This course takes you through the complete process of data handling, starting with AWS data processing services. You’ll begin with AWS Lambda, learning how to integrate serverless functions and manage scalable data pipelines. With practical exercises, you’ll explore how AWS Glue helps automate data preparation and manage complex ETL jobs, making data lake partitioning and modification of Glue Data Catalog easy to understand. Hands-on experience with Glue Studio and DataBrew will further enhance your knowledge in preparing data for analysis.
AWS Data Processing and Analysis
AWS Data Processing and Analysis
This course is part of AWS Certified Data Analytics Specialty (2023) Hands-on Specialization
Instructor: Packt - Course Instructors
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
8 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Integrate and scale data pipelines using AWS Lambda and Glue for efficient data processing
Analyze real-time data streams with Kinesis Analytics and OpenSearch to gain actionable insights
Implement security measures and manage data workflows for high-performance analysis
Analyze real-time data streams with Kinesis Analytics and OpenSearch to gain actionable insights
Skills you'll gain
Details to know
Shareable certificate
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Recently updated!
October 2024
Assessments
1 assignment
Taught in English
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This course is part of the AWS Certified Data Analytics Specialty (2023) Hands-on Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 2 modules in this course
In this module, we will delve into AWS processing services, beginning with an introduction to AWS Lambda and Glue. You’ll learn how to integrate these tools for serverless and ETL workflows. We will also explore advanced topics such as Glue ETL job execution, Lambda's cost optimization strategies, and EMR’s integration with other AWS services like Apache Spark, Hive, and Hadoop. Hands-on exercises will cover using Spark with Kinesis and Redshift, and how to process data lakes with EMR.
What's included
35 videos2 readings
In this module, we will focus on analyzing and querying data using AWS’s powerful analytics services. We begin with an introduction to Kinesis Analytics, OpenSearch, and Athena, followed by performance tuning and security best practices. Through hands-on exercises, you’ll build real-world applications to monitor data streams, optimize queries using Glue and Athena, and perform data warehousing with Redshift. Additionally, we’ll explore Redshift's durability, distribution styles, and newer features like AQUA and serverless options to improve large-scale data analytics.
What's included
32 videos1 reading1 assignment
Instructor
Offered by
Recommended if you're interested in Cloud Computing
The Open University
University of Maryland, College Park
Johns Hopkins University
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.