Gen AI
Generative AI is a subset of artificial intelligence focused on creating new and original content based on patterns learned from existing data. Unlike traditional AI, which typically analyzes and classifies data, generative AI produces novel outputs, such as text, images, music, or even videos.
Course Instructor Sateesh Pabbathi
₹27,000.00 Original price was: ₹27,000.00.₹24,999.00Current price is: ₹24,999.00.
Course Overview
Generative AI refers to artificial intelligence systems that can create new content or data, such as text, images, or music, based on patterns learned from existing data. These systems use models like Generative Adversarial Networks (GANs) or transformers to produce outputs that mimic human creativity, often with applications in art, design, and content generation.
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Course Curriculum
MODULE 1. INTRODUCTION TO CLOUD DATA ENGINEERING
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.
MODULE 2. SQL FUNDAMENTALS FOR DATA ENGINEERING
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
MODULE 3. PYTHON FOR DATA ENGINEERING
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.)
MODULE 4: CLOUD DATA STORAGE SOLUTIONS
AWS
Amazon S3 (Simple Storage Service)
Amazon RDS (Relational Database Service)
Amazon Redshift
AWS Glue Data Catalog
Azure
Azure Blob Storage
Azure SQL Database
Azure Synapse Analytics
Azure Data Lake Storage
Azure Table Storage
GCP
Google Cloud Storage
BigQuery
Cloud SQL
Cloud Bigtable
MODULE 5: DATA PROCESSING AND ETL
AWS
AWS Glue
Amazon EMR (Elastic MapReduce)
AWS Data Pipeline
AWS Step Functions
Azure
Azure Data Factory
Azure Databricks
Azure Stream Analytics
Azure Functions (for ETL)
GCP
Cloud Dataflow
Dataflow SQL (Apache Beam)
MODULE 6: DATA ORCHESTRATION AND WORKFLOW AUTOMATION
AWS
AWS Step Functions
AWS Data Pipeline
AWS Glue Workflows
Azure
Azure Data Factory
Azure Logic Apps
Azure Functions (for workflow automation)
GCP
Cloud Composer (Apache Airflow)
Cloud Scheduler
Cloud Functions
MODULE 7: REAL-TIME DATA PROCESSING AND STREAM ANALYTICS
AWS
Amazon Kinesis
AWS Lambda (for real-time processing)
Azure
Azure Stream Analytics
Azure Functions (for real-time processing)
GCP
Cloud Pub/Sub
Dataflow (for stream processing)
MODULE 8: DATA MONITORING AND LOGGING
Monitoring Tools (e.g., AWS CloudWatch, Azure Monitor, Google Cloud Operations Suite)
Logging Best Practices for Data Pipelines
Alerting and Anomaly Detection
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
MODULE 9: APACHE SPARK FOR CLOUD DATA ENGINEERING
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
MODULE 10 : DATABRICKS FOR DATA ENGINEERING
Getting Started with Databricks for Cloud Data Engineering
Setting Up a Databricks Workspace
Collaborative Data Processing in Databricks
Version Control and Collaboration Features
Databricks Notebooks and Jobs
Leveraging Databricks for Data Processing and Analysis
Clusters and Scalability in Databricks
Integrations with Cloud Data Storage Solutions
MODULE 11: SERVERLESS COMPUTING FOR DATA ENGINEERING
Serverless Architectures and Compute Services (e.g., AWS
Lambda, Azure Functions)
Benefits and Considerations of Serverless Data Pipelines
MODULE 12: DISASTER RECOVERY AND HIGH AVAILABILITY
Designing for Disaster Recovery and Business Continuity in the Cloud
Implementing High Availability Solutions for Data
Engineering Workloads
MODULE 13: COST OPTIMIZATION AND RESOURCE MANAGEMENT
Cloud Cost Management Strategies (e.g., AWS Cost
Explorer, Azure Cost Management)
Resource Scaling and Optimization Techniques
MODULE 14: DATA SECURITY AND COMPLIANCE IN THE CLOUD