Big Data - 2

Big Data - 2

Exploring Hadoop: Core Components, Ecosystem, and More

Introduction to Hadoop:


  • Hadoop is an open-source framework designed to process and store large volumes of data, commonly known as Big Data.

  • It provides a set of tools and libraries that allow distributed processing of data across clusters of computers.

  • Hadoop enables organizations to harness the power of distributed computing to analyze and extract valuable insights from vast datasets.

Role in Processing Big Data:

  • Hadoop plays a crucial role in handling the challenges posed by Big Data, including the three Vs: Volume, Velocity, and Variety.

  • Volume: Hadoop's distributed storage and processing capabilities make it possible to handle massive datasets that exceed the capacity of a single machine.

  • Velocity: Hadoop's ability to process data in parallel across multiple nodes enables real-time or batch processing of streaming data.

  • Variety: Hadoop's flexible architecture accommodates various data types, including structured, semi-structured, and unstructured data.

Distributed Computing Paradigm:

  • Hadoop employs a distributed computing model, where tasks are divided into smaller subtasks and executed in parallel across a cluster of machines.

  • This approach greatly accelerates processing times, as tasks are processed concurrently rather than sequentially.

  • The MapReduce programming model is a core component of Hadoop's distributed paradigm, facilitating parallel computation.


  • Hadoop's architecture is designed to scale horizontally, meaning that new machines can be added to the cluster to handle increased workloads.

  • As data volumes grow, organizations can expand their Hadoop clusters by simply adding more nodes, making it highly scalable.

  • Horizontal scaling allows for better utilization of resources and improved performance without requiring significant modifications to the system.

Fault Tolerance:

  • Fault tolerance is a crucial aspect of Hadoop's design, ensuring that the system continues to function in the presence of hardware or software failures.

  • Hadoop achieves fault tolerance in several ways, including data replication and automatic recovery mechanisms.

  • In Hadoop's Distributed File System (HDFS), data is stored in multiple replicas across different nodes to prevent data loss in case of hardware failures.

Core Hadoop Components:

Hadoop Distributed File System (HDFS)

The architecture of HDFS:

  • Hadoop Distributed File System (HDFS) is the primary storage system used by Hadoop to manage and store large datasets across a cluster.

  • HDFS follows a master-slave architecture consisting of two main components: NameNode and DataNode.


  • The NameNode is the master node responsible for managing the file system's metadata.

  • It stores information about the file structure, permissions, and the location of data blocks on DataNodes.

  • The NameNode maintains the namespace and serves clients' requests for file operations.


  • DataNodes are slave nodes that store the actual data blocks of files.

  • They manage data storage, retrieval, and replication based on instructions from the NameNode.

  • DataNodes report their health and status to the NameNode and perform data block replication for fault tolerance.

Data Replication for Fault Tolerance:

  • HDFS employs data replication to ensure fault tolerance in the face of hardware failures or data corruption.

  • Each data block is replicated across multiple DataNodes, typically three replicas by default.

  • If a DataNode becomes unavailable, the replicas can still be accessed from other nodes, ensuring data availability.


Explanation of the MapReduce Programming Model:

  • MapReduce is a programming model and processing framework that simplifies the parallel processing of large datasets in Hadoop.

  • It abstracts the complexity of distributed computing by breaking down tasks into two main phases: Map and Reduce.

Phases of MapReduce:

  1. Map Phase:

    • In the Map phase, input data is divided into smaller chunks, and each chunk is processed independently by a Map task.

    • The Map function takes input data, applies a transformation, and produces a set of intermediate key-value pairs.

    • The output of the Map phase is sorted and grouped by key to prepare for the Shuffle phase.

  2. Shuffle Phase:

    • The Shuffle phase involves sorting and shuffling the intermediate key-value pairs produced by the Map phase.

    • This process groups together all values associated with a particular key and prepares them for the Reduce phase.

  3. Reduce Phase:

    • In the Reduce phase, the grouped and sorted intermediate data is passed to Reduce tasks.

    • The Reduce function performs aggregation or summarization on the grouped data and produces the final output.

