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€ 1.990,-
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€ 2.368,10
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Booking number
42558
Venue
Online
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6 Events
€ 1.990,-
plus VAT.
€ 2.368,10
incl. VAT.
Booking number
42558
Venue
Online
6 Events
plus VAT.
incl. VAT.
Booking number
Venue
0 Events
Become a certified
Machine Learning Engineer
Machine Learning Engineer
This course is part of the certified Master Class "Machine Learning Engineer". If you book the entire Master Class, you save over 15 percent compared to booking this individual module.
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In-house training
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A 3-day intermediate course designed for professionals seeking an in-depth look at data engineering practices and solutions on AWS.
Contents
Through a balanced combination of theory, practical labs, and activities, participants learn to design, build, optimize, and secure data engineering solutions using AWS services.
From foundational concepts to hands-on implementation of data lakes, data warehouses, and both batch and streaming data pipelines, this course equips data professionals with the skills needed to architect and manage modern data solutions at scale.
Day 1
1. Data Engineering Roles and Key Concepts
- Role of a Data Engineer
- Key functions of a Data Engineer
- Data Personas
- Data Discovery
- AWS Data Services
2. AWS Data Engineering Tools and Services
- Orchestration and Automation
- Data Engineering Security
- Monitoring
- Continuous Integration and Continuous Delivery
- Infrastructure as Code
- AWS Serverless Application Model
- Networking Considerations
- Cost Optimization Tools
3. Designing and Implementing Data Lakes
- Introduction to data lakes
- Data lake storage
- Ingest data into a data lake
- Catalog data
- Transform data
- Server data for consumption
- Hands-on lab: Setting up a data lake on AWS
4. Optimizing and Securing a Data Lake Solution
- Open Table Formats
- Security using AWS Lake Formation
- Setting permissions with Lake Formation
- Security and governance
- Troubleshooting
- Hands-on lab: Automating data lake creation using AWS Lake Formation Blueprints
Day 2
5. Data Warehouse Architecture and Design Principles
- Introduction to data warehouses
- Amazon Redshift Overview
- Ingesting data into Redshift
- Processing data
- Serving data for consumption
- Hands-on Lab: Setting up a Data Warehouse using Amazon Redshift Serverless
6. Performance Optimization Techniques for Data Warehouses
- Monitoring and optimization options
- Data optimization in Amazon Redshift
- Query optimization in Amazon Redshift
- Orchestration options
7. Security and Access Control for Data Warehouses
- Authentication and access control in Amazon Redshift
- Data security in Amazon Redshift
- Auditing and compliance in Amazon Redshift
- Hands-on lab: Managing Access Control in Redshift
8. Designing Batch Data Pipelines
- Introduction to batch data pipelines
- Designing a batch data pipeline
- AWS services for batch data processing
9. Implementing Strategies for Batch Data Pipeline
- Elements of a batch data pipeline
- Processing and transforming data
- Integrating and cataloging your data
- Serving data for consumption
- Hands-on lab: A Day in the Life of a Data Engineer
Day 3
10. Optimizing, Orchestrating, and Securing Batch Data Pipelines
- Optimizing the batch data pipeline
- Orchestrating the batch data pipeline
- Securing the batch data pipeline
- Hands-on lab: Orchestrating data processing in Spark using AWS Step Functions
11. Streaming Data Architecture Patterns
- Introduction to streaming data pipelines
- Ingesting data from stream sources
- Streaming data ingestion services
- Storing streaming data
- Processing Streaming Data
- Analyzing Streaming Data with AWS Services
- Hands-on lab: Streaming Analytics with Amazon Managed Service for Apache Flink
12. Optimizing and Securing Streaming Solutions
- Optimizing a streaming data solution
- Securing a streaming data pipeline
- Considerations regarding compliance
- Hands-on lab: Access Control with Amazon Managed Streaming for Apache Kafka
Requirements
- Familiarity with basic machine learning concepts, such as supervised and unsupervised learning, regression, classification, and clustering algorithms
- Working knowledge of Python programming language and common data science libraries such as NumPy, Pandas, and Scikit-learn
- Basic understanding of cloud computing concepts and familiarity with the AWS platform
- Familiarity with SQL and relational databases is recommended but not mandatory.
- Experience with version control systems like Git is beneficial but not required.
Learning environment
Benefits
- Understanding the foundational roles and key concepts of data engineering, including data personas, data discovery, and relevant AWS services
- Identifying and explaining the various AWS tools and services crucial for data engineering, encompassing orchestration, security, monitoring, CI/CD, IaC, networking, and cost optimization
- Designing and implementing a data lake solution on AWS, including storage, data ingestion, transformation, and serving data for consumption
- Optimizing and securing a data lake solution by implementing open table formats, security measures, and troubleshooting common issues
- Designing and setting up a data warehouse using Amazon Redshift Serverless, understanding its architecture, data ingestion, processing, and serving capabilities
- Applying performance optimization techniques to data warehouses in Amazon Redshift, including monitoring, data optimization, query optimization, and orchestration
- Managing security and access control for data warehouses in Amazon Redshift, understanding authentication, data security, auditing, and compliance
- Designing effective batch data pipelines using appropriate AWS services for processing and transforming data
- Implementing comprehensive strategies for batch data pipelines, covering data processing, transformation, integration, cataloging, and serving data for consumption
- Optimizing, orchestrating, and securing batch data pipelines, demonstrating advanced skills in data processing automation and security
- Architecting streaming data pipelines, understanding various use cases, ingestion, storage, processing, and analysis using AWS services
- Optimizing and securing streaming data solutions, including compliance considerations and access control
Instructor
Vladimir Sabo
Methods
This course includes presentations, demonstrations, hands-on labs, and group exercises.
Final examination
Recommended for
This course is designed for professionals who are interested in designing, building, optimizing, and securing data engineering solutions using AWS services.
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