Data Processing course can be highly beneficial for Data Analysts as it covers ETL processes and various AWS services that streamline data processing, making data preparation more efficient and accurate, thus allowing for more accurate and reliable analysis.

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Solutions Architect

Solutions Architects are responsible for designing and implementing cloud-based solutions. The AWS: Data Processing course can provide Solutions Architects with knowledge of AWS data processing services and best practices. By completing this course, Solutions Architects can enhance their ability to design and deploy scalable and efficient data processing solutions for their customers.

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Cloud Engineer

Cloud Engineers are responsible for designing, building, and maintaining cloud-based systems. The AWS: Data Processing course can be beneficial for Cloud Engineers as it provides them with a deeper understanding of AWS services and technologies related to data processing. By completing this course, Cloud Engineers can enhance their ability to design and manage scalable and efficient data processing solutions on AWS.

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Data Engineer

A Data Engineer designs and builds systems that can store and process large datasets. With a strong understanding of the concepts of Extract, Transform, and Load, professionals in this career can perform these functions efficiently using various tools and services offered on AWS. Completion of the AWS: Data Processing course can expand the knowledge of a Data Engineer by further improving their ability to implement data processing pipelines using a variety of services and technologies.

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Big Data Analyst

Big Data Analysts are responsible for analyzing large datasets to extract meaningful insights. The AWS: Data Processing course can be beneficial for Big Data Analysts as it provides a solid foundation in data processing concepts, tools, and technologies, enabling them to effectively analyze and process big data.

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Business Intelligence Analyst

Business Intelligence Analysts use data to make informed decisions and solve business problems. The AWS: Data Processing course can be beneficial for Business Intelligence Analysts by providing them with hands-on experience in implementing ETL jobs, using EMR for data processing, and leveraging AWS services for data processing and analytics.

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Database Administrator

Database Administrators are responsible for managing and maintaining database systems. The AWS: Data Processing course can enhance the skills of Database Administrators by providing them with knowledge of cloud-based data processing services and technologies offered by AWS, enabling them to manage and optimize data processing tasks more efficiently.

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Machine Learning Engineer

Machine Learning Engineers are responsible for building, testing, and deploying machine learning models. The AWS: Data Processing course can aid Machine Learning Engineers in building effective data pipelines for ML model training by leveraging the various services and techniques taught in the course.

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Data Lake Engineer

Data Lake Engineers are responsible for designing, building, and managing data lakes. The AWS: Data Processing course can be useful for Data Lake Engineers as it provides them with knowledge of AWS services such as S3, EMR, and Glue, which are commonly used in data lake architectures.

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DevOps Engineer

DevOps Engineers are responsible for bridging the gap between development and operations teams. The AWS: Data Processing course may be useful for DevOps Engineers as it provides an understanding of the tools and techniques used for data processing in the cloud. This knowledge can help DevOps Engineers automate and streamline the data processing pipeline, enabling faster and more efficient software delivery.

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Data Scientist

Data Scientists use data to solve complex problems and extract meaningful insights. The AWS: Data Processing course may be useful for Data Scientists as it provides a foundation in data processing concepts and tools that are commonly used in data science projects.

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AWS Data Processing

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