MapReduce's Role in Distributed Data Processing:

  • MapReduce enables efficient distributed processing of large datasets across a cluster of machines.

  • It automatically handles parallelization, data distribution, and fault tolerance, freeing developers from managing these aspects.

  • MapReduce abstracts complex distributed algorithms, making it easier to develop scalable data processing applications.

Hadoop Ecosystem:

  • The Hadoop ecosystem is a collection of open-source tools and frameworks that extend the capabilities of the core Hadoop platform.

  • These tools address various aspects of data processing, storage, analysis, and management within the Hadoop environment.

  • The ecosystem components provide specialized functionalities to cater to different data processing requirements.

HBase for NoSQL Database Capabilities

HBase Introduction:

  • HBase is a distributed, scalable, and highly available NoSQL database that runs on top of the Hadoop Distributed File System (HDFS).

  • It provides real-time read and write access to large datasets and is designed for rapid data retrieval.

Key Features:

  • Schema-less design: Each row can have varying columns, allowing flexibility in data modelling.

  • High write and read throughput: Suited for applications requiring low-latency access to large datasets.

  • Automatic sharding and replication: Ensures distribution of data across the cluster for fault tolerance.

Pig for High-Level Data Processing

Pig Introduction:

  • Pig is a high-level scripting platform that simplifies data processing on Hadoop.

  • It provides a language called Pig Latin for expressing data transformations.


  • Abstraction of complex data processing tasks: Developers can express data transformations without writing low-level MapReduce code.

  • Optimization: Pig optimizes the execution of data processing tasks, improving performance.

  • Extensibility: Pig supports custom user-defined functions (UDFs) for specialized processing.

Hive for Querying and Managing Structured Data

Hive Introduction:

  • Hive is a data warehousing and SQL-like query language built on top of Hadoop.

  • It enables users to perform ad-hoc queries and analysis on structured data stored in HDFS.

Key Features:

  • Schema definition: Hive allows users to define schemas for their data, making it suitable for structured datasets.

  • HiveQL: A SQL-like query language for expressing queries, making it familiar to those experienced with relational databases.

  • Metadata management: Hive stores metadata about the data, making it easier to organize and query large datasets.

Spark for In-Memory Data Processing

Spark Introduction:

  • Apache Spark is a fast and flexible cluster computing system that offers in-memory data processing capabilities.

  • It can handle batch processing as well as real-time data streaming and machine learning tasks.


  • In-memory processing: Spark stores intermediate data in memory, significantly speeding up computations.

  • Versatility: Supports various programming languages (Scala, Java, Python, R) and libraries for different tasks.

  • Unified processing: Spark provides a unified platform for batch processing, interactive queries, streaming, and machine learning.

YARN for Resource Management

YARN Introduction:

  • Yet Another Resource Negotiator (YARN) is the resource management layer of Hadoop.

  • It separates the resource management and job scheduling aspects from the core Hadoop components.


  • Resource allocation: YARN allocates resources to different applications running on the cluster.

  • Scalability: Enables multi-tenancy by managing resources efficiently among different users and applications.

  • Flexibility: Supports various processing frameworks beyond MapReduce, making the cluster more versatile.

Hive Physical Architecture:

Overview of Hive's Architecture for Data Warehousing and Querying

Hive Architecture Overview:

  • Hive is a data warehousing and querying tool built on top of Hadoop, designed to provide a familiar SQL-like interface for data processing on Hadoop clusters.

  • Hive enables users to write queries that are then translated into underlying Hadoop jobs for data processing.

Components of Hive:

  1. Hive Clients: Users interact with Hive through command-line tools or graphical interfaces.

  2. Hive Server: Manages communication between clients and the Hive Metastore.

  3. Hive Metastore: Stores metadata about Hive tables, partitions, columns, and more.

  4. Execution Engine: Translates HiveQL queries into MapReduce or Tez jobs for execution on Hadoop clusters.

Hive Metastore and Its Role in Storing Metadata

Hive Metastore:

  • The Hive Metastore is a central repository that stores metadata about Hive tables, databases, columns, partitions, and their relationships.

