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Sri Raghavendra Educational Institutions Society (R)

(Approved by AICTE, Accredited by NAAC, Affiliated to VTU, Karnataka)

Sri Krishna Institute of Technology

www.skit.org.in

Title: Technologies used in Big data Environments

CO addressed: CO1

Course: Big Data Analytics

Presented by: Mr. P. Kiran Kumar

Department: ISE

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/skit.org.in

(Approved by AICTE, Accredited by NAAC, Affiliated to VTU, Karnataka)

Sri Krishna Institute of Technology

1: In-Memory Analytics:

In-Memory Analytics stores data in Random Access Memory (RAM) instead of reading it repeatedly from the hard disk. Since RAM is much faster than secondary storage, data can be processed quickly, enabling real-time analytics and faster decision-making.

Key Points

  • Stores data in RAM instead of hard disk.
  • Provides faster data access and processing.
  • Supports real-time analytics.
  • Reduces query response time.
  • Improves business decision-making.

Example

Amazon/Flipkart: During a festive sale, millions of users search for products. Frequently accessed product information is stored in RAM, allowing search results and recommendations to appear almost instantly.

  • Hard Disk (Slow) → Load into RAM → CPU Processing → Fast Analytics Results

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(Approved by AICTE, Accredited by NAAC, Affiliated to VTU, Karnataka)

Sri Krishna Institute of Technology

2: In-Database Processing

In-Database Processing performs data analysis inside the database instead of exporting data to external analytical software. This saves time, reduces data movement, and improves performance.

Key Points

  • Analytics are performed within the database.
  • Eliminates exporting large datasets.
  • Reduces processing time.
  • Improves security.
  • Minimizes network traffic.

Example

Banking System: A bank wants to calculate the total amount transferred today. Instead of exporting millions of records to another application, the database calculates the total directly.

OLTP Database

↓

Data Warehouse

↓

Database Analytics → Results

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(Approved by AICTE, Accredited by NAAC, Affiliated to VTU, Karnataka)

Sri Krishna Institute of Technology

3: Symmetric Multiprocessing (SMP)

SMP is a multiprocessor system where multiple processors share a common memory and a single operating system. All processors work together simultaneously to improve performance.

Key Points

  • Multiple processors share one memory.
  • Uses a single operating system.
  • High-speed communication.
  • Suitable for medium-scale processing.
  • Easy to program.

Example

Multi-Core Laptop: An 8-core laptop edits a video. Different cores process video, audio, and effects simultaneously while sharing the same RAM.

​

CPU1 CPU2 CPU3

\ | /

Shared Memory

|

One OS

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(Approved by AICTE, Accredited by NAAC, Affiliated to VTU, Karnataka)

Sri Krishna Institute of Technology

4: Massively Parallel Processing (MPP)

MPP is a system where many processors work in parallel, each having its own memory and operating system. The processors communicate through messages while processing different parts of the same task.

Key Points

  • Independent processors.
  • Each processor has its own memory.
  • Highly scalable.
  • Suitable for Big Data processing.
  • Faster execution of huge datasets.

Example

Google Search: When you search on Google, thousands of servers process different portions of the web simultaneously and combine the results.

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CPU1 → RAM1

CPU2 → RAM2

CPU3 → RAM3

CPU4 → RAM4

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(Approved by AICTE, Accredited by NAAC, Affiliated to VTU, Karnataka)

Sri Krishna Institute of Technology

5: Parallel vs Distributed Systems

A Parallel System consists of multiple processors working together within a single machine. A Distributed System consists of multiple independent computers connected through a network, each with its own memory and storage.

Key Differences

Parallel System → Single machine, Shared memory, Faster communication

Distributed System → Multiple machines, Separate memory, Network communication

Example

Parallel: A gaming PC with an 8-core processor rendering a 3D animation.

Distributed: A Hadoop cluster where 100 computers process different parts of a large dataset.

Parallel:

CPU1 CPU2 CPU3

↓

Shared Memory

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(Approved by AICTE, Accredited by NAAC, Affiliated to VTU, Karnataka)

Sri Krishna Institute of Technology

6: Shared Nothing Architecture

In Shared Nothing Architecture, each node has its own processor, memory, and disk. No hardware resources are shared, making the system highly scalable and fault tolerant.

Key Points

  • No shared memory.
  • No shared disk.
  • Independent nodes.
  • High scalability.
  • Fault isolation.

Example

Hadoop Cluster: Each DataNode has its own CPU, RAM, and storage. If one node fails, the remaining nodes continue processing without affecting the system.

​

Node1 Node2 Node3

CPU CPU CPU

RAM RAM RAM

Disk Disk Disk

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(Approved by AICTE, Accredited by NAAC, Affiliated to VTU, Karnataka)

Sri Krishna Institute of Technology

7: CAP Theorem

The CAP Theorem states that a distributed system can guarantee only two out of three properties:

Consistency (C): Every user gets the latest data.

Availability (A): Every request receives a response.

Partition Tolerance (P): The system continues working even if network failures occur.

Examples

Consistency (C):

After withdrawing ₹500 from an ATM, every ATM and the banking app immediately show the updated balance.

Availability (A):

Instagram continues serving users even if some likes or comments take a few seconds to update.

Partition Tolerance (P):

Google Drive continues working even if one data center is temporarily disconnected.

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