Unlock the full potential of AWS with the Data Processing and Orchestration on AWS course. Delivered over 2 days (16 hours), this course offers a comprehensive understanding of how to design, implement, and manage end-to-end data pipelines using a suite of AWS services.

Participants will learn to orchestrate data workflows effectively by leveraging services like AWS Glue, Step Functions, Lambda, CloudWatch, and more. Real-world scenarios and hands-on labs—such as incremental data loading from S3 to Redshift and building a data lake using AWS Lake Formation—ensure learners gain practical skills in data ingestion, processing, storage, and visualization.

This course emphasizes best practices in security, disaster recovery, and cost optimization to help professionals manage scalable and resilient data solutions on the AWS platform.

The course covers key areas such as:

• Introduction to Data Workflows on AWS: Understand the importance of data orchestration and where it fits in the AWS ecosystem.
• Batch and Real-Time Ingestion: Learn to ingest data using Amazon S3 for batch and Amazon Kinesis Firehose and AWS IoT Core for real-time data.
• Data Storage and Lake Management: Explore data warehousing with Amazon Redshift and Athena, and manage structured/unstructured data lakes using AWS Lake Formation.
• Serverless Data Processing: Implement ETL workflows using AWS Glue and AWS Lambda for scalable data transformation.
• Workflow Orchestration: Automate data pipelines using AWS Step Functions for sequencing multi-step processing tasks.
• Monitoring and Alerting: Utilize Amazon CloudWatch for monitoring pipeline health, setting alarms, and troubleshooting.
• Data Visualization and Analytics: Integrate with tools like Amazon QuickSight and Grafana to derive actionable insights from processed data.
• Security, Cost, and Disaster Recovery: Apply best practices in IAM, encryption, cost-efficiency, and resilience planning for robust data pipelines.

Course Prerequisites

To ensure success in this course, participants should meet the following prerequisites:

• Basic understanding of cloud computing concepts
• Familiarity with core AWS services and the AWS Management Console
• Introductory knowledge of scripting or programming (Python preferred)
• Understanding of data ingestion, storage, and processing concepts
• Awareness of networking fundamentals (e.g., VPCs, IP addressing) is helpful

These prerequisites ensure that learners are equipped to comprehend the course content while still making it accessible to a wide range of professionals.

Target Audience

The Data Processing and Orchestration on AWS course is designed for professionals who want to build and manage efficient data pipelines in the cloud, including:

• Data Engineers
• Data Scientists
• Cloud Architects
• Solution Architects
• IT Managers
• Database Administrators
• DevOps Engineers
• Big Data Analysts
• System Integrators
• IT Security Specialists
• Data Analysts
• Software Developers (Cloud/Big Data)
• Business Intelligence Professionals
• AWS Cloud Practitioners
• IT Consultants working on cloud-based data workflows

Why Choose us

⭢ Live Online Training (Duration : 16 Hours)

⭢ Guaranteed to run classes

⭢ Experienced & certified trainers

⭢ Query Handling session

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Learning Objectives

After completing the Data Processing and Orchestration on AWS course, learners will be able to:

• Understand the core principles of data pipelines and orchestration in AWS
• Select the appropriate AWS services for various ingestion, storage, and processing needs
• Implement batch and real-time data ingestion using S3, Kinesis, and IoT Core
• Create and manage data lakes using AWS Lake Formation
• Automate serverless ETL jobs using AWS Glue and AWS Lambda
• Build orchestrated workflows with AWS Step Functions for complex processing scenarios
• Monitor data workflows and set up alerts using Amazon CloudWatch
• Visualize processed data with tools like Amazon QuickSight and Grafana
• Apply security, cost-efficiency, and disaster recovery best practices
• Build scalable, maintainable data pipelines suitable for enterprise environments