AWS Data Engineer Training | AWS Data Engineer Online Course

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AWS Data Engineer

The AWS Certified Data Engineer - Associate certification serves as a crucial validation of proficiency in key…

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The AWS Certified Data Engineer - Associate certification serves as a crucial validation of proficiency in key AWS data services and practices. It attests to an individual's competency in managing data across its lifecycle within AWS environments. This certification highlights expertise in foundational AWS services essential for data engineering, including efficient data ingestion and transformation capabilities. Candidates demonstrate adeptness in orchestrating data pipelines to streamline data flow and processing, leveraging advanced programming concepts to enhance automation and efficiency.

Moreover, the certification signifies competence in designing robust data models that cater to diverse analytical and operational requirements. Certified professionals are adept at managing data lifecycles effectively, ensuring data remains accessible, secure, and compliant throughout its journey. Emphasis is placed on maintaining high standards of data quality, employing best practices and AWS tools to validate and enhance data accuracy and consistency. Overall, the AWS Certified Data Engineer - Associate credential underscores comprehensive skills in harnessing AWS's data capabilities to drive effective data management strategies and deliver scalable solutions tailored to organizational needs.

What Will You Learn?

Data Engineering Concepts and AWS Services: Understanding foundational data engineering principles and leveraging AWS services for scalable and efficient data management.
Fetching Data from External REST API: Implementing methods to retrieve data programmatically from external RESTful APIs for integration into data pipelines.
Fetching Data from SFTP Server: Configuring processes to securely fetch and integrate data from SFTP servers into AWS environments.
Ingesting Data into a Database: Utilizing AWS services to ingest and store data into databases like Amazon RDS or Amazon DynamoDB, ensuring data integrity and accessibility.
Creating Serverless Data Lake using S3 and Athena: Architecting a scalable and cost-effective data lake solution on AWS using Amazon S3 for storage and Amazon Athena for querying and analysis.
Creating Glue ETL Jobs and Workflows: Building Extract, Transform, Load (ETL) processes with AWS Glue to automate data transformation tasks and orchestrate workflows for seamless data integration.

Course Curriculum

Module 1: Introduction to Data Engineering and AWS

1.1 Fundamentals of Data Engineering
:: Overview of Data Engineering Roles and Responsibilities
:: Understanding Data Pipelines: Batch vs. Real-time Processing
:: Key Data Engineering Concepts: ETL, Data Lakes, Data Warehouses, and Data Analytics
1.2 Introduction to AWS for Data Engineers
:: Overview of AWS Services and the AWS Ecosystem
:: The Role of AWS in Modern Data Engineering
:: AWS Global Infrastructure and Core Concepts (Regions, Availability Zones, VPC)
1.3 Setting Up Your AWS Environment
:: Creating and Configuring an AWS Account
:: Introduction to AWS Management Console and CLI
:: Understanding IAM: Users, Roles, and Permissions
:: Billing and Cost Management in AWS

Module 2: AWS Storage Solutions

2.1 Amazon S3: The Foundation of AWS Storage
:: Introduction to Amazon S3 (Simple Storage Service)
:: S3 Buckets, Objects, and Storage Classes
:: S3 Versioning, Lifecycle Policies, and Data Archiving (Glacier)
:: S3 Security: Bucket Policies, ACLs, and Encryption
2.2 Amazon EBS and EFS
:: Understanding Amazon EBS (Elastic Block Store)
:: Amazon EFS (Elastic File System) for Scalable File Storage
:: Choosing Between S3, EBS, and EFS for Different Use Cases
:: Best Practices for Storage Optimization and Cost Management
2.3 AWS Data Transfer and Integration
:: Transferring Data into AWS: Snowball, DataSync, and Transfer Family
:: AWS Direct Connect and VPN for Secure Data Transfer
:: Using AWS Storage Gateway for Hybrid Cloud Storage
2.4 Real-time Scenario: Setting Up a Secure Data Lake
:: Configuring an Amazon S3 Data Lake
:: Securing and Organizing Data with S3 Bucket Policies and IAM Roles
:: Implementing Data Lifecycle Management and Archiving

Module 3: Databases and Data Warehousing in AWS

3.1 Introduction to AWS Databases
:: Overview of AWS Database Services: RDS, DynamoDB, Redshift, etc.
:: Choosing the Right Database: Relational vs. NoSQL vs. Data Warehouse
:: Basics of AWS RDS (Relational Database Service)
:: Amazon DynamoDB for NoSQL Workloads
3.2 Amazon Redshift: Data Warehousing
:: Introduction to Amazon Redshift and Its Architecture
:: Setting Up and Configuring a Redshift Cluster
:: Loading Data into Redshift: COPY Command, Redshift Spectrum
:: Redshift Query Performance Optimization: Distribution Styles, Sort Keys
3.3 Amazon Aurora and RDS
:: Working with Amazon Aurora: MySQL and PostgreSQL Compatibility
:: Automated Backups, Snapshots, and Multi-AZ Deployments
:: RDS Performance Insights and Tuning
:: Migrating Databases to AWS RDS (Database Migration Service – DMS)
3.4 Real-time Scenario: Designing a Data Warehouse in Redshift
:: Architecting a Data Warehouse Solution with Amazon Redshift
:: Integrating Redshift with S3 for Cost-effective Analytics
:: Implementing ETL Pipelines for Redshift using AWS Glue

