Introduction to Hadoop Ecosystem
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What is the primary purpose of Hadoop?

  • To handle big data in a distributed environment (correct)
  • To provide a relational database system
  • To replace traditional hardware systems
  • To rank web pages

Hadoop operates on expensive, specialized hardware.

False (B)

What distributed file system did Google create?

Google File System (GFS)

Hadoop is based on Google's __________.

<p>file system</p> Signup and view all the answers

Which of the following is NOT a property of Hadoop?

<p>High cost (D)</p> Signup and view all the answers

Hadoop can process data on nodes where they are stored.

<p>True (A)</p> Signup and view all the answers

What is one of the main challenges traditional systems face that Hadoop addresses?

<p>Inflexibility</p> Signup and view all the answers

Match the following aspects of Hadoop with their descriptions:

<p>Scalability = Handles data in a distributed manner Economical = Runs on commodity hardware Data Locality = Processes data where it is stored Reliability = Ensures consistent data processing</p> Signup and view all the answers

What is the primary challenge traditional systems face with modern data?

<p>They can only handle structured data (A)</p> Signup and view all the answers

Hadoop is designed to manage only structured data generated over the last 40 years.

<p>False (B)</p> Signup and view all the answers

What do we call the complex framework that handles massive amounts of data?

<p>Hadoop Ecosystem</p> Signup and view all the answers

The data generated today is mostly _____ or unstructured.

<p>semi-structured</p> Signup and view all the answers

Match the following terms with their descriptions:

<p>Hadoop = A framework for managing big data Traditional Systems = Designed for structured data only Structured Data = Data with a defined format Big Data = Massive amount of varied data formats</p> Signup and view all the answers

Why can traditional relational databases become expensive?

<p>They need vertical scaling for performance (B)</p> Signup and view all the answers

Big data can be effectively stored in traditional systems that are over 40 years old.

<p>False (B)</p> Signup and view all the answers

What is one of the major limitations of traditional systems when compared to the Hadoop Ecosystem?

<p>They cannot handle semi-structured or unstructured data.</p> Signup and view all the answers

What is the main advantage of Hadoop compared to vertical scaling in RDBMS?

<p>Horizontal scaling (A)</p> Signup and view all the answers

Hadoop can only handle structured data.

<p>False (B)</p> Signup and view all the answers

What are the two main components of HDFS?

<p>Name Node and Data Node</p> Signup and view all the answers

The block size used by HDFS is ______ MB.

<p>128</p> Signup and view all the answers

What is the primary purpose of the Name Node in Hadoop?

<p>Manage the file system metadata (D)</p> Signup and view all the answers

MapReduce processes tasks in a sequential manner.

<p>False (B)</p> Signup and view all the answers

Match the following HDFS components with their functions:

<p>Name Node = Master node that tracks file locations Data Node = Slave node that stores data blocks Map Phase = Splits the task into sub-tasks Reduce Phase = Aggregates results from the map phase</p> Signup and view all the answers

What is the role of the Data Node in Hadoop?

<p>Store data blocks and retrieve data as required</p> Signup and view all the answers

What is YARN primarily used for?

<p>Managing resources in a cluster (C)</p> Signup and view all the answers

HBase is a row-based NoSQL database.

<p>False (B)</p> Signup and view all the answers

What components make up Pig?

<p>Pig Latin and Pig Engine</p> Signup and view all the answers

The output of the map phase in YARN is a __________.

<p>key-value pair</p> Signup and view all the answers

Match the data processing engines with their functionalities:

<p>Batch Processing = Processes large volumes of data on a scheduled basis Stream Processing = Processes data in real time as it is ingested Interactive Processing = Provides immediate responses to queries Graph Processing = Analyzes relationships and connections within data</p> Signup and view all the answers

What scripting language is used by Pig?

<p>Pig Latin (A)</p> Signup and view all the answers

Hive was developed by Google.

<p>False (B)</p> Signup and view all the answers

What is the main purpose of Sqoop?

<p>To transfer data between relational databases and Hadoop ecosystem</p> Signup and view all the answers

Which of the following best describes Sqoop's primary function?

<p>Bringing data from Relational Databases into HDFS (B)</p> Signup and view all the answers

Flume can only collect data in batch mode.

<p>False (B)</p> Signup and view all the answers

What querying language does Sqoop use internally?

<p>Hive Querying Language (HQL)</p> Signup and view all the answers

Oozie is a workflow __________ system used in Hadoop.

<p>scheduler</p> Signup and view all the answers

Which of the following is NOT a function of Kafka?

<p>Managing job execution workflows (D)</p> Signup and view all the answers

Zookeeper is primarily used for data collection in Hadoop.

<p>False (B)</p> Signup and view all the answers

What is the advantage of using Sqoop for programmers?

