AWS Data Engineer Certification Training Course
Learn from the Best, Learn from TopD
Features of This Course
Why Choose AWS Data Engineer Certification Training?
This AWS Data Engineer Certification Course helps you prepare for the official AWS Certified Data Engineer Associate certification. Learn to implement various AWS services for designing data models, managing data life cycles, and ensuring data quality. The key features of this course are:
- Aligned with the AWS Certified Data Engineer Associate (DEA-C01) exam
- 30 Hours of Live Instructor-led Training
- 5+ Industry Use Cases, 20+ Hands-on Demos
- 9+ Assignments & Knowledge Checks
- Capstone Project
Join us today to get hands-on experience with state-of-the-art AWS services & acquire the necessary skills to become an accredited AWS Data Engineer.
Course Curriculum
Topics:
- Introduction to AWS Services
- AWS Global Infrastructure
- Data Engineering Fundamentals
- Properties of Data
- Basics of ETL
- Data Ingestion
- Modern Data Workflows
- Data Ingestion Patterns and Services
- Streaming vs. Batch Data Ingestion
- Replayability of Data Ingestion Pipelines
- Stateful and Stateless Data Transactions
- Reading Data from Streaming Sources
- Reading Data from Batch Sources
- Configuring Ingestion Options
- Batch Ingestion
- Consuming Data APIs
- Schedulers and Event Triggers
- Calling a Lambda Function from Amazon Kinesis
- Allowlists for IP Addresses
- Throttling and Overcoming Rate Limits
- Streaming Data Distribution
Hands-on:
- Setting Up a Data Stream Using Amazon Kinesis or Amazon MSK
- Consuming Data from the Stream Using AWS Lambda
- Configuring Batch Data Ingestion Using AWS Glue
- Scheduling Data Ingestion Jobs with Amazon EventBridge
Skills You Will Learn:
- Data Ingestion Patterns
- Batch Data Ingestion
- Configuring Data Ingestion
Topics:
- Data Transformation
- Overview of ETL Pipelines
- Business Requirements for ETL
- Data Characteristics: Volume, Velocity, and Variety
- ETL Pipeline Implementation
- Apache Spark for Data Processing
- Data Sources Connection
- Integrating Data from Multiple Sources
- Optimizing ETL Pipelines
- Optimizing Container Usage
- Cost Optimization Strategies
- Data Transformation Services
- Data Format Transformation
- Troubleshooting
- Making Data Available
- Creating Data APIs
Hands-on:
- Building an ETL Pipeline with AWS Glue
- Implementing Data Transformation Services Based on Requirements
- Connecting to Different Data Sources
- Creating Data APIs to Make Data Available to Other Systems
Skills You Will Learn:
- ETL Pipeline Design
- Optimizing ETL Processes
- Data Processing
- Managing Data APIs
Topics:
- Data Pipeline Orchestration
- Integrating AWS Services for ETL Pipelines
- Event-Driven Architecture
- Configuring AWS Services for Data Pipelines
- Serverless Workflows
- Building Data Pipelines
- Use Orchestration Services
- Using Notification Services
- Programming Concepts for Data Pipelines
- CI/CD for Data Pipelines
- SQL Queries for Data Transformations
- Infrastructure as Code (AWS CDK, AWS CloudFormation)
- Data Structures and Algorithms
- SQL Query Optimization
- Optimizing Code for Runtime Efficiency
- Configuring Lambda for Concurrency and Performance
- Using AWS SAM for Serverless Deployments
- Mounting Storage Volumes
Hands-on:
- Using Orchestration Services to Build Workflows for Data ETL Pipelines
- Implementing and Maintaining Serverless Workflows
- Setting Up Notifications for Pipeline Events
- Implementing a CI/CD Pipeline for Data Pipelines Using AWS CodePipeline and AWS CodeBuild
- Deploying a Serverless Data Pipeline with AWS SAM
Skills You Will Learn:
- Data Pipeline Orchestration
- Serverless Workflows
- CI/CD for Data Pipelines
Topics:
- Storage Platforms
- Storage Services
- Configurations for Performance Demands
- Data Storage Formats
- Common Data Storage Formats
- Choosing the Right Format for Specific Use Cases
- Aligning Data Storage with Data Migration Requirements
- Understanding Data Migration Requirements
- How to Select Storage Solutions That Meet Migration Needs
- Determining the Appropriate Storage Solution for Access Patterns
- Analyzing Access Patterns
