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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.
Read more
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.
Read more
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
Register for this course and see more details by visiting:
OpenCourser.com/course/6vafqr/aws
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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.
Read more
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.
Read more
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
Good to know
Know what's good ,
what to watch for , 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
Save this course
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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
Show steps
Refresh your understanding of querying data with EMR before beginning the course to strengthen foundational skills and ensure success.
Browse courses on
ETL
Show steps
-
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
Show steps
Enhance your learning experience by organizing your course materials, notes, and assignments in a structured manner.
Show steps
-
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
Show steps
Reinforce your understanding of SQL fundamentals to strengthen your foundation for data processing concepts.
Show steps
-
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.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Seek guidance from experienced data professionals
Show steps
Expand your knowledge and gain valuable insights by connecting with experienced data professionals who can provide mentorship and guidance.
Show steps
-
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
Show steps
Practice querying data using EMR to solidify your understanding of data processing techniques.
Browse courses on
Querying Data
Show steps
-
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
Show steps
Enhance your understanding of EMR by following step-by-step tutorials on clustering, data processing, and analytics.
Browse courses on
Amazon EMR
Show steps
-
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)
Show steps
Practice configuring ELB to distribute traffic across multiple EC2 instances, improving the scalability and availability of your application.
Browse courses on
ELB
Show steps
-
Create an ELB
-
Configure health checks
-
Add instances to the ELB
-
Test the ELB configuration
Build a Data Pipeline with EMR
Show steps
Solidify your understanding of data processing by building a data pipeline with EMR during the course.
Browse courses on
Data Processing
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
Test your skills and gain practical experience by participating in data analytics hackathons, where you can solve real-world data processing challenges.
Show steps
-
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
Show steps
Solidify your understanding by sharing your knowledge and helping others learn about data processing concepts.
Show steps
-
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
Show steps
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
Show steps
-
Define data sources
-
Create Kinesis streams and applications
-
Develop data processing logic
-
Deploy and monitor the pipeline
Practice Querying with EMR
Show steps
Refresh your understanding of querying data with EMR before beginning the course to strengthen foundational skills and ensure success.
Browse courses on
ETL
Show steps
Show steps
Show steps
- 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
Show steps
Enhance your learning experience by organizing your course materials, notes, and assignments in a structured manner.
Show steps
Show steps
Show steps
- 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
Show steps
Reinforce your understanding of SQL fundamentals to strengthen your foundation for data processing concepts.
Show steps
Show steps
Show steps
- 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.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Seek guidance from experienced data professionals
Show steps
Expand your knowledge and gain valuable insights by connecting with experienced data professionals who can provide mentorship and guidance.
Show steps
Show steps
Show steps
- 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
Show steps
Practice querying data using EMR to solidify your understanding of data processing techniques.
Browse courses on
Querying Data
Show steps
Show steps
Show steps
- 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
Show steps
Enhance your understanding of EMR by following step-by-step tutorials on clustering, data processing, and analytics.
Browse courses on
Amazon EMR
Show steps
Show steps
Show steps
- 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)
Show steps
Practice configuring ELB to distribute traffic across multiple EC2 instances, improving the scalability and availability of your application.
Browse courses on
ELB
Show steps
Show steps
Show steps
- Create an ELB
- Configure health checks
- Add instances to the ELB
- Test the ELB configuration
Build a Data Pipeline with EMR
Show steps
Solidify your understanding of data processing by building a data pipeline with EMR during the course.
Browse courses on
Data Processing
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
Test your skills and gain practical experience by participating in data analytics hackathons, where you can solve real-world data processing challenges.
Show steps
Show steps
Show steps
- 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
Show steps
Solidify your understanding by sharing your knowledge and helping others learn about data processing concepts.
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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 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.
Data Integration Engineer
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.
Big Data Architect
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.
Data Warehouse Engineer
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.
Data Analyst
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.
Solutions Architect
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.
Cloud Engineer
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.
Data Engineer
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.
Big Data Analyst
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.
Business Intelligence Analyst
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.
Database Administrator
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.
Machine Learning Engineer
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.
Data Lake Engineer
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.
DevOps Engineer
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.
Data Scientist
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.
For more career information including salaries, visit:
OpenCourser.com/course/6vafqr/aws
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
AWS Data Processing .
Save
A practical guide to Apache Spark, covering topics such as data storage, data processing, data analytics, and data machine learning.
A comprehensive guide to Hadoop, covering topics such as data storage, data processing, data analytics, and data visualization.
A comprehensive guide to Spark, covering topics such as data storage, data processing, data analytics, and data machine learning.
A beginner's guide to Amazon EMR, covering topics such as cluster management, data storage, data processing, and data analysis.
Provides a comprehensive overview of big data. It covers a wide range of topics, including data ingestion, storage, processing, and analytics.
Provides a comprehensive overview of Spark, a popular open-source big data processing framework. It covers a wide range of topics, including Spark architecture, programming models, and use cases.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6vafqr/aws
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Effort
3 weeks of study, 3-3:30 hours/week
Level
Introductory
Via
Coursera
Institution
Whizlabs
Instructor
Whizlabs Instructor
Language
English
This course belongs to a
collection
called
Exam Prep DAS-C01: AWS Certified Data Analytics Specialty. It's comprised of five courses:
Good to know
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
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