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AWS Data Engineer

AWS Data Engineer Training: In today’s modern technology, businesses rely on cloud computing to handle large amounts…

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AWS Data Engineer Training:

In today’s modern technology, businesses rely on cloud computing to handle large amounts of data efficiently. AWS Data Engineers design and build data pipelines that move and process data across AWS services. They create ETL (Extract, Transform, Load) workflows to collect data from databases, APIs, and streaming sources. Using tools like AWS Glue, EMR, Redshift, and Kinesis, they clean, transform, and store data for reporting and analysis. Their work helps companies make smart decisions based on accurate and well-processed data.

AWS Data Engineers also focus on keeping data secure and following rules for compliance using AWS IAM, KMS, and CloudTrail. They work closely with data scientists and analysts to organize data for business insights and machine learning. With knowledge of Python, SQL, Spark, and NoSQL databases, they create real-time and batch data solutions. These solutions help businesses find patterns, improve operations, and make better plans. As companies continue using cloud technology, the demand for AWS Data Engineers keeps growing.

Our AWS Data Engineer Course teaches you how to build and manage data pipelines using Amazon Web Services. You will learn to use important tools like S3, Glue, Redshift, and Kinesis to collect, process, and analyze data. This AWS Data Engineer Training is perfect for beginners and IT professionals who want to improve their cloud skills. With hands-on practice, real projects, and expert guidance, this AWS Data Engineer Online Course provides the experience needed to work with data in the cloud and advance your career in cloud data engineering.

What Will You Learn?

Module 1: Introduction to AWS and Data Engineering Fundamentals. Overview of the AWS ecosystem and cloud computing. Key data engineering concepts and the role of a data engineer
Module 2: AWS Core Services and Architecture. Deep dive into AWS services like EC2, S3, IAM, and VPC. Designing secure and scalable cloud architectures
Module 3: Data Storage Solutions on AWS. Building data lakes with Amazon S3. Exploring databases such as Amazon RDS, DynamoDB, and Redshift for data warehousing
Module 4: Data Ingestion and Streaming. Implementing real-time data ingestion using Amazon Kinesis. Batch data ingestion techniques and using AWS Data Pipeline
Module 5: ETL and Data Processing. Creating ETL pipelines with AWS Glue. Leveraging AWS Lambda for serverless data processing
Module 6: Data Analytics and Visualization: Optimizing Amazon Redshift for analytical workloads. Visualizing data with AWS QuickSight for business insights
Module 7: Big Data Processing and Machine Learning Integration: Utilizing AWS EMR for processing large-scale data sets. Integrating data pipelines with AWS machine learning services
Module 8: Security, Monitoring, and Best Practices: Ensuring data security with AWS IAM and encryption techniques. Monitoring workflows with AWS CloudWatch and troubleshooting best practices
Module 9: Hands-on Projects and Real-World Applications: Applying your knowledge through practical projects. Exploring real-world case studies to build end-to-end data solutions on AWS

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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