DATABRICKS WITH PYSPARK

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The Databricks with PySpark course is designed for learners who want to build skills in big data processing, data engineering, and analytics using Databricks and PySpark. The training covers data transformation, distributed computing, ETL pipeline development, Spark architecture, data analysis, and real-world data engineering workflows. Through hands-on exercises and practical projects, students learn how to process large datasets efficiently and build scalable data solutions in cloud environments. As organizations increasingly rely on data-driven decision-making, this course is ideal for data engineers, data analysts, Spark developers, ETL professionals, and anyone looking to advance their career in modern data engineering and analytics.

Duration of Training : 60 Hours
Batch type : Weekdays/Weekends
Mode of Training : Classroom/Online/Corporate Training
Detailed Syllabus • Hands-on Labs • Assignments • Support-Focused • Implementation

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Overview

The Syllabus

Curriculum Designed by Experts

Module 1: Introduction to Big Data & Databricks

Introduction to Big Data
Challenges of Traditional Data Processing
Big Data Ecosystem Overview
Introduction to Apache Spark
What is Databricks?
Databricks Architecture
Lakehouse Architecture
Data Engineering Concepts
Batch vs Real-Time Processing
Distributed Computing Fundamentals
Databricks Workspace Overview
Databricks Use Cases in Industry

Module 2: Databricks Environment Setup

Creating Databricks Workspace
Cluster Configuration
Notebook Basics
Workspace Management
Databricks Runtime
Job Scheduling
DBFS (Databricks File System)
Secrets Management Basics
Workspace Security Basics
Cost Optimization Basics
Integration with Cloud Platforms

Module 3: Python Fundamentals for PySpark

Python Basics
Variables & Data Types
Operators
Conditional Statements
Loops
Functions
File Handling
Exception Handling
Modules & Packages
Working with JSON & CSV Files
Python Libraries for Data Engineering

Module 4: Apache Spark Fundamentals

Introduction to Apache Spark
Spark Architecture
Spark Components
Driver & Executor Concepts
Spark Cluster Managers
RDD Fundamentals
DataFrames Introduction
Datasets Overview
Lazy Evaluation
Transformations & Actions
Spark Execution Workflow

Module 5: PySpark DataFrame Operations

Creating DataFrames
Reading CSV, JSON, Parquet Files
Schema Definition
Selecting & Filtering Data
Sorting & Aggregations
GroupBy Operations
Joins in PySpark
Window Functions
User Defined Functions (UDF)
Handling Null Values
Duplicate Removal
DataFrame Optimization Techniques

Module 6: Data Processing & Transformation

ETL Concepts
Data Cleansing
Data Validation
Data Transformation Techniques
Data Enrichment
Flattening Nested Data
Complex JSON Processing
Incremental Data Loading
Batch Processing
Data Partitioning
Data Deduplication

Module 7: Delta Lake & Lakehouse Architecture

Introduction to Delta Lake
ACID Transactions
Delta Tables
Time Travel Feature
Schema Enforcement
Schema Evolution
Data Versioning
MERGE Operations
OPTIMIZE & VACUUM
Delta Lake Best Practices
Data Lake vs Lakehouse

Module 8: Databricks SQL & Analytics

Databricks SQL Basics
SQL Queries in Databricks
Joins & Subqueries
Window Functions
Temporary Views
Materialized Views Basics
Data Visualization
Dashboard Creation
Query Optimization
SQL Analytics Workflows

Module 9: Real-Time Streaming with PySpark

Introduction to Spark Streaming
Structured Streaming Concepts
Streaming Architecture
Reading Streaming Data
Kafka Integration Basics
Streaming Transformations
Watermarking Concepts
Window Operations
Streaming Aggregations
Fault Tolerance in Streaming
Real-Time Data Pipelines

