AWS Data Engineer Associate Certification Training

  • Home
  • AWS Data Engineer Associate Certification Training
$539

$399

-26% Off
 
+Wishlist
Categories
Cloud Computing & DevOps

AWS Data Engineer Associate Certification Training

CertOcean's AWS Certified Data Engineer Associate training is designed to prepare you for the new  DEA-C01 certification exam.This course covers like Setting up schedulers, Optimizing data processing, and Managing data pipelines.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+ 5+ industry use cases and 20+ hands-on demos with capstone projects
  • 9+ Assignments & Knowledge Checks

The AWS Certified Data Engineer - Associate (DEA-C01) certification validates your expertise in core AWS data services, data ingestion, transformation, pipeline orchestration, programming concepts, data modeling, lifecycle management, and data quality assurance. The key takeaway from CertOcean's AWS Data Engineer 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.

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

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

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

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

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

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

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

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

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

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

Topics:

  • Use Cases for Implementing AWS Data Solutions
  • Emerging Trends and Technologies in AWS Data Engineering
  • Best Practices for Keeping Up with Industry Trends in AWS Data Engineering

Course Description

Become a Certified AWS Data Engineer with CertOcean’s Expert-Led Program

CertOcean's AWS Certified Data Engineer – Associate course is designed to equip professionals with the skills and knowledge to design, build, maintain, and secure data pipelines on the AWS platform. Whether you're a data analyst, cloud practitioner, or aspiring data engineer, this course helps you gain in-demand skills to process big data, use AWS analytics services like Glue, Redshift, and Kinesis, and prepare confidently for the certification exam.

Our course is aligned with the official AWS exam blueprint and includes real-world case studies, hands-on labs, and expert mentorship to ensure you’re job-ready and exam-ready.

Features

Instructor-led live sessions

30 Hours of Online Live Instructor-led Classes.

Practical Hands-on

Each class will be followed by practical training sessions for a better hands-on experience.

Lifetime Access

Students will get lifetime access to all the course materials where presentations, quizzes, installation guides, and class recordings are available.

24/7 expert support

We provide 24/7 support to all the students, thereby resolving technical queries. 

Certification

Once you have completed your final course training and project, you will receive a certificate stating you are a AWS Data Engineer Associate training certified.

Frequently Asked Questions (FAQs):

You will gain skills in designing, building, and maintaining data processing systems on the AWS platform, including data pipelines, data warehousing, data modeling, and AWS services.

With your AWS Data Engineering skills, you can pursue roles such as Data Engineer, AWS Certified Developer, Senior Data Engineer, Data Engineering Manager, Cloud DevOps Engineer, and positions in data warehousing and big data processing

Yes, AWS data engineering requires coding skills, particularly in languages like Python and Java, to effectively design, implement, and maintain data solutions on the AWS platform.

An AWS Data Engineer Associate designs, implements, and manages data solutions on AWS. Responsibilities include data ingestion, transformation, storage, modeling, pipeline orchestration, ensuring data quality, governance, security, and performance optimization.

The following will help you to prepare for the Exam:

  • Enroll in CertOcean's AWS Data Engineer Associate course.
  • Attend 30 hours of live instructor-led training sessions.
  • Engage with 5+ industry use cases and projects.
  • Complete 20+ hands-on demos and capstone projects.
  • Work through 9+ assignments and knowledge checks.
  • Master data ingestion, transformation, and pipeline management.
  • Learn to optimize data processing and scheduling tasks.
  • Understand data security and governance best practices.
  • Access lifetime course materials and future updates.
  • Utilize 24x7 support for real-time doubt resolution.

AWS provides key services for data engineering, including Amazon S3 for storage, AWS Glue for ETL, Amazon Redshift for data warehousing, Kinesis for real-time streaming, EMR for big data processing, RDS/Aurora for relational databases, DynamoDB for NoSQL, Lake Formation for data lakes, and Athena for querying S3 data.

4.3

Course Rating

Contact Us