Big Data Technologies

Big Data Technologies are designed to store, process, and analyze extremely large volumes of data that are too big, too fast, or too complex for traditional databases and systems.

Modern companies like Google, Amazon, Netflix, Uber, and Flipkart generate terabytes to petabytes of data daily, making Big Data technologies essential.

1. Big Data Concepts

What Is Big Data?

Big Data refers to datasets that:

• Are extremely large in size
• Are generated continuously
• Cannot be efficiently processed using traditional systems

Big Data is not just about size, but about complexity and speed.

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The 5 V’s of Big Data

1. Volume

• Massive amounts of data
• Examples: logs, transactions, images, videos

2. Velocity

• Speed at which data is generated
• Examples: sensor data, social media feeds

3. Variety

• Different types of data
• Structured, semi-structured, unstructured

4. Veracity

• Data quality and reliability
• Noise, missing values, inconsistencies

5. Value

• Extracting useful insights from data

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Why Traditional Systems Fail

• Single-machine processing
• Limited scalability
• High hardware costs
• Slow processing

Big Data systems use distributed computing to solve these problems.

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2. Hadoop Ecosystem

What Is Hadoop?

Apache Hadoop is an open-source framework that allows distributed storage and processing of large datasets across clusters of computers.

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Core Components of Hadoop

1. HDFS (Hadoop Distributed File System)

HDFS is responsible for storage.

Key Features:

• Stores data in blocks (default 128MB)
• Replicates data across nodes
• Fault-tolerant

Architecture:

• NameNode → Metadata manager
• DataNode → Actual data storage

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2. YARN (Yet Another Resource Negotiator)

YARN manages cluster resources.

It:

• Allocates CPU and memory
• Schedules jobs
• Enables multiple applications to run

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3. MapReduce

MapReduce is a batch processing model.

Phases:

• Map → Process data chunks
• Shuffle → Sort and group
• Reduce → Aggregate results

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Hadoop Ecosystem Tools

Tool	Purpose
Hive	SQL-like queries
Pig	Data flow scripting
HBase	NoSQL database
Sqoop	RDBMS to Hadoop
Flume	Log ingestion
Oozie	Workflow scheduling

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Limitations of Hadoop

• Slow batch processing
• Complex programming
• High latency

This led to the rise of Apache Spark.

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3. Spark Basics

What Is Apache Spark?

Apache Spark is a fast, in-memory distributed computing framework used for big data processing.

Spark is 100× faster than MapReduce for some workloads.

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Why Spark Is Popular

• In-memory computation
• Simple APIs
• Supports batch and streaming
• Works with Hadoop

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Spark Core Concepts

RDD (Resilient Distributed Dataset)

• Immutable distributed data structure
• Fault-tolerant
• Parallel processing

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DataFrames & Datasets

• Structured data abstraction
• Optimized using Catalyst optimizer
• Easier than RDDs

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Spark Ecosystem

• Spark SQL
• Spark Streaming
• MLlib
• GraphX

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4. PySpark

What Is PySpark?

PySpark is the Python API for Apache Spark.

It allows data scientists to use Spark’s power with Python’s simplicity.

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Why PySpark Is Important

• Python popularity
• Scalable data processing
• Integrates with ML pipelines

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Key PySpark Operations

Transformations

• map
• filter
• select
• groupBy

Lazy evaluation is used.

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Actions

• collect
• count
• show
• save

Actions trigger execution.

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Example Use Case

Processing millions of job postings on a platform like CommonJobs to:

• Analyze skills demand
• Track hiring trends
• Generate reports

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5. Spark SQL

What Is Spark SQL?

Spark SQL allows querying structured and semi-structured data using SQL.

It bridges traditional SQL with big data.

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Why Spark SQL Is Powerful

• Familiar SQL syntax
• Optimized query execution
• Works with DataFrames

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Features of Spark SQL

• Supports Hive queries
• Handles JSON, Parquet, ORC
• Uses Catalyst Optimizer