Podcast
Questions and Answers
What are the two main layers of Hadoop?
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?
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?
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?
How does HDFS handle hardware failures?
What problem does the replication of data in HDFS solve?
What problem does the replication of data in HDFS solve?
What is one primary advantage of using Hadoop for data storage?
What is one primary advantage of using Hadoop for data storage?
Who were the original creators of Hadoop?
Who were the original creators of Hadoop?
Which of the following best describes Hadoop's programming framework?
Which of the following best describes Hadoop's programming framework?
What does the 'Volume' aspect of Big Data refer to?
What does the 'Volume' aspect of Big Data refer to?
In what year was Hadoop donated to the Apache Foundation?
In what year was Hadoop donated to the Apache Foundation?
Which of the following describes the 'Velocity' aspect of Big Data?
Which of the following describes the 'Velocity' aspect of Big Data?
What task did the New York Times undertake using Hadoop’s technology?
What task did the New York Times undertake using Hadoop’s technology?
What are the two main technologies that Hadoop is based on?
What are the two main technologies that Hadoop is based on?
What is a characteristic of 'Variety' in Big Data?
What is a characteristic of 'Variety' in Big Data?
What is a significant concern when using large data frameworks like Hadoop?
What is a significant concern when using large data frameworks like Hadoop?
Which method is commonly used by Google for processing large data sets?
Which method is commonly used by Google for processing large data sets?
What does the 'Divide and Conquer' strategy in Big Data philosophy entail?
What does the 'Divide and Conquer' strategy in Big Data philosophy entail?
What type of model does Hadoop utilize for job coordination?
What type of model does Hadoop utilize for job coordination?
What is a challenge associated with scaling out in Big Data environments?
What is a challenge associated with scaling out in Big Data environments?
Which of the following is NOT a component of the 3 Vs of Big Data?
Which of the following is NOT a component of the 3 Vs of Big Data?
When discussing Big Data, what is a difficulty encountered with parallel programming?
When discussing Big Data, what is a difficulty encountered with parallel programming?
What is one main purpose of having multiple replicas of the same block in HDFS?
What is one main purpose of having multiple replicas of the same block in HDFS?
How does the NameNode determine which replica of a block a client should read?
How does the NameNode determine which replica of a block a client should read?
During the write process in HDFS, what does the client do after connecting to the NameNode?
During the write process in HDFS, what does the client do after connecting to the NameNode?
What is one of the tradeoffs to consider when placing replicas of a block in HDFS?
What is one of the tradeoffs to consider when placing replicas of a block in HDFS?
What happens when a data node fails during the write process in HDFS?
What happens when a data node fails during the write process in HDFS?
What is the primary responsibility of the Name Node in HDFS architecture?
What is the primary responsibility of the Name Node in HDFS architecture?
What is the role of the Data Node in HDFS?
What is the role of the Data Node in HDFS?
Why is it beneficial to use blocks in HDFS instead of managing files directly?
Why is it beneficial to use blocks in HDFS instead of managing files directly?
What is the default block size in HDFS?
What is the default block size in HDFS?
In an HDFS read operation, what does the Name Node provide to the client?
In an HDFS read operation, what does the Name Node provide to the client?
What happens if a Data Node fails during a read operation in HDFS?
What happens if a Data Node fails during a read operation in HDFS?
What is the purpose of the Secondary Name Node in HDFS?
What is the purpose of the Secondary Name Node in HDFS?
Why does HDFS prefer clients to read blocks directly from Data Nodes instead of going through the Name Node?
Why does HDFS prefer clients to read blocks directly from Data Nodes instead of going through the Name Node?
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.