<p>It allows writing MapReduce functions using simple HQL queries.</p> Signup and view all the answers

Which of the following describes a significant advantage of Spark over MapReduce?

<p>Offers in-memory processing for faster performance (C)</p> Signup and view all the answers

Spark SQL API is used specifically for querying unstructured data.

<p>False (B)</p> Signup and view all the answers

What is the primary role of Spark Core in the Spark Ecosystem?

<p>Execution engine</p> Signup and view all the answers

Spark has its own Ecosystem built on ________.

<p>Scala</p> Signup and view all the answers

Match the following Spark components with their descriptions:

<p>MLlib = Machine learning library for data science tasks GraphX = Graph computation engine for graph-structured data Streaming API = Handles real-time data processing Spark SQL = APIs for querying structured data</p> Signup and view all the answers

Which data sources can the Streaming API easily integrate with?

<p>Flume, Kafka, and Twitter (B)</p> Signup and view all the answers

Spark is solely designed for batch processing similar to Hadoop.

<p>False (B)</p> Signup and view all the answers

Name a challenge associated with understanding the Hadoop ecosystem as mentioned in the content.

<p>Intimidation and complexity of components</p> Signup and view all the answers

Flashcards

Big Data

A massive amount of data generated rapidly and in various formats.

Traditional Systems

Outdated systems designed for structured data; not optimal for handling big data.

Hadoop Ecosystem

A complex framework of components designed to handle big data.

Semi-structured data

Data with some defined structure, but less structured than relational tables.

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Unstructured data

Data lacking a pre-defined format; often needs special processing.

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Vertical Scalability

Increasing system resources (memory, processing power) to handle more data.

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Data Silos

Separate stores of data that are not easily combined.

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Hadoop

A framework for storing and processing large datasets.

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Distributed file system

A file system that splits data and stores it across multiple computers.

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Google File System (GFS)

A distributed file system developed at Google to manage large amounts of data.

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Scalability

The ability of a system to handle increasing amounts of data.

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Commodity hardware

Inexpensive computer hardware.

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Data locality

Processing data on the same computer where it is stored.

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Distributed environment

A system where work is divided across multiple computers.

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YARN

A resource manager that schedules and manages applications on a Hadoop cluster, allowing for efficient data processing.

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Map Phase

The initial processing step in Hadoop, where data is divided into splits and processed in parallel by individual Map tasks.

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Reduce Phase

The final processing stage in Hadoop, where the output from Map tasks is aggregated, summarized, and stored in HDFS.

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HBase

A column-oriented NoSQL database designed for handling massive amounts of data with fast read and write operations.

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Pig

A scripting language that simplifies data analysis on Hadoop by simplifying the process of writing MapReduce jobs.

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Pig Latin

The scripting language used in Pig, providing a SQL-like syntax for expressing data analysis logic.

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Hive

A distributed data warehouse system built on top of Hadoop, allowing for easy querying and analysis of large datasets.

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Horizontal Scaling

Adding more machines (nodes) to a system to handle increased data and processing demands.

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Fault Tolerance

The ability of a system to continue operating even if parts fail.

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What is Hadoop's advantage over traditional systems in terms of scalability?

Hadoop efficiently scales horizontally by adding nodes, unlike traditional systems which rely on vertical scaling, which can be costly and inefficient.

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What is Hadoop's advantage over traditional systems in terms of data types?

Hadoop can handle structured, semi-structured, and unstructured data, unlike traditional RDBMS systems which are limited to structured data.

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HDFS: Name Node

The central control point in HDFS, responsible for managing the locations of data blocks across the cluster.

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HDFS: Data Node

A server in HDFS that stores data blocks and communicates with the Name Node.

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MapReduce Algorithm

A core component of Hadoop that efficiently processes large datasets by dividing tasks into smaller, parallel processes.

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MapReduce: Map Phase

The first phase of MapReduce, where data is transformed into key-value pairs, which are then distributed to different nodes.

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Sqoop's Role

Sqoop is a tool used to efficiently transfer data between relational databases (like MySQL, Postgres) and Hadoop's Distributed File System (HDFS).

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Hive Querying Language (HQL)

HQL is a SQL-like language used by Sqoop to interact with data in Hadoop. It allows programmers to easily write MapReduce jobs using simple HQL queries.

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Flume's Function

Flume is a service designed for collecting, aggregating, and moving large amounts of data from various sources into Hadoop's HDFS.

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Flume's Data Collection

Flume can collect data both in real-time (as it's generated) and in batches (collecting data periodically).

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Kafka's Purpose

Kafka is a system that sits between applications producing data (Producers) and applications consuming data (Consumers), allowing efficient data flow and communication.