- Matching Storage Solutions to These Patterns
Hands-on:
- Implementing the Appropriate Storage Services Cost and Performance Requirements
- Applying Storage Services to Appropriate Use Cases
- Integrating Migration Tools into Data Processing Systems
- Implementing Data Migration Using Amazon Redshift Spectrum and Federated Queries
Skills You Will Learn:
- Identifying Storage Platforms
- Data Storage Formats
- Utilizing Storage Services
Topics:
- Creating a Data Catalog
- Steps to Create a Data Catalog
- AWS Glue Data Catalog
- Apache Hive Metastore
- Data Classification
- Business and Technical Requirements
- Metadata
- Data Catalogs
- Metadata Components
- Role of Data Catalogs in Data Management
- Lifecycle Management of Data
- Storage Solutions for Hot and Cold Data
- Data Retention Policies and Legal Requirements
Hands-on:
- Using AWS Glue to Build and Reference a Data Catalog
- Discovering Schemas and Using AWS Glue Crawlers
- Synchronizing Data Partitions with AWS Glue
- Performing Load and Unload Operations
- Managing S3 Versioning and DynamoDB TTL
Skills You Will Learn:
- Creating a Data Catalog
- Metadata Management
- Data Classification
Topics:
- Data Modeling Concepts
- Structured, Semi-Structured, and Unstructured Data Modeling
- Schema Evolution Techniques
- Tools for Schema Conversion
- AWS Schema Conversion Tool
- AWS DMS Schema Conversion
- Data Lineage and Trustworthiness
- Ensuring Data Accuracy with Data Lineage
- Tools for Tracking Data Lineage
- Indexing, Partitioning, and Data Optimization Techniques
- Best Practices for Indexing and Partitioning
- Data Compression and Optimization Techniques
Hands-on:
- Creating Schemas for Amazon Redshift, DynamoDB, and Lake Formation
- Addressing Changes in Data Characteristics with Schema Evolution Techniques
- Implementing Indexing and Partitioning Strategies
- Establishing Data Lineage by Using AWS Tools
Skills You Will Learn:
- Building a Data Catalog
- Managing Metadata
- Data Lifecycle Management
Topics:
- Automating Data Processing with AWS Services
- Overview of AWS Data Processing Services
- Maintaining and Troubleshooting
- Using API Calls for Data Processing
- Calling SDKs to Access Amazon Features from Code
- Orchestrating Data Pipelines
- Using Amazon MWAA and Step Functions
- Troubleshooting Amazon-Managed Workflows
- Managing Events and Schedulers with EventBridge
- Preparing Data Transformation with AWS Glue DataBrew
- Using AWS Lambda to Automate Data Processing
- Querying Data with Amazon Athena
- Analyzing Data with AWS Services
- Provisioned and Serverless Services
- Data Visualization Techniques and Tools
- Data Cleansing Techniques
- Data Aggregation and Analysis
- Data Aggregation
- Visualizing Data
- Verifying and Cleaning Data
Hands-on:
- Create a Data Pipeline Using Amazon MWAA and Step Functions
- Calling SDKs to Access Amazon Features from Code
- Consuming and Maintaining Data APIs
- Transform Data Using AWS Glue DataBrew
- Visualize Data Using Amazon QuickSight
- Write and Execute SQL Queries on Amazon Athena
Skills You Will Learn:
- Automate Data Processing
- Analyze Data
- Query and Aggregate Data
Topics:
- Maintaining Data Pipelines
- Logging Application Data
- Best Practices for Performance Tuning
- Logging Access to AWS Services
- Monitoring and Auditing
- Extracting Logs for Audits
- Logging and Monitoring Solutions
- Monitoring to Send Alerts
- Troubleshooting Data Pipelines
- Troubleshooting Performance
- Using CloudTrail to Track API Calls
- Logging Application Data with Amazon CloudWatch Logs
- Analyzing Logs
- Data Sampling Techniques
- Implementing Data Skew Mechanisms
- Data Validation
- Data Profiling
- Data Quality Checks and Rules
- Running Data Quality Checks During Data Processing
- Defining Data Quality Rules
Hands-on:
- Set Up Logging and Monitoring with AWS CloudWatch Logs and CloudTrail
- Troubleshoot Data Pipelines Using AWS Glue and Amazon EMR
- Analyze Logs with Amazon CloudWatch Logs Insights and Athena
- Run Data Quality Checks with AWS Glue DataBrew
Skills You Will Learn:
- Implementing Data Logging
- Log Analysis
- Data Quality Management
Topics:
- Overview of VPC Security
