AWS Data Analytics Services: Curriculum Overview

This document provides a structured roadmap for mastering AWS Data Analytics services as required for the AWS Certified Cloud Practitioner (CLF-C02). It covers the tools used to ingest, process, store, and visualize data at scale.

## Prerequisites

Before diving into Data Analytics, students should possess a foundational understanding of the following:

• Cloud Fundamentals: Understanding of the AWS Global Infrastructure (Regions and Availability Zones).
• Storage Basics: Proficient knowledge of Amazon S3 (buckets, objects, and storage classes) as it acts as the data lake for most analytics workflows.
• Basic SQL: Familiarity with standard SQL queries (SELECT, FROM, WHERE) for tools like Amazon Athena and Redshift.
• Data Concepts: A general understanding of the difference between structured data (databases) and unstructured data (logs, media files).

## Module Breakdown

## Visualizing the Data Pipeline

## Learning Objectives per Module

Module 1: Real-time Streaming with Kinesis

• Objective: Differentiate between Kinesis Data Streams (low-latency) and Kinesis Data Firehose (delivery to S3/Redshift).
• Key Skill: Identifying when to use Kinesis Video Streams for camera telemetry versus Kinesis Data Analytics for SQL on moving data.

Module 2: The Data Organizer (AWS Glue)

• Objective: Explain the role of a Data Catalog in managing metadata.
• Key Skill: Understanding the ETL process (Extract, Transform, Load) to clean and prep data for analysis.

Module 3: Interactive Querying (Amazon Athena)

• Objective: Execute standard SQL queries directly against data stored in Amazon S3.
• Key Skill: Recognizing Athena as serverless—no infrastructure to manage, pay only for the queries run.

Module 4: Business Intelligence (Amazon QuickSight)

• Objective: Create data visualizations, charts, and interactive dashboards.
• Key Skill: Connecting QuickSight to various AWS sources (RDS, S3, Redshift) for reporting.

## Formula / Concept Box: Batch vs. Streaming

## Examples: Use Case Scenarios

[!TIP] Use these scenarios to decide which service fits the business need.

1. The Ad-Hoc Analyst: A researcher has 50GB of CSV logs in an S3 bucket and needs to find specific error codes immediately.
   • Service: Amazon Athena (Direct SQL on S3).
2. The Digital Marketer: A company needs a visual dashboard to track sales performance across different regions in real-time.
   • Service: Amazon QuickSight.
3. The Video Security Firm: A facility needs to ingest thousands of hours of security footage for AI facial recognition.
   • Service: Amazon Kinesis Video Streams.
4. The Legacy Migrator: A bank wants to move 10 years of structured financial records into a massive, searchable warehouse.
   • Service: Amazon Redshift.

## Real-World Application

In modern enterprises, data analytics is the engine of decision-making:

• E-commerce: Using Kinesis to track clickstream data and QuickSight to show marketing teams which products are trending right now.
• Healthcare: Using AWS Glue to scrub sensitive patient data before moving it into a data lake for medical research.
• Finance: Using Amazon Redshift to run complex queries on petabytes of transaction history to identify long-term market trends.

## Success Metrics

To demonstrate mastery of this curriculum, the student must be able to:

• Correctly identify which service performs ETL (Answer: Glue).
• Explain how Athena interacts with Amazon S3 (Answer: SQL queries on raw data).
• Distinguish between Redshift (Data Warehouse) and EMR (Big Data Frameworks).
• Select the appropriate tool for visualization (Answer: QuickSight).
• Identify the service used for real-time ingestion of data (Answer: Kinesis).