Module 4: Data Ingestion and ETL Pipelines

4.1 AWS Glue: Serverless ETL
:: Introduction to AWS Glue and Its Components
:: Crawlers, Data Catalog, and ETL Jobs
:: Writing and Managing ETL Scripts with Glue
:: Glue Studio for Visual ETL Workflow Development
4.2 Amazon Kinesis: Real-time Data Streaming
:: Introduction to Amazon Kinesis: Streams, Firehose, and Analytics
:: Ingesting Real-time Data with Kinesis Data Streams
:: Real-time ETL with Kinesis Firehose
:: Streaming Analytics with Kinesis Data Analytics
4.3 AWS Lambda and Serverless Data Processing
:: Introduction to AWS Lambda and Event-driven Architectures
:: Triggering Lambda Functions from S3, DynamoDB, and Kinesis
:: Building Serverless ETL Pipelines with Lambda
:: Best Practices for Lambda Performance and Cost Optimization
4.4 Real-time Scenario: Building a Real-time Data Pipeline
:: Streaming Data Ingestion with Kinesis and Lambda
:: Real-time Data Processing and Transformation
:: Loading Processed Data into Redshift or S3 for Analytics

Module 5: Data Analytics and Machine Learning

5.1 Amazon Athena: Serverless Query Service
:: Introduction to Amazon Athena for Data Analytics
:: Querying Data Directly from S3 Using SQL
:: Integrating Athena with AWS Glue Data Catalog
:: Optimizing Athena Queries for Performance and Cost
5.2 Amazon EMR: Big Data Processing
:: Introduction to Amazon QuickSight for Data Visualization
:: Connecting QuickSight to Redshift, Athena, and RDS
:: Creating Interactive Dashboards and Reports
:: Embedding Analytics with QuickSight in Applications
5.4 Machine Learning with AWS SageMaker
:: Introduction to Amazon SageMaker for ML Model Development
:: Data Preparation and Feature Engineering with SageMaker
:: Training, Tuning, and Deploying Models in SageMaker
:: Real-time Inference and Batch Predictions with SageMaker Endpoints
5.5 Real-time Scenario: End-to-End Data Analytics Pipeline
:: Building a Data Analytics Pipeline from S3 to Athena and QuickSight
:: Implementing a Machine Learning Workflow with SageMaker
:: Automating Data Processing and Model Training with Step Functions

Module 6: Data Security and Governance

6.1 Data Security in AWS
:: AWS Shared Responsibility Model
:: Implementing IAM Best Practices for Data Access Control
:: Encrypting Data at Rest and in Transit: S3, RDS, KMS
:: Logging and Monitoring with AWS CloudTrail and CloudWatch
6.2 Compliance and Data Governance
:: AWS Artifact for Compliance Reports and Certifications
:: Managing Data Lineage and Metadata with AWS Glue
:: Implementing Data Governance Best Practices in AWS
6.3 Real-time Scenario: Securing a Data Pipeline
:: Implementing End-to-End Encryption in a Data Pipeline
:: Configuring IAM Policies for Secure Data Access
:: Auditing Data Access and Activity with CloudTrail

Module 7: Advanced Data Engineering with AWS

7.1 Building Data Lakes on AWS
:: Architecting a Data Lake with Amazon S3 and AWS Lake Formation
:: Securing and Managing Data Lakes with IAM, Lake Formation, and Glue
:: Integrating Data Lakes with Redshift, Athena, and EMR for Analytics
7.2 Data Migration Strategies
:: Migrating On-premises Data to AWS: Snowball, DMS, SCT
:: Hybrid Cloud Architectures: Integrating On-premises and AWS Data
:: Data Replication and Synchronization with AWS DMS
7.3 Advanced Data Pipeline Architectures
:: Designing Fault-tolerant and Scalable Data Pipelines
:: Event-driven Architectures with AWS Lambda, SNS, and SQS
:: Implementing Complex Workflows with AWS Step Functions
:: Managing Workflow State and Errors
7.4 Real-time Scenario: Building a Scalable Data Lake
:: Architecting and Implementing a Data Lake for Large-scale Data
:: Integrating Real-time and Batch Processing Pipelines
:: Optimizing Data Storage and Retrieval for Analytics

Module 8: Monitoring, Optimization, and Cost Management

8.1 Monitoring and Logging in AWS
:: Monitoring AWS Resources with CloudWatch
:: Setting Up Alarms and Notifications for Data Pipelines
:: Centralized Logging with AWS CloudWatch Logs and S3
8.2 Performance Optimization
:: Optimizing Data Storage and Retrieval (S3, Redshift, RDS)
:: Improving Query Performance with Redshift Spectrum, Athena, and EMR
:: Scaling Data Pipelines Efficiently with Auto Scaling and Load Balancing
8.3 Cost Management and Optimization
:: AWS Cost Explorer and Budgets for Cost Monitoring
:: Identifying and Reducing AWS Costs with Trusted Advisor
:: Cost Optimization Strategies for Data Engineering Workloads
:: Leveraging Spot Instances and Reserved Instances for Cost Savings
8.4 Real-time Scenario: Monitoring and Optimizing a Data Pipeline
:: Implementing Comprehensive Monitoring for a Data Pipeline
:: Tuning Pipeline Performance for High-volume Data Processing
:: Managing Costs and Optimizing Resource Utilization

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