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AWS Certified Data Engineer - Associate Training (DEA-C01)
AWS Certified Data Engineer - Associate Training (DEA-C01)
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Why enroll for AWS Data Engineer Certification Course?
Benefits of AWS Certification for Data Engineer
According to Market Data Forecast, the global market for big data and data engineering services is expected to grow by 17.6% between 2024 and 2029, from USD 75.55 billion to USD 169.9 billion. The need for AWS-certified professionals who can handle massive amounts of data is becoming more and more evident, which will improve the employment chances for skilled Data Engineers.
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Why AWS Data Engineer Certification Course from edureka
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Lifetime Access
- Course Access Never Expires
- Free Access to Future Updates
- Unlimited Access to Course Content
24x7 Support
- One-On-One Learning Assistance
- Help Desk Support
- Resolve Doubts in Real-time
Hands-On Project Based Learning
- Industry-Relevant Projects
- Course Demo Dataset & Files
- Quizzes & Assignments
Industry Recognised Certification
- Edureka Training Certificate
- Graded Performance Certificate
- Certificate of Completion
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About your AWS Data Engineer Certification Course
Skills Covered
Tools Covered
AWS Certified Data Engineer - Associate (DEA-C01) Curriculum
Curriculum Designed by Experts
AWS Data Engineering and Data Ingestion
21 Topics
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
![skill]( "Hands On Experience")
- 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
![skill]( "skill you will learn")
- Data Ingestion Patterns
- Batch Data Ingestion
- Configuring Data Ingestion
Data Transformation and Processing
16 Topics
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
![skill]( "Hands On Experience")
- 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
![skill]( "skill you will learn")
- ETL Pipeline Design
- Optimizing ETL Processes
- Data Processing
- Managing Data APIs
Orchestrating Data Pipelines and Programming Concepts
18 Topics
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
![skill]( "Hands On Experience")
- 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
![skill]( "skill you will learn")
- Data Pipeline Orchestration
- Serverless Workflows
- CI/CD for Data Pipelines
Data Storage Solutions
12 Topics
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
![skill]( "Hands On Experience")
- 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
![skill]( "skill you will learn")
- Identifying Storage Platforms
- Data Storage Formats
- Utilizing Storage Services
Data Cataloging and Lifecycle Management
13 Topics
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
![skill]( "Hands On Experience")
- 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
![skill]( "skill you will learn")
- Creating a Data Catalog
- Metadata Management
- Data Classification
Designing Data Models and Schema Conversion
12 Topics
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
![skill]( "Hands On Experience")
- 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
![skill]( "skill you will learn")
- Building a Data Catalog
- Managing Metadata
- Data Lifecycle Management
Automating Data Processing and Analyzing Data with AWS
20 Topics
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
![skill]( "Hands On Experience")
- 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
![skill]( "skill you will learn")
- Automate Data Processing
- Analyze Data
- Query and Aggregate Data
Maintaining and Monitoring Data Pipelines
20 Topics
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
![skill]( "Hands On Experience")
- 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
![skill]( "skill you will learn")
- Implementing Data Logging
- Log Analysis
- Data Quality Management
Data Authentication and Authorization
19 Topics
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
![skill]( "Hands On Experience")
- 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
![skill]( "skill you will learn")
- Implementing Authentication Methods
- Managing AWS Policies
- Applying Authorization Methods
Data Encryption, Logging, and Governance
15 Topics
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
![skill]( "Hands On Experience")
- 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
![skill]( "skill you will learn")
- Data Encryption Techniques
- Protecting Sensitive Data
- Data Privacy and Governance
Industrial Use Cases (Self-paced)
3 Topics
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
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AWS Certified Data Engineer training Course Details
What are the learning objectives of our AWS Course for Data Engineers?
The AWS Data Engineer Certification Course helps learners master AWS services, data ingestion, transformation, and pipeline management. It covers amazon s3 data storage solutions, data modeling, security, monitoring, and hands-on learning.
What are the prerequisites for this AWS Data Engineer Certification Course?
The preliminary knowledge of AWS services and data engineering would be beneficial.
What are the skills required for an AWS Data Engineer?
As a Certified AWS Data Engineer, several skills are essential for success. Key skills include proficiency in SQL, AWS services like Glue, data modeling, ETL processes, programming languages like Python, and experience with big data technologies.
AWS Data Engineer Training Projects
Scalable Data Ingestion and Management for Real-time Analytics
Design a scalable and robust data pipeline for ingesting, transforming, storing, and analyzing real-time financial transaction data from various sources. Set up extensive data op....Secure and Optimized Data Pipeline for Batch Processing
Design and implement a secure and optimized data pipeline for batch processing of large healthcare datasets. Ensure efficient data storage, regular maintenance, and robust govern....AWS Data Engineer Certification
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 AWS Certified Data Engineer - Associate (DEA-C01) Exam certification costs 150 USD
This certification is valid for 3 years. Before your certification expires, you can recertify by passing the latest version of this exam.
To unlock Edureka’s AWS Data Engineer Training certificate, you must fully participate in the course and complete all required quizzes and projects as part of the evaluation process.
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FAQs
What are the key takeaways of the Edureka’s AWS Data Engineer Online Course?
The key takeaway from Edureka’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.
What is Edureka's refund policy regarding the course if I am not satisfied with the course content?
Typically, Edureka allows learners to request a refund within a specified period if they are not satisfied with the course content. Your satisfaction with the course is our priority.
What financial options are available for learners interested in the AWS Data Engineer Training?
Edureka offers various financial options for learners, including installment payment plans, no-cost EMI options, or discounts on course fees. These flexible financial options make it convenient for learners to invest in their AWS data engineering training.
What skills will I gain from the AWS Data Engineer Course?
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.
What career options can I look forward to with my AWS Data Engineer skills?
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
How will this Data Engineering AWS Course help me find a better job and boost my career?
After completing the AWS Certified Data Engineer Associate course validates your ability to design, build, and manage data pipelines on AWS, enhancing your employability in a competitive job market.
Is AWS certification worth it for a data engineer?
Yes, pursuing AWS certifications, especially the AWS Certified Data Engineer – Associate, validates your ability to design, implement, and maintain data solutions on AWS, boosting your career prospects.
Does AWS data engineer require coding?
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.
What is the salary of data center engineer in AWS in India?
According to AmbitionBox, AWS Data Engineer salaries in India range between ₹4 Lakhs to ₹21 Lakhs for professionals with 1 to 7 years of experience.
What is the job roles of data engineer associate in AWS?
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.
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