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AWS Data Engineer Certification Course
AWS Data Engineer Certification Course
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Why enroll for AWS Data Engineer Certification Course?
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
Tools Covered
AWS Data Engineer Certification Course 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 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.
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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.
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