Why enroll for AWS Data Engineer Certification Course?
AWS certification is rated as one of the most popular and lucrative cloud certifications in IT globally - Global Knowledge Study.
As per Market Data Forecast, the global big data and data engineering services market size is anticipated to grow at a CAGR of 17.6% from 2024 to 2029.
According to Glassdoor, the average salary for a Cloud Data Engineer is USD 131,216 per year in the United States.
AWS Data Engineer Certification Course Benefits
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
Design data storage solutions
Ingest and transform Data
Design data models
Design and implement data security
Monitor data storage and data processing
Create and manage data pipelines
Tools Covered
AWS Data Engineer Certification Course Curriculum
Curriculum Designed by Experts
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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Free Career Counselling
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AWS Data Engineer Certification Course Details
About AWS Data Engineer Certification Course
The AWS Data Engineer Certification Course is the best solution for learners aiming to develop skills in the core AWS services, ingestion and transformation of data, and Data pipeline management. With expert instructors, learners can easily implement hands-on learning in data pipelines and ensure data quality. The key learning features of this course include:
Ingest, transform, and manage data pipelines while integrating programming principles.
Select the best data storage solution, create data models, organize data schemas, and manage data lifecycles.
Deploy, maintain, and monitor data pipelines.
Perform data analysis and ensure data integrity.
Implement proper authentication, authorization, data encryption, privacy, and governance measures.
What are the prerequisites for this AWS Data Engineer Certification Course?
The preliminary knowledge of AWS services and data engineering would be beneficial.
Why should you become a AWS Certified Data Engineer?
Learning AWS Data Engineer Certification Course can offer various advantages such as:
Career Growth: The growing demand for data engineering worldwide brings many opportunities for certified AWS Data Engineers, creating numerous career prospects for individuals in this industry.
Future Demand: The need for skilled data engineers will grow in the future. This makes it ideal to gain certification in AWS Data Engineering.
Skills: This certification course on AWS Data Engineer covers skills such as ingestion and transformation of data, data pipelines,and security measures.
Salary Growth: Individuals with AWS Data Engineer Certification can anticipate more salary growth than non certified individuals, which gives them an extra benefit in this dynamic market of data engineering.
Hands-on Experience: This certification course offers several hands-on materials aligned with the latest industry tools and technologies.
Industry Project Exposure: Learners are exposed to industry-relevant projects aligned with data engineering and AWS tools and services.
What are the objectives of our AWS Data Engineer Certification Course?
Our AWS Data Engineer Certification Course is designed as per industry standards and helps learners in designing, implementing, and maintaining secure and compliant data processing pipelines using AWS Services. The AWS Certified Data Engineer - Associate (DEA-C01) exam validates a candidate’s ability to implement data pipelines and to monitor, troubleshoot, and optimize cost and performance issues in accordance with best practices.
What are the skills required for an AWS Data Engineer?
As a Certified AWS Data Engineer, several skills are essential for success. Here are some of the most important skills you will need:
Data Modelling and Database Design
SQL and other query languages
Data Integration and ETL Tools
Data Analysis and Visualization
Cloud Platform Skills
Programming languages such as Python and Java
Data Pipeline Development
Who should go for this AWS Data Engineer Certification Course?
Anyone with a zeal to learn and who wants to become a Data Engineer or Data Architect can join this training.
What are the system requirements for this AWS Data Engineer Certification Course?
You need a basic configuration with a minimum of 4GB RAM and i3 processor, with a good internet connection.
How will I execute practicals in this AWS Data Engineer Certification Course?
You will be using an AWS Free Tier Account to execute the practicals. The detailed, step-by-step installation guides are available on the LMS. Edureka's 24*7 technical support team is always available to promptly assist you with your queries.
Is Data Engineering a good career option?
Data Engineering is a great career option. It is a rapidly expanding field with a bright future. As the amount of data produced by companies and individuals increases, so will the demand for data engineers in the future.
How can beginners learn AWS Data Engineering?
Beginners can become familiar with AWS data engineering services easily as it is a user-friendly cloud-based platform. Learning its capabilities and functionality requires appropriate direction and a well-structured training path. Beginners interested in a career in data engineering using AWS can sign up for our online training and earn certificates to demonstrate their expertise in this domain.
AWS Data Engineer Certification Course 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 Course Certification
The AWS Data Engineer are the working professionals who has the knowledge in the both domain of data engineering and AWS cloud services. They majority of role is to develop and monitor the data pipelines in the AWS Cloud Platform.
AWS Certified Data Engineer - Associate Certification certifies an individual's expertise in designing and implementing the ingestion, transformation, orchestration of data pipelines. This certification validates a candidate's skills and knowledge in designing and implementing data solutions using AWS Data Services.
Professionals who are Data Engineers, Database Administrators, and Developers working with AWS services should take the AWS Certified Data Engineer - Associate exam to demonstrate their knowledge and skills in designing and implementing data solutions using AWS Data Services.
