Have a question?
Send enquiry
Message sent Close

AWS DATA SPECALITY

The AWS Certified Data Analytics – Specialty certification is designed for individuals with expertise in data analytics and experience working with AWS. It focuses on the ability to design, build, and maintain data analytics solutions on AWS.

The certification is aimed at professionals with a deep understanding of data analytics concepts and AWS tools, validating their ability to design and manage complex analytics solutions.

Course Instructor Sateesh Pabbathi

Original price was: ₹30,000.00.Current price is: ₹23,999.00.

Enroll Now

Course Overview

The AWS Certified Data Analytics – Specialty certification validates advanced expertise in designing, implementing, and maintaining data analytics solutions on AWS. It covers a range of topics, including data collection, storage, processing, analysis, and visualization. Key areas include leveraging AWS services like Amazon Redshift, AWS Glue, Amazon Athena, and Amazon Kinesis to create scalable and efficient data analytics architectures. The certification is aimed at professionals with significant experience in data analytics and AWS, showcasing their ability to handle complex data-driven tasks and solve business problems using AWS technologies.

Download Brochure

Course Curriculum

MODULE 1: INTRODUCTION TO AWS DATA ENGINEERING

The Role of Data Engineering in Modern
Organizations

Overview of Data Engineering Workflow

Understanding Big Data Technologies

Introduction to AWS Data Services

Data Lakes vs. Data Warehouses

Best Practices in Data Engineering

Industry Trends in Data Engineering

MODULE 2: DATA INGESTION

Introduction to Data Ingestion

Real-time Data Streaming with Amazon Kinesis

Batch Data Ingestion Strategies

Data Replication Techniques

Data Extraction from Various Sources (RDBMS,
NoSQL, APIs)

Change Data Capture (CDC) Methods

Data Validation and Quality Checks during Ingestion

MODULE 3: DATA STORAGE

Introduction to Data Storage in AWS

Amazon S3 and its Storage Classes

Data Partitioning and Bucket Design in S3

Amazon Redshift as a Data Warehouse

Optimizing Data Storage Costs

Data Archival and Lifecycle Policies

Introduction to NoSQL with Amazon DynamoDB

MODULE 4: DATA TRANSFORMATION

Introduction to Data Transformation

ETL Process Overview

AWS Glue for Data Cataloging and ETL

Data Transformation Best Practices

Lambda Functions for Data Transformation

Real-time Data Transformation with Kinesis

Data Validation and Cleansing Techniques

MODULE 5: DATA PROCESSING

Introduction to Data Processing


Working with AWS EMR (Elastic MapReduce)


Data Processing with Apache Spark on AWS


Optimization Techniques for Data Processing


Data Partitioning and Shuffling in EMR

Resource Management in EMR (YARN)

Real-time Data Processing with Kinesis Data Analytics

MODULE 6: DATA ORCHESTRATION

Introduction to Data Orchestration


Workflow Design with AWS Step Functions


Data Pipeline Orchestration with AWS Data Pipeline

Error Handling and Retry Strategies


Scheduling and Triggering Data Workflows


Managing Dependencies between Workflow Steps


Coordinating Data Tasks with Step Functions

 

MODULE 7: DATA WAREHOUSING AND BUSINESS INTELLIGENCE

Introduction to Data Warehousing


Amazon Redshift Architecture and Concepts


Data Modeling Best Practices in Redshift


Designing Efficient Redshift Queries


Data Visualization with Amazon QuickSight


Building Interactive Dashboards


Extract, Transform, Load (ETL) for Business
Intelligence

MODULE 8: DATA GOVERNANCE AND SECURITY

Introduction to Data Governance


AWS IAM (Identity and Access Management) for Data
Engineers


Encryption at Rest and in Transit


Compliance and Regulatory Standards


Fine-Grained Access Control


Data Masking and Anonymization


Audit Trails and Logs

MODULE 9: DATA MONITORING AND LOGGING

Introduction to Data Monitoring


AWS CloudWatch Metrics and Alarms


Custom Metrics and Dashboards


Log Analysis and Insights


AWS CloudTrail for Auditing and Compliance


Integration with SIEM (Security Information and Event
Management) Systems


Anomaly Detection and Automated Remediation

MODULE 10: DATA QUALITY AND DATA LINEAGE

Introduction to Data Quality


Data Validation Techniques


Data Cleansing and Enrichment


Error Handling and Logging for Data Quality


Data Lineage and Impact Analysis


Metadata Management with AWS Glue Data Catalog


Data Quality Monitoring and Reporting

MODULE 11: DATA ENGINEERING BEST PRACTICES

Introduction to Data Engineering Best Practices


Design Patterns for Scalability


Performance Optimization Techniques


Cost Optimization and Resource Management

MODULE 12: CAPSTONE PROJECT (HANDS-ON)
MODULE 13: REAL-WORLD USE CASES AND CASE STUDIES

Industry-Specific Data Engineering Use Cases


Case Studies in Finance, Healthcare, E-commerce, etc.


Best Practices from Real-world Implementations


Challenges and Solutions in Complex Scenarios


Scalable Architectures for High-Volume Data

MODULE 14: AWS DATA ENGINEERING CERTIFICATION PREPARATION

Review of Key Concepts for AWS Data EngineeringCertification (if applicable)


Practice Exams and Mock Assessments


Tips and Strategies for Certification Success


Resources for Further Exam Preparation