  • It separates the metadata from the actual data stored in HDFS, enabling better management and query optimization.

Role in Storing Metadata:

  • Hive Metastore maintains information about the structure and schema of tables, making it easier to manage and query structured data.

  • It stores information about data locations, data types, and partitioning strategies.

  • This separation of metadata allows users to perform queries and analysis on the schema without necessarily accessing the data itself.

Transformation of HiveQL Queries to MapReduce/Tez Jobs

HiveQL to Hadoop Job Conversion:

  • HiveQL is a SQL-like query language used in Hive for expressing data manipulation and retrieval operations.

  • When a user submits a HiveQL query, Hive translates it into a sequence of MapReduce or Tez jobs for execution.

Query Compilation and Execution:

  • The Hive Query Compiler translates the HiveQL query into a series of stages and tasks that correspond to MapReduce or Tez jobs.

  • Each stage includes transformations like filtering, aggregation, and sorting.

  • The query execution engine then orchestrates the execution of these stages on the Hadoop cluster.

MapReduce vs. Tez:

  • In earlier versions of Hive, queries were primarily executed as MapReduce jobs.

  • Tez is an alternative execution engine that offers performance improvements by optimizing the execution plan and reducing the overhead of multiple MapReduce jobs.

  • Hive can choose between MapReduce and Tez based on query characteristics and the execution engine's availability.

Hadoop Limitations and RDBMS Versus Hadoop:

Hadoop Limitations

Real-Time Processing Challenges:

  • Hadoop's primary design is for batch processing of large volumes of data.

  • Real-time data processing can be challenging due to the inherent latency in writing, replicating, and processing data across distributed nodes.

  • While improvements have been made with technologies like Spark and Flink, Hadoop's architecture may not be the best fit for real-time processing compared to specialized streaming frameworks.

Complexity of Programming:

  • Developing applications on Hadoop often requires writing MapReduce jobs or using higher-level frameworks like Spark.

  • This can be more complex than traditional programming languages for certain tasks, leading to a steeper learning curve.

Lack of Built-in Security:

  • Hadoop's early versions lacked robust built-in security features.

  • Though later versions have incorporated security enhancements, ensuring data privacy and access control can still be complex.

Scalability and Hardware Costs:

  • While Hadoop's horizontal scalability is a strength, it also presents challenges.

  • As the cluster grows, managing and maintaining a large number of nodes can become complex and expensive in terms of hardware and operational costs.

Comparison between Hadoop and Traditional RDBMS

AspectHadoopTraditional RDBMS
Data StructureDesigned for unstructured or semi-structured dataStructured data with defined schemas
ScalabilityScales horizontally by adding more hardware to the clusterScales vertically by adding resources to a server
Processing ParadigmBatch processing for complex transformations and analysisReal-time transaction processing with ACID properties
Schema FlexibilitySupports schema-on-read, flexible data structuring at query timeRequires a predefined schema at data insertion
Data VolumeDesigned for handling massive data volumes beyond traditional DB capacityGenerally suited for smaller datasets
Complex QueriesWell-suited for complex analytical queries involving large dataBetter for simpler to moderately complex queries and transactions

Hadoop Distributed File System (HDFS):

Exploration of HDFS Architecture

HDFS Overview:

  • Hadoop Distributed File System (HDFS) is a fundamental component of the Hadoop ecosystem, designed to store and manage vast amounts of data across a distributed cluster.

  • HDFS breaks down large files into smaller blocks and distributes them across multiple nodes in the cluster.

HDFS Architecture:

  • HDFS follows a master-slave architecture, consisting of two main components: NameNode (master) and DataNodes (slaves).

  • The NameNode manages metadata and keeps track of the location of data blocks on DataNodes.

Explanation of Block Storage, Replication, and Fault Tolerance Mechanisms

Block Storage:

  • HDFS divides files into fixed-size blocks (e.g., 128MB or 256MB) for efficient storage and distribution.

  • Each block is stored as a separate file on the underlying file system of the DataNodes.

