Data Engineering & Real-Time Processing Systems Internship Program: 6-Week Structured Learning and Experience

Introduction

Data Engineering is at the heart of modern data-driven organizations, enabling the collection, transformation, and delivery of data for analysis, insights, and decision-making. This internship program provides a comprehensive understanding of data pipelines, distributed systems, and real-time data processing using industry-standard tools such as Hadoop, Spark, and Kafka. Participants will gain practical experience in building scalable data solutions, understanding the differences between batch and stream processing, and integrating cloud storage for modern data architectures.

The course is designed for aspiring data engineers and analytics professionals aiming to bridge the gap between theoretical concepts and real-world big data applications. It culminates in a real-time data processing mini-project that consolidates all learned skills in a functional pipeline.

Program Highlights

Week 1: Introduction to Data Engineering & Hadoop Ecosystem

·      Role of Data Engineers: Explore responsibilities, infrastructure setup, and real-world use cases in modern businesses.

·      Setting Up Hadoop: Learn about distributed storage and processing by configuring a single-node Hadoop 3.x cluster.

·      Understanding HDFS: Study how data is stored and accessed in a distributed file system.

Week 2: MapReduce Programming & Apache Spark Fundamentals

·      MapReduce Model: Write a word count program in Java/Python and understand MapReduce job execution.

·      Apache Spark Introduction: Install Spark and perform basic RDD operations (map, reduce, filter) for data manipulation.

Week 3: Structured Data with Spark SQL & ETL Pipeline

·      Spark SQL & DataFrames: Load CSV files, perform SQL queries, and explore structured data processing.

·      ETL Pipeline Creation: Extract data from CSV, transform it, and load it into JSON format using Spark.

Week 4: Event Streaming with Apache Kafka

·      Kafka Basics: Install Kafka, configure topics, and produce/consume messages using command-line tools.

·      Kafka Producer-Consumer App: Develop an application using Python/Java to simulate real-time data flow.

Week 5: Real-Time Data Processing & Cloud Data Storage

·      Integrating Spark & Kafka: Build a Spark Streaming job to consume Kafka messages and process data in real-time.

·      Introduction to Data Lakes: Understand the architecture, benefits, and cloud solutions for data lakes (e.g., AWS S3, Azure Blob).

·      Cloud Storage: Upload and manage data using AWS S3 or equivalent services.

Week 6: Monitoring, Comparison, and Mini Project

·      Batch vs Stream Processing: Compare methodologies, tools, and use cases in a structured format.

·      Data Pipeline Monitoring: Explore tools like Apache Airflow and Prometheus for pipeline monitoring.

·      Case Study: Analyze a real-world business case utilizing real-time data processing.

·      Mini Project: Build a complete pipeline using Kafka → Spark Streaming → File Storage with architecture diagrams, code, and reports.

Expected Outcomes

By the end of this internship, participants will:

·      Understand the role and importance of data engineering in modern enterprises.

·      Gain hands-on experience with Hadoop, MapReduce, Spark, and Kafka.

·      Build ETL pipelines and process both batch and real-time data efficiently.

·      Integrate cloud storage solutions for scalable data management.

·      Analyze performance differences between batch and stream processing.

·      Monitor and manage data pipelines using industry-standard tools.

·      Complete a mini project demonstrating end-to-end real-time data processing skills.