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Big Data Technologies

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Big Data Technologies

Last Updated : July 29, 2025
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About Course

This comprehensive course explores the essential
technologies, architectures, and methodologies for
efficiently processing, storing, and analyzing massive
datasets—from petabytes to exabytes—that challenge
traditional systems. Students will gain hands-on
expertise in designing and implementing robust,
scalable data processing pipelines and advanced
analytics workflows, utilizing industry-leading
distributed computing frameworks like Apache Spark,
Hadoop, and Kafka.

What Will You Learn?

  • Understand the fundamental challenges and strategic opportunities big data presents across various industries, from e-commerce to scientific research.
  • Design effective distributed data storage solutions, including data lakes (e.g., S3, ADLS) and various NoSQL databases (e.g., MongoDB, Cassandra, Neo4j),
  • tailored for diverse data types and access patterns.
  • Implement robust batch processing pipelines using MapReduce and advanced Spark paradigms, alongside real-time stream processing solutions with Apache Kafka and Apache Flink for immediate insights.
  • Apply leading distributed computing frameworks like Apache Spark and Presto to perform complex data transformations and analytical queries on massive,
  • disparate datasets.
  • Develop scalable data engineering workflows, including ETL/ELT processes and data orchestration using tools like Apache Airflow, essential for supporting large-scale AI and machine learning applications.
  • Evaluate critical performance metrics, scalability, cost optimization strategies, and security considerations in designing and operating enterprise-grade big data systems.

Course Content

Introduction to Big Data
Explore the foundational concepts of big data, including the 5 V's (Volume, Velocity, Variety, Veracity, and Value) and their implications. Examine the historical evolution of data processing systems, common big data architecture patterns (e.g., Lambda, Kappa), real-world use cases across finance, healthcare, and retail, and emerging industry trends like Data Mesh and DataOps.

Distributed Storage Systems
Dive into core distributed file systems like HDFS (Hadoop Distributed File System) and various NoSQL databases, including document-oriented (MongoDB), key-value (Redis), column-family (Cassandra), and graph databases (Neo4j). Learn about building and managing scalable data lakes on cloud object storage, optimizing data warehousing solutions for petabyte-scale data, and best practices for data governance, cataloging, and security with tools like Apache Atlas and Ranger.

Batch Processing Frameworks
Understand the foundational MapReduce paradigm and the broader Hadoop ecosystem, including YARN for resource management. Master Apache Spark's unified analytics engine, covering its architecture, RDDs, DataFrames, and Spark SQL programming models. Explore distributed data processing patterns for large-scale ETL, and learn about advanced job scheduling, resource management, and performance tuning techniques for batch workloads on multi-node clusters.

Stream Processing
Contrast real-time versus traditional batch processing, and explore core concepts and challenges unique to stream processing, such as event time vs. processing time. Cover Apache Kafka for high-throughput, fault-tolerant event streaming and delve into robust stream processing frameworks like Spark Streaming and Apache Flink, including advanced topics like windowing operations, stateful processing, and achieving exactly-once semantics for critical data pipelines.

Big Data Analytics
Utilize distributed SQL engines such as Presto/Trino and Apache Hive for interactive queries on data lakes, along with Spark SQL and DataFrames for complex analytical transformations. Learn about implementing machine learning algorithms on distributed systems using Spark MLlib, performing graph processing at scale with GraphX, and applying optimization techniques for accelerating complex analytics workloads and report generation.

Big Data Architecture and Operations
Design end-to-end data pipelines for diverse use cases, from IoT data ingestion to customer analytics, and implement data orchestration using tools like Apache Airflow or Prefect. Address critical aspects of monitoring and troubleshooting in distributed big data systems, consider data security and privacy compliance (GDPR, HIPAA), and explore cost- effective, cloud-based big data services on platforms like AWS EMR, Google Cloud Dataproc, and Azure HDInsight.

Capstone Project
Students will design and implement a comprehensive big data solution for a real-world scenario, such as analyzing sentiment from social media streams or optimizing logistics for a large e-commerce platform. This project will require integrating both batch (e.g., historical data processing) and streaming components (e.g., real-time analytics). You will process and analyze a large, representative dataset (e.g., 1TB+), extract meaningful, actionable insights, optimize the solution for performance and scalability, and thoroughly document your chosen architecture, implementation details, and findings in a professional technical report, including a performance benchmark.

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