Big Data
Why?
Ever wondered how Netflix recommends movies you'll love, or how Google processes billions of searches instantly? That's the magic of big data! In our digital world, we're generating massive amounts of data every second - from social media posts to online purchases. Traditional computers just can't handle this scale anymore. This course teaches you the superpowers needed to tame these data giants using the same tools that power companies like Facebook, Amazon, and Netflix. You'll learn to think big and work with systems that can crunch through petabytes of data like it's nothing. These skills are incredibly valuable - big data professionals are in high demand across every industry!
What?
This course takes you through the essential big data technologies step by step. You'll start with understanding what makes data 'big' and why traditional systems can't handle it. Then you'll learn Hadoop - the foundation of big data processing - along with its key components like HDFS for storage, YARN for resource management, and HBase for fast database operations. You'll also explore modern tools like Kafka for real-time data streams, Spark for fast processing, Hive for SQL-like queries, and Pig for data transformations. The course balances theory with practical experience, so you'll understand how these systems work and actually use them through command-line operations.
Curriculum:
Big Data Fundamentals
Introduction to big data concepts and characteristics. Understanding the 4 Vs of big data: Volume (immense amount of data generated daily), Variety (different types of data), Velocity (rapid rate of data generation), and Veracity (uncertainty and incompleteness of data). Exploration of big data challenges that traditional systems face due to size, complexity, and processing speed requirements. Overview of big data solutions and introduction to Hadoop as the primary framework for distributed computing and storage.
Hadoop Fundamentals and HDFS
Comprehensive introduction to Hadoop as an open-source software framework for storing and running applications on clusters of commodity hardware. Understanding Hadoop's importance in providing distributed computing capabilities, fault tolerance, and scalability. Deep dive into Hadoop Distributed File System (HDFS) architecture, including NameNode (master), DataNodes (slaves), and Backup Nodes. Learning HDFS operations, commands, and data flow processes. Exploring concepts like block structure, replication strategy, rack awareness, and fault tolerance mechanisms.
YARN - Yet Another Resource Negotiator
Understanding YARN as Hadoop's resource management platform that separates resource management from data processing. Learning YARN architecture with ResourceManager, NodeManager, ApplicationMaster, and Container components. Exploring how YARN overcomes MapReduce v1 limitations by enabling multiple processing frameworks to run on the same Hadoop cluster simultaneously. Understanding scheduling mechanisms including FIFO, Capacity, and Fair schedulers. Examining YARN workflow and how it improves cluster utilization and application performance.
HBase - Distributed NoSQL Database
Introduction to HBase as a distributed, column-oriented database built on top of HDFS. Understanding HBase data model based on Google's Bigtable, including concepts of row keys, column families, columns, timestamps, and cells. Learning HBase architecture with HMaster, RegionServers, and ZooKeeper integration. Exploring HBase physical and logical data models, storage mechanisms, and key operations (Get, Put, Scan, Delete). Understanding when to use HBase and comparing it with traditional RDBMS and HDFS. Practical experience with HBase shell commands and basic operations.
Apache Kafka - Distributed Streaming Platform
Introduction to Apache Kafka as a distributed streaming platform for building real-time data pipelines and streaming applications. Understanding Kafka's publish-subscribe model with producers sending messages and consumers receiving them. Learning Kafka architecture including brokers, topics, partitions, and consumer groups. Exploring Kafka's characteristics: high throughput, real-time processing, fault tolerance, and scalability. Understanding use cases for real-time and batch processing, integration with Apache Spark and Hadoop ecosystem. Learning about partition replication, leader-follower concepts, and ZooKeeper's role in cluster management.
Apache Spark - Unified Analytics Engine
Introduction to Apache Spark as a distributed computing system designed for big data processing and analytics. Understanding Spark's advantages over MapReduce: speed, efficiency, and support for multiple programming languages. Learning Spark architecture including Driver, Cluster Manager, Workers, and Executors. Exploring Spark RDDs (Resilient Distributed Datasets) with their features of resilience, distributed processing, immutability, and lazy evaluation. Understanding RDD operations including transformations (map, filter, flatMap, join) and actions (collect, count, reduce, save). Practical experience with Spark programming model and basic transformations.
Hadoop Ecosystem Tools: PIG, HIVE, and ZOOKEEPER
Comprehensive overview of essential Hadoop ecosystem tools. Learning Apache Pig as a platform for analyzing large datasets with Pig Latin scripting language, including data flow concepts and execution modes. Understanding Apache Hive as a data warehousing system that provides SQL-like interface (HiveQL) for querying big data stored in Hadoop. Exploring Hive data models, partitions, buckets, and major components (UI, Driver, Metastore, Compiler, Execution Engine). Introduction to Apache ZooKeeper as a centralized coordination service for distributed applications, including naming services, configuration management, cluster management, and leader election. Comparing and contrasting these tools for different big data processing scenarios.
Notes
This course is more about understanding the concepts and architectures of big data solutions (not using them). Focus heavily on understanding the architecture of each system - draw diagrams if it helps! For the exam, practice comparing different tools (when to use Spark vs MapReduce, HBase vs traditional databases, etc.). The Hadoop commands seem tricky at first, but install it on your machine and play around with it to get used to it. For Spark transformations, understand the difference between transformations and actions, and know the common ones like map, filter, and reduce. You can try it with pySpark library in Python. Don't get overwhelmed by all the acronyms (HDFS, YARN, RDD...) make a cheat sheet! Most importantly, try to understand WHY each tool exists and what problem it solves. The professor loves asking 'when would you use X instead of Y' so think practically about use cases.
