Data Engineering

INTRODUCTION TO CLOUD DATA ENGINEERING

Module 1

Overview of Cloud Computing and Data Engineering: Understanding the fundamentals of cloud computing and how it revolutionizes data engineering.
Benefits of Cloud-Based Data Solutions: Exploring the advantages of using cloud platforms for data storage, processing, and analytics.
Comparison of AWS, Azure, and GCP: A detailed comparison of the leading cloud providers, highlighting their strengths and use cases in data engineering.

SQL FUNDAMENTALS FOR DATA ENGINEERING

Module 2

Mastering SQL Queries for Data Retrieval and Transformation
SELECT Statements and Data Retrieval
Filtering and Sorting Data
Aggregation and Grouping
Joins and Subqueries
Window Functions for Advanced Analysis
Creating and Modifying Tables

PYTHON FOR DATA ENGINEERING

Module 3

Python Essentials for Data Engineers
Data Types, Variables, and Operators
Control Structures (Loops, Conditional Statements)
Functions and Modules
File Handling in Python
Exception Handling
Data Structures (Lists, Dictionaries, etc.)

CLOUD DATA STORAGESOLUTIONS

Module 4

DATA PROCESSING AND ETL

Module 5

DATA ORCHESTRATIONAND WORKFLOW AUTOMATION

Module 6

REAL-TIME DATAPROCESSING AND STREAM ANALYTICS

Module 7

DATA MONITORING ANDLOGGING

Module 8

Monitoring Tools (e.g., AWS Cloud Watch, Azure Monitor, Google Cloud Operations Suite)
Logging Best Practices for Data Pipelines
Alerting and Anomaly Detectio
Implementing Effective Data Monitoring Strategies
Setting up Monitoring Dashboards and Alerts
Log Collection and Aggregation
Anomaly Detection and Alerting
Performance Metrics and KPIs
Error and Exception Handling in Logs
Integrating with Monitoring Tools

REAL-TIME DATAPROCESSING AND STREAM ANALYTICS

Module 9

Introduction to Apache Spark for Cloud Data Processing
Setting Up Apache Spark on Cloud Platforms
Data Ingestion and ETL with Apache Spark on Cloud
Optimizing Data Pipelines with Apache Spark
Spark SQL: Querying and Analyzing Data on the Cloud
Real-time Stream Processing with Apache Spark
Machine Learning with Apache Spark on Cloud
Advanced Techniques for Scalable Data Engineering with Spark
Monitoring and Debugging Apache Spark Applications on the Cloud
Best Practices for Performance and Cost Optimization in Cloud-based Spark Deployments

DATA MONITORING ANDLOGGING

Module 10

Getting Started with Data bricks for Cloud Data Engineering Setting Up a Data bricks Workspace
Collaborative Data Processing in Data bricks
Version Control and Collaboration Features
Data bricks Notebooks and Jobs
Leveraging Data bricks for Data Processing and Analysis
Clusters and Scalability in Data bricks
Integrations with Cloud Data Storage Solutions

SERVERLESS COMPUTINGFOR DATA ENGINEERING

Module 11

DISASTER RECOVERY ANDHIGH AVAILABILITY

Module 12

COST OPTIMIZATION ANDRESOURCE MANAGEMENT

Module 13

DATA SECURITY AND COMPLIANCE IN THE CLOUD

Module 14

Security Best Practices for Cloud Data Engineering
Encryption and Key Management
Access Control and Role-based Permissions
Data Masking and Anonymization
Regulatory Compliance (GDPR, HIPAA, etc.)
Security Auditing and Monitoring
Incident Response and Data Breach Handling

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