Big Data Concepts and Scaling Methods

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Questions and Answers

What are the two main layers of Hadoop?

Data Warehousing and Querying Engine

Storage Layer and Analysis Layer

Database Management System and Application Layer

Distributed File System (HDFS) and Execution Engine (MapReduce) (correct)

What architectural model does Hadoop use?

Monolithic Architecture

Master-Slave Shared-Nothing Architecture (correct)

Client-Server Architecture

Peer-to-Peer Architecture

Which of the following is a motivation for using HDFS?

To enable data storage on a single high-capacity server

To avoid data replication for efficiency

To manage data across multiple machines to handle large datasets (correct)

To run on high-end, expensive hardware

How does HDFS handle hardware failures?

It replicates the data to ensure availability Signup and view all the answers

What problem does the replication of data in HDFS solve?

The risk of data loss due to machine failure Signup and view all the answers

What is one primary advantage of using Hadoop for data storage?

Redundant, fault-tolerant storage Signup and view all the answers

Who were the original creators of Hadoop?

Doug Cutting and Mike Cafarella Signup and view all the answers

Which of the following best describes Hadoop's programming framework?

Parallel computing framework Signup and view all the answers

What does the 'Volume' aspect of Big Data refer to?

The total amount of data generated Signup and view all the answers

In what year was Hadoop donated to the Apache Foundation?

2006 Signup and view all the answers

Which of the following describes the 'Velocity' aspect of Big Data?

Data needs to be processed quickly Signup and view all the answers

What task did the New York Times undertake using Hadoop’s technology?

Translating TIFF images to PDF files Signup and view all the answers

What are the two main technologies that Hadoop is based on?

Google File System (GFS) and MapReduce Signup and view all the answers

What is a characteristic of 'Variety' in Big Data?

Data comes in various formats Signup and view all the answers

What is a significant concern when using large data frameworks like Hadoop?

Handling failures and data loss Signup and view all the answers

Which method is commonly used by Google for processing large data sets?

MapReduce Signup and view all the answers

What does the 'Divide and Conquer' strategy in Big Data philosophy entail?

Separating tasks and then merging results Signup and view all the answers

What type of model does Hadoop utilize for job coordination?

Distributed file system Signup and view all the answers

What is a challenge associated with scaling out in Big Data environments?

Managing distributed systems Signup and view all the answers

Which of the following is NOT a component of the 3 Vs of Big Data?

Variability Signup and view all the answers

When discussing Big Data, what is a difficulty encountered with parallel programming?

Synchronization between multiple threads Signup and view all the answers

What is one main purpose of having multiple replicas of the same block in HDFS?

To prevent the NameNode from becoming a bottleneck in the cluster Signup and view all the answers

How does the NameNode determine which replica of a block a client should read?

Based on the data locality in the cluster Signup and view all the answers

During the write process in HDFS, what does the client do after connecting to the NameNode?

The client writes blocks directly to the data nodes specified by the NameNode Signup and view all the answers

What is one of the tradeoffs to consider when placing replicas of a block in HDFS?

Balancing reliability with both write and read bandwidth Signup and view all the answers

What happens when a data node fails during the write process in HDFS?

The NameNode identifies the failure and replicates the missing blocks Signup and view all the answers

What is the primary responsibility of the Name Node in HDFS architecture?

File System Name Space management Signup and view all the answers

What is the role of the Data Node in HDFS?

Performing block operations Signup and view all the answers

Why is it beneficial to use blocks in HDFS instead of managing files directly?

Files can exceed the storage capacity of a single disk Signup and view all the answers

What is the default block size in HDFS?

64MB Signup and view all the answers

In an HDFS read operation, what does the Name Node provide to the client?

The location of the data blocks Signup and view all the answers

What happens if a Data Node fails during a read operation in HDFS?

The client automatically connects to another Data Node Signup and view all the answers

What is the purpose of the Secondary Name Node in HDFS?

To serve as a backup for the Name Node Signup and view all the answers

Why does HDFS prefer clients to read blocks directly from Data Nodes instead of going through the Name Node?

It reduces the load on the Name Node Signup and view all the answers

Study Notes

Big Data

3 Vs of Big Data: Volume, Velocity, Variety
Volume: Data volume is increasing exponentially
- 44x increase from 2009-2020
- From 0.8 zettabytes to 35zb
Velocity: Data is generated quickly and needs to be processed fast
- Digital Streams, Social Media, Online Data Analytics
Variety:
- Structured: Relational Data (Tables/Transaction/Legacy Data)
- Text Data (Web)
- Semi-structured Data (XML)
- Graph Data
- Social Network, Semantic Web (RDF)

Scaling

Divide and conquer: Divide work and combine results
Scale out and Scale up are methods for scaling
- Scale out: using more machines
- Scale up: using more powerful machines

Google Case Study

Google processed about 24 petabytes of data per day in 2009
Solution: MapReduce
Challenges:
- A single machine cannot serve all the data
- Need a distributed system to store and process in parallel
- Hard to implement parallel programming and threading

Hadoop

Advantages:
- Redundant, Fault-tolerant data storage
- Parallel computing framework
- Job coordination

Hadoop - A Little History

Hadoop implementation based on Google File System (GFS) and MapReduce
Created by Doug Cutting and Mike Cafarella in 2005
Donated to Apache in 2006

Hadoop Architecture

Two main layers:
- Distributed file system (HDFS)
- Execution engine (MapReduce)
Master-slave shared-nothing architecture

HDFS

Motivation:
- Store large amounts of data on multiple machines
- Use commodity hardware
- Handle hardware failure
- Replicate data
- Coordinated data organization

HDFS Architecture

Name Node: Controller
- File System Name Space Management
- Block Mappings
Data Node: Block Operations, Replication
Secondary name Node: Checkpoint node

HDFS Inside: Blocks

Block size Default: 64MB
Purpose:
- Files can be larger than a single disk
- Fixed block size is easy to manage
- Facilitates replication and load balancing

HDFS Inside: Read

Client connects to Name Node for data location
Client reads blocks directly from data nodes (without going through Name Node)
Client connects to another node when there's a failure

HDFS Inside: Write

Client connects to Name Node to write data
Client writes blocks directly to data nodes with desired replication factor
Name Node addresses node failures and replicates missing blocks

HDFS - Resources

Apache Hadoop Documentation: https://hadoop.apache.org/docs/current/
Data Intensive Text Processing with Map-Reduce: http://lintool.github.io/MapReduceAlgorithms/
Hadoop Definitive Guide: https://www.amazon.com/Hadoop-Definitive-Guide-Tom-White/dp/1449311520

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Description

Explore the fundamental concepts of Big Data, focusing on the 3 Vs: Volume, Velocity, and Variety. Understand scaling strategies such as 'divide and conquer,' and the distinction between scaling out and scaling up. Delve into real-world case studies, including Google's innovative data processing techniques.