Is this course suitable for beginners?

Yes,beginners with foundational SQL, Python, and AWS knowledge can start and succeed.

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 projects are included?

It include scalable pipelines, ETL batch processing, and live capstone projects built on AWS .

Do I get lifetime access and support?

Yes,lifetime access to class recordings, resources, and 24×7 learner support .

How long does it take to learn AWS Data Engineering?

It takes Around 5–6 weeks (30 hours plus self-study/projects) for learners with some AWS and Python experience

Is AWS Data Engineer in demand?

Yes,cloud data engineering roles are in high demand globally, with competitive salaries.

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.

What is AWS data engineer ?

An AWS Data Engineer specializes in designing, building, and managing data pipelines, storage systems, and integration solutions on the Amazon Web Services (AWS) platform.

AWS Data Engineer Certification Training Course Online

Name: AWS Data Engineer Certification Training Course Online
Item: AWS Data Engineer Certification Training Course Online
Rating: 5
Author: Sudha Kargi

Why should you take AWS Data Engineer Certification Course?

AWS data engineer certification is rated as one of the most popular 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 Engineering and Data Ingestion

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

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Data Transformation and Processing

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
Managing Data APIs

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Orchestrating Data Pipelines and Programming Concepts

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

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Data Storage Solutions

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

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Data Cataloging and Lifecycle Management

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

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Designing Data Models and Schema Conversion

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

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Automating Data Processing and Analyzing Data with AWS

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

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Maintaining and Monitoring Data Pipelines

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

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Data Authentication and Authorization

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

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Data Encryption, Logging, and Governance

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

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Industrial Use Cases (Self-paced)

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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View course curriculum

Testimonial Reviews

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AWS Data Engineer Certification Training Course Online

Why should you take AWS Data Engineer Certification Course?

Curriculum

AWS Data Engineering and Data Ingestion

Data Transformation and Processing

Orchestrating Data Pipelines and Programming Concepts

Data Storage Solutions

Data Cataloging and Lifecycle Management

Designing Data Models and Schema Conversion

Automating Data Processing and Analyzing Data with AWS

Maintaining and Monitoring Data Pipelines

Data Authentication and Authorization

Data Encryption, Logging, and Governance

Industrial Use Cases (Self-paced)

AWS Certified Data Engineer training Course Details

What is this AWS Data Engineering course?

What will I learn in this online training course?

Do I need any prerequisites to take this course?

What is included in the AWS Data Engineering course?

What AI tools are used in the course?

What projects will I work on?

What does Edureka include in this course?

Are hands-on labs included?

What is the delivery format?

Is the DEA-C01 exam included?

Who Should Take This Course?

Testimonial Reviews

Video Reviews

FAQs

Is this course suitable for beginners?

What is Edureka's refund policy regarding the course if I am not satisfied with the course content?

What financial options are available for learners interested in the AWS Data Engineer Training?

What projects are included?

Do I get lifetime access and support?

How long does it take to learn AWS Data Engineering?

Is AWS Data Engineer in demand?

Does AWS data engineer require coding?

What is the salary of data center engineer in AWS in India?

What is the job roles of data engineer associate in AWS?

What is AWS data engineer ?

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