The AWS Certified Data Engineer - Associate Exam is a certification offered by AWS that measures a candidate's proficiency in various areas of data engineering on AWS. This includes designing and implementing AWS data storage solutions, data integration and transformation solutions, data processing solutions, as well as data security and compliance solutions
The AWS Certified Data Engineer - Associate (DEA-C01) Exam certification costs 150 USD
The duration of AWS Certified Data Engineer - Associate exam is 130 minutes
The passing score for the AWS Certified Data Engineer - Associate certification exam is 720 out of 1000, determined by the knowledge and skills needed to demonstrate competence and the difficulty of the questions.
The language offered are English, Japanese, Korean, and Simplified Chinese
The exam has the following content topics and weightings:
Data Ingestion and Transformation (34% of scored content)
Data Store Management (26% of scored content)
Data Operations and Support (22% of scored content)
Data Security and Governance (18% of scored content)
If you fail an exam, you must wait 14 calendar days before you are eligible to retake the exam. There is no limit on exam attempts. However, you must pay the full registration fee for each exam attempt. Once you have passed an exam, you will not be able to retake the same exam for two years. If the exam has been updated with a new exam guide and exam series code, you will be eligible to take the new exam version.
This certification is valid for 3 years. Before your certification expires, you can recertify by passing the latest version of this exam.
Exam results, including results for beta exams, will be available within five business days after you complete your exam. You will receive an email message when your exam results are available in your AWS Certification Account, under Exam History.
No, Amazon does not provide any free AWS certification.
This AWS Data Engineer Certificate will be awarded by AWS to professionals who can demonstrate their expertise and knowledge by successfully passing the AWS Certified Data Engineer - Associate (DEA-C01) Exam.
The Certification exam voucher is not included in this course
To unlock Edureka’s AWS Data Engineer Training completion certificate, you must ensure the following:
Completely participate in this AWS Data Engineer Certification Course.
Evaluation and completion of the quizzes and projects listed.
John Doe
Title
with Grade X
XYZ123431st Jul 2024
The Certificate ID can be verified at www.edureka.co/verify to check the authenticity of this certificate
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AWS Data Engineer Certification Course FAQs
What are the key takeaways of the Edureka’s AWS Data Engineer Certification Course Online?
The key takeaway from Edureka’s 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.
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 Certification Course?
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 are the criteria for admission to be a candidate for this AWS Data Engineer Certification Course?
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.
What skills will I gain from the AWS Data Engineer Certification Course?
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 best practices for data security and compliance within AWS environments.
What career options can I look forward to with my AWS Data Engineer skills?
With your AWS Data Engineer skills, you can pursue careers as a Cloud Data Engineer, focusing on building and managing data pipelines and architectures, or as a Data Analytics Engineer, leveraging AWS services to analyze and optimize data for business insights.
How will this AWS Data Engineer Certification Course help me find a better job and boost my career?
This AWS Data Engineer Certification Course equips you with the skills to design, build, and manage data pipelines on AWS, enhancing your employability in a competitive job market. It validates your expertise in cloud data engineering, making you a sought-after candidate for advanced roles.
What is the minimum education requirement to become an AWS Data Engineer?
Generally, no specific college degree is required to become an AWS Data Engineer. The minimum education requirement for a common starting point is a bachelor's degree in computer science, information technology, data engineering, or a related field.
Which is the best language to use for AWS Data Engineering?
AWS Data Engineering supports multiple languages, such as Python, SQL, and Java. The choice of the best language may depend on the specific requirements of your project, but these languages are widely popular for ETL processes, data analytics, and machine learning tasks in AWS.
Is it possible for a fresh graduate to find job opportunities after completing the AWS Data Engineer course?
Completing an AWS Data Engineering course can definitely increase your chances of finding job opportunities as a fresh graduate.
Why should I take AWS Data Engineer Certification Course?
There are several reasons why taking the AWS Data Engineer Certification Course can be beneficial for your career:
High demand for skilled AWS data engineers
Industry-recognized certification
Comprehensive coverage of AWS data services
Career advancement opportunities
Personal and professional growth
Is AWS Data Engineering a good career?
Yes, AWS Data Engineering is a promising career due to the growing demand for data-driven decision-making and the widespread adoption of cloud computing. It offers competitive salaries, numerous job opportunities, and the chance to work with cutting-edge technologies.
What is the future of AWS Data Engineering?
The future of AWS Data Engineering is marked by increasing automation, AI-driven analytics, and enhanced data integration capabilities, enabling more efficient and scalable data management solutions. Additionally, advancements in machine learning and real-time data processing will drive innovation in data-driven decision-making and business intelligence.
Does AWS data engineer require coding?
Yes, AWS data engineers typically need coding skills in languages like Python, SQL, or Scala for tasks such as data processing, pipeline automation, and ETL operations within AWS services.