Data engineering on AWS : the complete training.

Format:

Video

Contributor:

Prajapati, Ashish, instructor.

Packt Publishing, publisher.

Language:

English

Subjects (All):

Cloud computing.

Big data.

Amazon Web Services (Firm).

Physical Description:

1 online resource (1 video file (18 hr., 56 min.)) : sound, color.

Edition:

[First edition].

Place of Publication:

[Birmingham, United Kingdom] : Packt Publishing, 2025.

Summary:

This course begins by laying the foundation of data analytics and introducing AWS data engineering services. You'll start with AWS Glue, learning to catalog, transform, and manage data using workflows, job bookmarks, and quality checks, followed by visual data preparation with Glue Databrew. Next, you'll move into data warehousing with Amazon Redshift, from cluster creation to serverless deployment and performance tuning. As the journey continues, the focus shifts to real-time data processing with Amazon Kinesis and MSK, covering stream management, Flink applications, and Kafka integration. You'll then explore big data processing using Amazon EMR, understanding MapReduce, Spark, and cost-effective serverless execution. The course then guides you through building data lakes using AWS Lake Formation and querying them efficiently with Amazon Athena. In the final stages, you'll visualize data using Amazon QuickSight and orchestrate pipelines through Step Functions and AppFlow. You'll also gain experience with AWS data migration tools like DMS and DataSync. The course concludes with extended AWS services including Lambda, S3, EC2, and DynamoDB, empowering you to design and manage complete, scalable data platforms in the cloud. To access the supplementary materials, scroll down to the 'Resources' section above the 'Course Outline' and click 'Supplemental Content.' This will either initiate a download or redirect you to GitHub. What you will learn Build ETL pipelines using AWS Glue and Databrew Deploy and optimize Amazon Redshift clusters Stream and process data with Kinesis and MSK Run big data jobs using Amazon EMR and Spark Create and manage secure AWS data lakes Visualize and query data using Athena and QuickSight Audience This course is designed for aspiring data engineers, cloud engineers, and developers who wish to deepen their expertise in AWS-based data architecture. Prerequisites include basic knowledge of cloud computing, databases, and programming (preferably Python or SQL). Familiarity with AWS services is helpful but not mandatory. About the Author Ashish Prajapati: Ashish Prajapati is a seasoned technical professional based in London, UK, with extensive expertise in virtualization and cloud migration. He is passionate about simplifying cloud learning for both individuals and enterprises, making it accessible and enjoyable. With a strong background in cloud technologies, Ashish integrates real-world examples into his training, enabling learners to grasp complex concepts effortlessly. His work has empowered countless cloud enthusiasts to start their cloud journey, master the fundamentals, and successfully achieve cloud certifications.

Notes:

OCLC-licensed vendor bibliographic record.

ISBN:

1-80638-609-7

OCLC:

1530938367