Module 10: Cloud Integration with Databricks

AWS Integration
S3 Integration
IAM Roles Basics
AWS Data Pipeline Concepts
Azure Integration
Azure Data Lake Storage
Azure Synapse Basics
Azure Databricks
Google Cloud Basics
GCS Integration
BigQuery Integration Basics

Module 11: Security & Governance

Databricks Security Basics
Workspace Access Control
Role-Based Access Control
Data Encryption Basics
Secure Cluster Access
Unity Catalog Basics
Data Governance Concepts
Audit Logging
Compliance Basics

Module 12: Performance Optimization & Tuning

Spark Optimization Basics
Partitioning Strategies
Caching & Persistence
Broadcast Joins
Shuffle Optimization
Cluster Sizing
Job Performance Monitoring
Memory Management Basics
Spark UI Analysis
Cost Optimization

Module 13: Workflow Automation & Orchestration

Databricks Workflows
Job Scheduling
Notebook Automation
CI/CD Basics
Integration with Airflow
Delta Live Tables Basics
Workflow Monitoring
Pipeline Automation

Module 14: AI/ML Basics in Databricks

Introduction to MLflow
Machine Learning Workflow
Feature Engineering Basics
Model Tracking
Experiment Management
AI Integration Basics
Generative AI Overview
AI Use Cases in Data Engineering

Module 15: Monitoring & Troubleshooting

Cluster Monitoring
Job Failure Analysis
Spark UI Troubleshooting
Streaming Failure Recovery
Log Analysis
Debugging PySpark Jobs
Data Quality Monitoring
Pipeline Health Monitoring

Hands-on Labs & Assignments

Databricks Workspace Setup
PySpark Data Processing Labs
Delta Lake Table Creation
ETL Pipeline Development
Kafka Streaming Integration
SQL Analytics Dashboard
Data Cleansing Exercises
Spark Optimization Labs
Cloud Storage Integration
Workflow Automation Tasks

Real-Time Projects

Project 1: Retail Sales Data Pipeline
Ingest sales data
Clean & transform data
Build analytics dashboards

Project 2: Real-Time Log Analytics System
Kafka data ingestion
Streaming analytics
Alert generation

Project 3: Healthcare Data Lakehouse
Process patient records
Data quality validation
Delta Lake implementation

Project 4: Banking Fraud Detection Pipeline
Real-time transaction processing
Fraud pattern analysis
Streaming alerts

Project 5: E-Commerce Recommendation Analytics
User behavior analysis
Product recommendation insights
Dashboard visualization

Job-Oriented Scenarios

Optimize Slow Spark Jobs
Handle Large Volume Data Processing
Real-Time Streaming Failure Troubleshooting
Delta Lake Data Recovery
Cloud Storage Access Issues
Data Pipeline Failure Analysis
Spark Memory Optimization
Incremental Data Processing Scenarios
Multi-Cloud Databricks Integration

Assignments

Build ETL Pipelines
Create Delta Lake Tables
Implement Streaming Workflows
Optimize Spark Jobs
Build SQL Dashboards
Develop Data Validation Frameworks
Automate Databricks Workflows

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Live Online Training

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Global Certification Preparation

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Assignments and Projects Guided by Mentors

And Many More Features

Course completion certificate and Global Certifications are part of our all Master Program

Live Classroom Training

Weekend / Weekdays / Morning / Evening Batches

80:20 Practical and Theory Ratio

Real-life Case Studies

Easy Coverup if you missed any sessions

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Life Time Video Classroom Access ( coming soon )

Resume Preparations and Mock Interviews

And Many More Features

Course completion certificate and Global Certifications are part of our all Master Program

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Self Paced Learning

Learn 300+ Courses at Your Own Time

50000+ Satisfied Learners

Course Completion Certificate

Practical Labs Available

Mentor Support Available

Doubt Clearing Session Available

Attend Our Virtual Job Fair

10% Discounted Global Certification

Course completion certificate and Global Certifications are part of our all Master Program

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