Big Data & Data Engineering with Spark, Databricks & AWS

This advanced program is designed to make you an industry-ready Big Data & Data Engineer by covering the complete ecosystem—from Core Java and Scala to Apache Spark, Databricks, AWS Cloud, and Generative AI integration.

You will learn how to process massive datasets, build real-time and batch pipelines, and deploy scalable data solutions using modern tools like Spark, Kafka, Databricks, AWS Glue, Redshift, and EMR.

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About Us

I am Irfan Khan, a seasoned software development professional with over 15 years of experience, specializing in the Hire, Train, and Deploy (HTD) model.

I have successfully trained over 20,000 candidates and helped them secure positions at top MNCs such as ATOS Origin, Zensar, Amdocs, Deloitte, Cerner, Persistent Systems, Nucleus Software, Intellect Design Arena, 3i Infotech, Emphasis, MetricStream, Samsung, and Accenture.

With 17+ years of industry experience, our expert trainer brings deep knowledge and real-world expertise across multiple cutting-edge technologies. Having worked on diverse projects and trained numerous professionals, the trainer is passionate about transforming beginners into confident, job-ready developers.

What You Will Learn

Core Programming (Java + Scala)

  • Core Java fundamentals, OOP, collections, JDBC
  • Scala programming for Big Data (functional + OOP concepts)
  • File handling, APIs, and data processing

Big Data Ecosystem

  • Understanding Big Data (4Vs: Volume, Variety, Velocity, Veracity)
  • Hadoop ecosystem (HDFS, MapReduce)
  • Distributed architecture (Master/Worker nodes, fault tolerance)
  • Tools like Hive, Kafka, Flume

Apache Spark (Core to Advanced)

Core Concepts

  • RDDs, DataFrames, Datasets
  • Transformations, actions, lazy evaluation
  • Fault tolerance and lineage

Spark SQL

  • Querying structured data
  • DataFrame operations (join, groupBy, filter)
  • Integration with Hive

Advanced Topics

  • Spark Streaming (real-time pipelines)
  • Kafka integration
  • UDFs and complex data processing
  • Deploying Spark on AWS/Azure/GCP

Databricks (Lakehouse Platform)

Essentials

  • Lakehouse architecture & Delta Lake
  • ETL pipeline design
  • Performance optimization
  • Workflow automation (Jobs)

Advanced

  • Delta Live Tables (DLT)
  • Unity Catalog & governance
  • Advanced Spark integrations
  • BI dashboards & SQL analytics

Generative AI in Data Engineering

  • GenAI pipeline design
  • LLM orchestration
  • RAG (Retrieval-Augmented Generation) pipelines
  • AI-powered data applications
  • Model monitoring & lifecycle

AWS Cloud for Data Engineering

AWS Fundamentals

  • Cloud concepts, security, pricing
  • Core AWS services

Data Engineering Services

  • AWS Glue - ETL pipelines
  • Amazon Redshift - Data warehousing
  • Amazon S3 - Data lake
  • EMR - Big Data processing
  • Kinesis - Real-time streaming
  • SageMaker - ML integration

ETL / Data Integration (IDMC)

  • Cloud Data Integration (CDI)
  • Data transformations (Joiner, Aggregator, Lookup)
  • Workflow orchestration & scheduling
  • API-based integration (CAI)
  • Monitoring, logging, performance tuning

Capstone Project (Real-World Implementation)

Build a complete Big Data pipeline:

  • Data Lake setup using Amazon S3
  • Real-time streaming using Kinesis
  • ETL pipelines using AWS Glue
  • Data warehouse using Redshift
  • ML integration with SageMaker
  • End-to-end pipeline using IDMC + AWS services