  • This block-based approach allows for better utilization of storage capacity and efficient data transfer.


  • HDFS replicates data blocks to ensure fault tolerance and data availability.

  • By default, each data block is replicated three times across different DataNodes.

  • Replicas are stored on separate racks to minimize the impact of rack-level failures.

Fault Tolerance Mechanisms:

  1. Data Replication:

    • Replicating data blocks across multiple nodes ensures that data is still available even if one or more nodes fail.

    • If a DataNode becomes unavailable, the replicas can still be accessed from other nodes.

    • The NameNode keeps track of block locations and the number of replicas.

  2. Heartbeat and Block Report:

    • DataNodes periodically send heartbeat signals to the NameNode to indicate their availability.

    • DataNodes also provide block reports to inform the NameNode about the blocks they store.

  3. Data Recovery:

    • If a DataNode fails or becomes unresponsive, the NameNode identifies missing or under-replicated blocks.

    • The NameNode schedules the replication of these blocks to other nodes to restore the desired replication factor.

  4. Safe Mode:

    • The NameNode enters safe mode during startup to ensure data consistency.

    • While in safe mode, the NameNode does not allow any modifications to the file system, ensuring that the system stabilizes before accepting write requests.

Processing Data with Hadoop:

Utilizing MapReduce for Processing Large Datasets

MapReduce Overview:

  • MapReduce is a programming model and processing framework designed to handle large-scale data processing tasks in a distributed manner.

  • It breaks down complex computations into simpler map and reduces tasks that can be executed across a cluster of machines.

Advantages of MapReduce:

  • Scalability: MapReduce can efficiently process massive datasets by distributing tasks across multiple nodes.

  • Fault Tolerance: It automatically handles node failures and ensures task completion by rerunning failed tasks on other nodes.

  • Parallel Processing: Map and reduce tasks are processed in parallel, speeding up data processing.

Steps Involved in Writing and Running a MapReduce Job

1. Data Input:

  • The first step involves providing input data stored in HDFS, which is then divided into splits for processing.

  • Each input split is assigned to a map task for processing.

2. Map Phase:

  • In the map phase, the input data is processed by a map function.

  • The map function takes key-value pairs as input and produces intermediate key-value pairs as output.

  • The output of the map phase is shuffled and sorted by key before moving to the reduce phase.

3. Shuffle and Sort Phase:

  • The shuffle and sort phase groups intermediate key-value pairs by their keys.

  • Data with the same key is moved to the same reducer to prepare for aggregation in the reduce phase.

4. Reduce Phase:

  • In the reduce phase, the aggregated intermediate data is processed by a reduce function.

  • The reduce function takes a key and its associated list of values and produces the final output.

5. Data Output:

  • The output of the reduce phase is stored in HDFS as the final processed data.

Writing and Running a MapReduce Job:

  1. Writing the Map and Reduce Functions:

    • Developers write custom map and reduce functions tailored to the specific data processing task.

    • The map function applies transformations to the input data and emits intermediate key-value pairs.

    • The reduce function processes grouped intermediate data and produces the final output.

  2. Configuring the Job:

    • Developers configure the job using Hadoop's MapReduce API.

    • This includes specifying the input and output paths, the map and reduce classes, and other job-specific settings.

  3. Submitting the Job:

    • The configured job is submitted to the Hadoop cluster for execution.

    • The Hadoop cluster's resource manager (YARN) schedules tasks and manages their execution.

  4. Monitoring and Debugging:

    • Developers monitor the progress of the job using Hadoop's web interface.

    • If errors occur, logs are available for debugging and troubleshooting.

Managing Resources and Applications with Hadoop YARN:

Introduction to Hadoop YARN (Yet Another Resource Negotiator)

YARN Overview:

  • Hadoop YARN (Yet Another Resource Negotiator) is a resource management layer introduced in Hadoop 2.0.

  • It decouples resource management and job scheduling from the MapReduce programming model, making Hadoop more versatile.

YARN's Role in Managing Resources and Applications in a Hadoop Cluster

Resource Management:

  • YARN is responsible for managing and allocating resources across nodes in a Hadoop cluster.

