AWS Certified Data Engineer - Associate: Hands-On Training & Exam Prep

What you will learn:

Master key AWS data analytics services like Amazon Redshift, Kinesis, Athena, and QuickSight
Learn to build and deploy scalable and reliable data analytics solutions on AWS
Gain a comprehensive understanding of data security, management, and governance on AWS
Prepare for the AWS Certified Data Engineer - Associate exam with in-depth knowledge and practice questions

Description

Unlock your data engineering potential with our comprehensive AWS Certified Data Engineer - Associate course. Dive into the world of cloud analytics, gaining hands-on experience with essential AWS services like Amazon Redshift, Kinesis, Athena, and QuickSight. This course is designed to equip you with the knowledge and practical skills necessary to excel as a data engineer on the AWS platform.

Beyond mastering these powerful tools, you'll gain a deep understanding of data security, management, and architectural best practices on AWS. This course will serve as your guide to the AWS Certified Data Engineer - Associate certification, providing you with a structured path to success. Whether you're a seasoned data professional or a beginner eager to enter the field, this course is the perfect launchpad for your cloud analytics career.

Curriculum

Data Engineering Fundamentals

This section lays the groundwork for a successful data engineering career. Explore the roles and responsibilities of data engineers, delve into different types of data storage systems, and understand the concepts of ACID and BASE, OLTP vs OLAP, and the 4 V's of Big Data. You'll also gain a comprehensive understanding of data engineering concepts through in-depth lectures and practical exercises.

Big Data Technologies

Dive deep into the world of Big Data technologies, gaining insights into various approaches and solutions for handling massive datasets. This section covers key concepts, practical implementations, and real-world scenarios, ensuring you have a solid foundation in this rapidly evolving field.

AWS Certification: Exam Preparation and Beyond

This section guides you through the process of preparing for the AWS Certified Data Engineer - Associate exam. Discover valuable tips and strategies to maximize your chances of success, including exam application procedures and helpful resources for ESL learners. Learn about the benefits of the certification and how it can propel your career forward.

Domain 1: Data Collection

Gain proficiency in collecting and ingesting data from diverse sources using AWS services. This section covers Kinesis Data Streams, Kinesis Data Firehose, Kinesis Data Analytics, Amazon Managed Streaming for Apache Kafka (MSK), AWS Data Migration Service (DMS), Amazon Simple Queue Service (SQS), Amazon MQ, Amazon Simple Notification Service (SNS), AWS Direct Connect, and AWS Snow Family. Through hands-on demos, you'll learn practical applications of these services and their integration into real-world data pipelines.

Domain 2: Data Storage and Management

Master the art of storing and managing data effectively using AWS solutions. Explore Amazon Simple Storage Service (S3), Amazon Elastic Block Store (EBS), Amazon Elastic File System (EFS), AWS Backup, Amazon DynamoDB, DynamoDB Accelerator (DAX), Amazon RDS, Amazon Elasticache, and Amazon Keyspaces. Through hands-on demonstrations, you'll gain practical experience with these services, including their capabilities, limitations, and best practices for efficient data storage and management.

Domain 3: Data Processing

This section focuses on processing data efficiently and effectively using AWS services. Explore Amazon EC2, AWS Glue, Elastic MapReduce (EMR), AWS Lambda, AWS Step Functions, and AWS Lake Formation. Through hands-on demos, you'll gain practical experience with these services, including their capabilities, limitations, and best practices for efficient data processing.

Domain 4: Data Analysis and Visualization

Gain proficiency in analyzing and visualizing data using powerful AWS services. Explore Amazon Athena, Amazon Redshift, Amazon OpenSearch, Amazon QuickSight, Amazon Neptune, Amazon DataZone, and Amazon Timestream. Through hands-on demonstrations, you'll gain practical experience with these services, including their capabilities, limitations, and best practices for data analysis and visualization.

Amazon Virtual Private Cloud (VPC)

This section covers the fundamentals of AWS Virtual Private Cloud (VPC), a critical component of secure and scalable cloud deployments. Explore the core concepts of VPCs, including network isolation, subnets, routing, security groups, and network address translation (NAT). Through in-depth lectures, you'll gain a comprehensive understanding of VPCs and their role in building secure and reliable cloud environments.

Domain 5: Data Security

Ensure the confidentiality, integrity, and availability of your data with a deep understanding of AWS security best practices. This section covers key security principles, services, and tools, including IAM (Identity and Access Management), KMS (Key Management Service), CloudTrail, CloudWatch, and more. You'll learn how to secure data at rest, in transit, and in use, ensuring the highest level of protection for your valuable information.

AWS Data Analytics Reference Architecture

Gain insights into a comprehensive reference architecture for data analytics on AWS. This section provides a practical guide to building a robust and scalable data analytics platform, outlining best practices and common patterns for designing and deploying efficient and secure data solutions in the cloud.

Practice Questions and Answers

Strengthen your understanding and test your knowledge with a comprehensive set of practice questions and detailed explanations. This section covers key concepts, scenarios, and real-world applications, providing valuable insights into the types of questions you can expect on the AWS Certified Data Engineer - Associate exam. By reviewing these questions and their explanations, you'll gain confidence and prepare for the real-world challenges of data engineering on AWS.