Lecture 15 – Big Data Analytics for Data Mining

Lecture 15 explains Big Data Analytics for Data Mining, including Hadoop, Spark, HDFS, MapReduce, distributed storage, real-time processing, MLlib, large-scale clustering, classification, and industry case studies.

As organizations generate enormous volumes of data from sensors, mobile apps, IoT devices, social networks, transactions, and enterprise systems, traditional data mining methods become insufficient. Big Data technologies enable processing, storage, and mining of data that is too large, too fast, or too complex for a single machine.

This lecture builds a deep understanding of modern Big Data Analytics and how it empowers Data Mining in distributed environments.

Introduction to Big Data Analytics

What Is Big Data?

Big Data refers to datasets that are:

• Too large
• Too fast
• Too diverse
  for traditional systems to handle.

The 5Vs of Big Data

V	Description
Volume	Massive size (TBs, PBs)
Velocity	High-speed generation
Variety	Structured, semi-structured, unstructured
Veracity	Data quality issues
Value	Useful insights extracted

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Why Traditional Data Mining Fails at Big Data

Memory Limitations

Most ML algorithms assume all data fits in RAM but big datasets exceed memory.

Single Machine Bottlenecks

Single machines cannot:

• Scale horizontally
• Handle streaming data
• Process billions of records

Hence the need for distributed systems.

Big Data Architecture Overview

Big Data systems combine:

• Distributed file systems
• Distributed processing
• Data ingestion engines
• Machine learning pipelines

Distributed Storage

Data is stored across multiple nodes.

Examples:

• HDFS
• Amazon S3
• Google Cloud Storage

Distributed Computing

Computation is split across nodes.

Frameworks:

• Hadoop MapReduce
• Apache Spark
• Apache Flink

Batch vs Real-Time Analytics

Batch Processing

Handles large historical datasets.
Example: Hadoop MapReduce.

Real-Time Processing

Handles streaming data.
Example: Kafka + Spark Streaming.

Hadoop Ecosystem for Data Mining

Apache Hadoop revolutionized large-scale data processing.

HDFS (Hadoop Distributed File System)

Key idea: Split large files into blocks and store across cluster nodes.

Block Diagram:

File → Blocks → Distributed Nodes

NameNode & DataNode

• NameNode → Master, manages metadata
• DataNode → Stores actual blocks

MapReduce

A programming model with two phases:

Map Phase:

Processes input and produces key-value pairs.

Reduce Phase:

Aggregates values for each key.

Example (Word Count):

Map: (word, 1)
Reduce: sum counts

Hive & Pig

Tool	Description
Hive	SQL-like queries on Hadoop
Pig	Dataflow scripts for ETL

AWS Big Data Blog

Apache Spark for Big Data Mining

Spark is the most widely used big data engine today.

Spark Architecture

Spark uses:

• Cluster Manager
• Executors
• Driver Program

Spark RDDs & DataFrames

RDD: Resilient Distributed Dataset

Low-level, fault-tolerant distributed collection.

DataFrames

Optimized, structured, SQL-compatible.

Spark SQL

Run SQL queries on distributed data.

Spark Streaming

Processes live data streams.

Sources:

• Kafka
• Flume
• TCP sockets

Spark MLlib

Spark’s machine learning library includes:

• KMeans
• Logistic Regression
• Decision Trees
• Random Forests
• Gradient Boosted Trees
• FP-Growth

Distributed Processing Models

In-Memory Computation

Spark keeps data in memory, making it much faster than Hadoop MapReduce.

DAG Execution Model

Spark builds a Directed Acyclic Graph (DAG) for optimized execution.

Cluster Managers

• YARN
• Mesos
• Kubernetes

Lecture 14 – Time Series Data Mining & Sequential Pattern Analysis

Data Mining on Hadoop

Distributed Classification

Algorithms implemented using MapReduce:

• Naive Bayes
• Decision Trees

Distributed Clustering

KMeans on Hadoop iterates using MapReduce jobs.

Large-Scale Association Rules

FP-Growth distributed implementations scan partitions independently.

Data Mining on Spark

MLlib Pipelines

Automated pipelines include:

• Data preprocessing
• Model training
• Evaluation

Logistic Regression Example

LogisticRegression()
  .setMaxIter(20)
  .fit(trainingData)

KMeans on Spark

KMeans().setK(3).fit(data)

FP-Growth

FPGrowth(minSupport=0.2).fit(df)

Big Data Storage Technologies

HDFS

Distributed block storage.

NoSQL Databases

Designed for unstructured data.

Examples:

• MongoDB
• Cassandra
• HBase

Columnar & Document Stores

Great for analytics.

Examples:

• Parquet
• ORC

Big Data Processing Tools

Kafka

Distributed event streaming.

Flink

Streaming-first processing engine.

Storm

Low-latency real-time processing.

Case Studies

Netflix

Uses Spark for:

• Recommendations
• Real-time personalization
• A/B testing

Uber

Uses Kafka + Spark for:

• Surge pricing
• Driver matching
• Route optimization

Amazon

Uses Hadoop + EMR for:

• Customer analytics
• Inventory prediction
• Fraud detection

Summary

Lecture 15 explored Big Data Analytics for Data Mining including Hadoop, Spark, distributed storage, real-time streaming, MLlib, distributed clustering/classification, and real-world industry use cases. Students gain the knowledge required to handle large-scale analytical systems and the transition from traditional data mining to modern distributed AI pipelines.

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