- Security Groups and Network ACLs
- Managed Services vs. Unmanaged Services
- Authentication Methods
- Password-based
- Certificate-based
- Role-based Authentication
- AWS Managed Policies vs. Customer Managed Policies
- Authorization Methods
- Role-based
- Policy-based
- Tag-based
- Attribute-based
- Principle of Least Privilege
- Definition and Application in AWS Security
- Role-based Access Control (RBAC) and Access Patterns
- Implementing and Managing RBAC
- Protecting Data from Unauthorized Access
- Best Practices
Hands-on:
- Creating and Updating IAM Groups, Roles, Endpoints, and Services
- Creating and Rotating Credentials for Password Management
- Setting Up IAM Roles for Access
- Applying IAM Policies to Roles, Endpoints, and Services
- Managing Permissions through Lake Formation
Skills You Will Learn:
- Implementing Authentication Methods
- Managing AWS Policies
- Applying Authorization Methods
Topics:
- Data Encryption Options
- Encryption in Amazon Redshift, EMR, AWS Glue
- Client-Side vs. Server-Side Encryption
- Protecting Sensitive Data
- Methods and Best Practices
- Data Anonymization
- Masking
- Key Salting
- Logging and Audit Preparation
- Application Logging
- Logging Access to AWS Services
- Centralized AWS Logs
- Data Privacy and Governance
- Protecting PII
- Data Sovereignty
Hands-on:
- Encrypting and Decrypting Data with AWS KMS
- Setting Up and Managing Cross-Account Encryption
- CloudTrail to Track API Calls
- CloudWatch Logs to Store Application Logs
- Analyzing Logs by Using AWS Services
- Use AWS Macie and Lake Formation for PII Identification and Privacy
Skills You Will Learn:
- Data Encryption Techniques
- Protecting Sensitive Data
- Data Privacy and Governance
AWS Training Course Features
Instructor-led Live Sessions
We use only the finest instructors in the IT industry with good experience. Learn from our instructor and interact live at your desired place via virtual learning programs scheduled to run at specific times.
E-Learning Self-Paced Training
We offer self-paced training programs, which are structured in modules so as to offer maximum flexibility to those who wish to work around their already hectic schedules.
One to One Training
We offer is one to one training as a mode of educational training where you can Interact one to one with the instructor to get a fully focused training experience. It is preferred by students who prefer a personalized approach.
24 x 7 Expert Support
We have a lifetime 24x7 online support team to resolve all your technical queries, through a ticket based tracking system.
Certification
After successfully completing your course & projects, TopD Learning will provide a professional certification for you.
Lifetime Access
You will get lifetime access to our LMS where quizzes, presentations & class recordings are available.
Course Completion Certification
Give your resume a BOOST, and join Top Companies with a good package.
You will receive a course completion certificate post completing all assignments & tasks certifying that you have learned the skills and completed the course successfully.
Frequently Asked Questions
FAQs
The key takeaway from our AWS Data Engineer Certification Course is a comprehensive understanding of AWS data engineering concepts, along with the ability to design, implement, and manage data solutions on the AWS platform. This course equips you with valuable skills and certifications for your career growth.
There is no requirement for eligibility to enroll in our AWS Data Engineer Certification Course. Anyone interested in learning about the art of AWS data engineering can join our training course program and begin their journey. But, having a basic understanding of data structures and algorithms, SQL, Programming knowledge of Python and Java, Cloud platforms, distributed systems, and Data pipelines are helpful.
You will gain skills in designing, building, and maintaining data processing systems using AWS services, and expertise in automating data workflows, ensuring data quality, and analyzing data for insights. Additionally, you’ll learn the best practices for data security and compliance within AWS environments.
