Reviews 5,0
Reviews 5,0
Big Data on AWS
With AWS, you process massive data volumes securely, scalably, and cost-efficiently. Cloud-native technologies like Kinesis, EMR, and Redshift form the foundation for smart analytics and automated data flows.
Key topics
- Building efficient data pipelines and big data architectures in AWS.
- Using cloud-native tools for fast and secure data processing.
- Scaling resources optimally, applying security concepts, and keeping costs under control.
- Integrating analytics, AI, and machine learning into existing workflows.
- Preparing data visually and using it as the basis for strategic decisions.
Prerequisite
Those who already have some experience with IT systems or cloud services will find it easier to get started – deep prior knowledge is not required.
Target group
Designed for everyone who wants to build practical big data expertise with AWS: developers, data analysts, and architects who actively shape cloud strategies.
Mastering AWS tools for big data means processing data more efficiently, making better decisions, and creating real value in your organization.
Course content
Big Data – Core Concepts and Principles
- What big data is and why it matters
- Planning and building a data pipeline
- Requirements of modern data architectures
Big Data Collection and Transmission
- Overview: Methods for collecting data
- Strategies for data transmission
Real-Time Data Streaming with Amazon Kinesis
- Streaming mass data in real time with Kinesis
- Amazon’s Kinesis product family
- Routing data via Kinesis Firehose
- Video streaming with Kinesis Video Streams
- Real-time analytics with Kinesis Data Analytics
Scalable Data Management on AWS
- AWS storage types and their use cases
- Principles of modern storage strategies
- Choosing the right storage model for your needs
Processing and Analyzing Large Data Volumes
- Approaches to processing large datasets
- Running analytics with Amazon Athena
Hadoop and Amazon EMR for Big Data
- Introduction to Amazon EMR and Hadoop concepts
- Ingesting data efficiently – best practices
- Using Amazon EMR hands-on
- Architectural principles of Amazon EMR
Amazon EMR in Practice
- Building and running your own applications
- Setting up and configuring clusters
- Reviewing results from completed jobs
Frameworks and Libraries in Hadoop
- Hadoop ecosystem and available libraries
- Additional frameworks on Amazon EMR
Monitoring and Web Interfaces on Amazon EMR
- Using Hue as a web interface in Amazon EMR
- Monitoring cluster performance
Spark-Based Analytics on Amazon EMR
- Core concepts and how Spark works
- Running Spark in Amazon EMR environments
Automating ETL with AWS Glue
- Understanding and configuring AWS Glue
- Orchestrating and managing Glue jobs
Data Warehouse Analytics with Amazon Redshift
- Data warehouses compared to traditional databases
- Overview of the Amazon Redshift platform
- Architecture and setup of Amazon Redshift
Security and Data Protection in AWS
- Security concepts for Amazon deployments
- Amazon EMR Security overview
- Understanding AWS Identity & Access Management
- Protecting and securing data assets
- Amazon Kinesis Security overview
- Amazon DynamoDB Security overview
- Amazon Redshift Security overview
Keeping AWS Big Data Costs Under Control
- TCO analysis for Amazon EMR deployments
- Pricing options for Amazon EC2
- Amazon Kinesis cost models
- Cost optimization with Amazon DynamoDB
- Amazon Redshift pricing overview
- Budget strategies using AWS cost tools
Data Visualization and Workflow Management
- Visualization strategies in the big data context
- Using Amazon QuickSight for data visualization
- Orchestrating workflows for big data processes
Architecture Patterns for Big Data Systems
- Overview of proven architecture patterns
Wrap-up and Next Steps
- What to explore next
Up to 100% funding for your continuing education
Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.
Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.
What does Big Data on AWS mean?
- Big Data on AWS refers to using Amazon cloud services such as EMR, Redshift, and Kinesis to securely store, efficiently process, and purposefully analyze large volumes of data.
Who should take a Big Data on AWS course?
- Particularly relevant for professionals in IT, data science, and business intelligence – and for anyone who analyzes data professionally or shares responsibility for data-driven decisions.
What topics does the training cover?
- Key areas include data processing with EMR, visualization with QuickSight, storage in S3, streaming with Kinesis, and security and cost management strategies.
Do I need prior knowledge?
- Basic cloud knowledge and a foundational understanding of data processing are helpful. Experience with AWS or programming is an advantage, but not a requirement.
What career benefits does this training offer?
- Strong AWS and big data skills increase your chances of landing roles in cloud architecture, data engineering, or analytics – across industries and company sizes.
Does the training lead to a certification?
- Yes. The training concludes with an official AWS certification – an internationally recognized credential for proven knowledge in big data and cloud technology.
Why choose AWS for big data?
- AWS offers scalable infrastructure, high availability, extensive analytics capabilities, and a broad portfolio of services – ideal for diverse data formats, workflows, and real-time applications.