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ZooKeeper's Role

ZooKeeper provides coordination and synchronization in a Hadoop cluster, helping to manage and organize the different nodes within the system.

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Oozie's Scheduling

Oozie is a workflow scheduler used to schedule and execute jobs written in various Hadoop components like MapReduce, Hive, and Pig.

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Oozie's Workflow

Oozie can create pipelines of individual jobs, allowing them to be executed sequentially or in parallel to complete larger tasks.

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Spark's Edge

Spark processes data in memory, making it significantly faster than traditional Hadoop MapReduce, which relies on disk-based processing.

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Spark's Versatility

Spark supports various programming languages like Java, Python, Scala, and R, making it adaptable to diverse data science and processing needs.

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Spark's Real-time Power

Beyond batch processing, Spark excels at real-time data analysis, enabling applications to handle live streams of data.

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Spark SQL: Structured Data

Spark SQL allows users to query structured data organized in dataframes and Hive tables, enabling seamless integration with relational databases.

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Spark Streaming: Real-time Insight

The Spark Streaming API empowers real-time processing of data from various sources like Flume, Kafka, and Twitter, allowing for immediate insights.

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MLlib: Machine Learning Powerhouse

Spark MLlib is a powerful library for scalable machine learning tasks, leveraging the distributed computing capabilities of Spark.

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GraphX: Graph Analytics Engine

Spark GraphX provides a framework for analyzing graph-structured data, enabling operations like finding connected components and shortest paths.

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Spark Ecosystem: A Unified Platform

With its core engine, Spark SQL, Streaming, MLlib, and GraphX, Spark offers a comprehensive ecosystem for various data processing and analysis needs.

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Study Notes

Introduction to Hadoop Ecosystem

  • Big data is the massive amount of data generated rapidly in various formats.
  • Traditional systems are not suitable for storing and handling big data.
  • Hadoop is a complex framework of multiple components needed to manage big data.
  • Hadoop's components work together to overcome limitations of traditional systems.
  • Understanding each component within Hadoop can be challenging.

Problems with Traditional Systems

  • Today's data is often semi-structured or unstructured.
  • Traditional systems are designed for structured data (rows and columns).
  • Vertically scaling traditional systems is expensive (adding more processing, memory, and storage).
  • Data is often stored in silos, making pattern analysis difficult.

Solution: Hadoop

  • Hadoop addresses the drawbacks of traditional systems.
  • Google File System (GFS) was a precursor that inspired Hadoop.
  • Hadoop is an open-source framework.
  • Data is stored and processed in a distributed environment across multiple machines.

Hadoop Properties

  • Highly scalable, handling data in a distributed manner.
  • Horizontal scaling instead of vertical scaling (cost-effective).
  • Fault-tolerant through data replication.
  • Economical, using commodity hardware.
  • Data locality (data processed where it's stored).

Hadoop Ecosystem Components

  • HDFS (Hadoop Distributed File System): Stores data as files.
  • Files are split into blocks (128MB) and distributed across cluster machines.
  • Master-slave architecture (NameNode and DataNode).

MapReduce

  • Hadoop's core algorithm for handling big data.
  • Divides a large task into smaller tasks.
  • Distributes tasks across multiple machines.
  • Processes tasks in parallel.
  • Map and Reduce phases form the basis of MapReduce, a distributed computing model.

YARN (Yet Another Resource Negotiator)

  • Manages resources in a Hadoop cluster.
  • Enables various data processing engines.
  • Increases efficiency by allowing multiple applications to use cluster resources efficiently.

Other Ecosystem Components

  • HBase: Column-based NoSQL database, optimized for high volume data and real-time interactions
  • Hive: Distributed data warehouse system with a SQL-like query language.
  • Pig: Data analysis tool that allows writing scripts to perform data transformations in a simplified way that translates to MapReduce jobs.
  • Sqoop: Transfers data between relational databases and Hadoop.
  • Flume: Collects, aggregates, and moves large amounts of data into Hadoop.
  • Kafka: Facilitates the flow of data from data producers to consumers.
  • Oozie: A workflow scheduler system.
  • ZooKeeper: Distributed synchronization service.

Spark

  • Alternative framework to Hadoop.
  • In-memory processing for faster performance.
  • Supports real-time processing in addition to batch processing.
  • Several ecosystem components build on Spark core such as Spark SQL, streaming APIs, MLlib, and GraphX.

Big Data Processing Stages

  • Ingestion (collecting and loading data)
  • Storage (storing data in HDFS or other systems)
  • Processing (transforming and cleaning data)
  • Analysis (extracting knowledge/insights from data)

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This quiz covers the fundamental concepts of the Hadoop ecosystem, including its components, advantages over traditional systems, and how it addresses the challenges of managing big data. Gain insights into how Hadoop enables effective storage and processing of large volumes of data.

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