  • It ensures that applications receive the necessary computational resources (CPU, memory) to execute efficiently.

Job Scheduling:

  • YARN schedules different types of applications, not just MapReduce jobs. It supports various processing frameworks like Spark, Tez, and others.

  • By separating resource management from application logic, YARN provides better flexibility and efficient resource utilization.


  • YARN enables multi-tenancy by allowing multiple applications to share the same cluster while isolating their resource usage.

  • This ensures the fair allocation of resources among different users and applications.

ApplicationMaster and NodeManager Functionalities


  • Each application submitted to YARN has its own ApplicationMaster.

  • The ApplicationMaster is responsible for negotiating resources with the ResourceManager, coordinating task execution, and handling failures.

  • It monitors the progress of tasks, requests additional resources if needed, and reports the status back to the ResourceManager.


  • NodeManager is responsible for managing resources on individual nodes in the cluster.

  • It tracks the resource availability, monitors node health, and reports it to the ResourceManager.

  • NodeManager launches and monitors containers, which are isolated environments where application tasks run.


  • A container is a fundamental unit of resource allocation and isolation in YARN.

  • It encapsulates CPU, memory, and other resources needed for executing a specific task of an application.

  • Containers provide a controlled environment that ensures fair resource usage and fault isolation.

Application Lifecycle:

  1. Application Submission: An application is submitted to the ResourceManager.

  2. Resource Allocation: The ResourceManager allocates resources based on availability and constraints.

  3. ApplicationMaster Allocation: The ResourceManager allocates resources for the ApplicationMaster of the application.

  4. Task Execution: The ApplicationMaster negotiates resources for tasks and monitors their execution on NodeManagers.

  5. Progress Tracking: The ApplicationMaster updates the ResourceManager on task progress.

  6. Resource Deallocation: Once the application completes, resources are released for other applications.

MapReduce Programming:

Overview of the MapReduce Programming Model

MapReduce Model:

  • MapReduce is a programming model for processing and generating large datasets in a parallel and distributed manner.

  • It involves dividing tasks into two main phases: the Map phase and the Reduce phase.

Map Phase:

  • Input data is split into chunks, and each chunk is processed independently by a map function.

  • The map function takes key-value pairs from the input data and produces intermediate key-value pairs.

Shuffle and Sort Phase:

  • Intermediate key-value pairs from the map phase are grouped and sorted by their keys.

  • Data with the same key is moved to the same reducer for aggregation in the reduce phase.

Reduce Phase:

  • The grouped and sorted data is processed by a reduce function.

  • The reduce function takes a key and its associated values, then performs aggregation or summarization.

Writing Map and Reduce Functions for Data Processing

Map Function:

  • The map function processes each input record and extracts relevant information.

  • It emits intermediate key-value pairs, where the key represents a category or identifier, and the value is the processed data.

Reduce Function:

  • The reduce function takes a key and a list of associated values.

  • It performs aggregation, computation, or transformation on the values to produce the final output.

Understanding Key-Value Pairs and the MapReduce Workflow

Key-Value Pairs:

  • Key-value pairs are the fundamental data structure in MapReduce.

  • The key is used to group related data together during the shuffle and sort phase.

  • The value contains the data associated with the key.

MapReduce Workflow:

  1. Input Data: The input data is divided into chunks, and each chunk is assigned to a map task.

  2. Map Phase: The map function processes the input data, producing intermediate key-value pairs.

  3. Shuffle and Sort: Intermediate data is shuffled and sorted by key to prepare for the reduce phase.

  4. Reduce Phase: The reduce function aggregates or processes the grouped data, producing the final output.

  5. Output Data: The output data is stored in HDFS or another storage location.

MapReduce Example: Word Count

Map Phase:

  • Map function reads a document and emits key-value pairs where the key is a word and the value is 1.

Shuffle and Sort:

  • Intermediate data is sorted by word.

Reduce Phase:

  • Reduce function sums up the counts of the same words, providing the word count for each word.