Everand Logo

Welcome to Everand!

  • Discover millions of ebooks, audiobooks, and so much more with a free trial

    From $11.99/month after trial. Cancel anytime.

    Big Data Frameworks
    Big Data Frameworks
    Big Data Frameworks
    Ebook146 pages1 hour

    Big Data Frameworks

    By Mark Jackson

    ()

    Read preview

    About this ebook

    Big Data Frameworks: Architectures, Tools, and Techniques for Managing Large-Scale Data. Comprehensive review of Apache Storm, Samza, Google BigQuery, Amazon Redshift, Azure Synapse and more offers a comprehensive exploration of the fundamental concepts and cutting-edge technologies essential for handling vast and complex data environments. This book serves as an essential guide for data engineers, architects, and analysts who seek to understand and leverage the power of big data frameworks in today's data-driven world.

    Big Data Frameworks is written in a clear and accessible style, making complex concepts understandable and actionable. Whether you are a seasoned professional or new to the field, this book provides the knowledge and tools needed to effectively manage and leverage large-scale data for strategic decision-making and innovation.

    Unlock the potential of big data with this essential guide and transform your approach to managing and analyzing large datasets.

     

    LanguageEnglish
    PublisherMjPublishing4972
    Release dateNov 25, 2024
    ISBN9798227890641
    Big Data Frameworks

    Read more from Mark Jackson

    Related to Big Data Frameworks

    Related ebooks

    Data Modeling & Design For You

    View More
    View More

    Related podcast episodes

    Related articles

    • UNLIMITED

      Facilities Systems

      Oct 21, 2018

      Facilities Systems
      5 min read

    • UNLIMITED

      What is ELT?

      Apr 1, 2021

      It stands for extract, load, and transform- the processes a data pipeline uses for replicating the data from a source system into a target system such as a cloud data warehouse. 1. Extraction is the first step in which data is copied from the source

      6 min read

    • UNLIMITED

      Network monitoring 2022

      Feb 10, 2022

      Network monitoring 2022
      4 min read

    • UNLIMITED

      Leighton Wolffe

      Mar 20, 2020

      The cannabis industry has plenty of data floating around, but how much is put to use? As with most big data, it’s desperately underutilized. Lighting, irrigation, and HVAC systems could be transmitting information about crop health twenty-four hours

      4 min read

    • UNLIMITED

      Real World Computing

      May 11, 2023

      Migrating to Azure isn’t necessarily the toughest part of a successful cloud migration, explains our guest columnist Many organisations succeed at deploying resources in or migrating to Microsoft Azure. But many of those same organisations fail to en

      6 min read

    • UNLIMITED

      Business protection 2024

      Aug 8, 2024

      Business protection 2024
      3 min read

    • UNLIMITED

      Monitor And Graph Your System Metrics

      Dec 13, 2022

      Credit: https://oss.oetiker.ch/rrdtool Matt Holder has worked in IT support for over a decade, and always tries to use Linux alongside other installed systems. The code used in this article can be downloaded from https:// github.com/ mattmole/ LXF297

      8 min read

    • UNLIMITED

      Extending The Time Equation

      Jul 26, 2021

      Extending The Time Equation
      4 min read

    • UNLIMITED

      The Future Is All Quantum

      Oct 1, 2021

      The Future Is All Quantum
      2 min read

    • UNLIMITED

      The Virtual Garage

      Aug 6, 2021

      The Virtual Garage
      11 min read

    • UNLIMITED

      ‘Digital Twin’ Can Make Wireless Networks Better

      Jul 24, 2024

      Researchers have developed a new method for predicting what data wireless computing users will need before they need it, making wireless networks faster and more reliable. The new method makes use of a technique called a “digital twin,” which effecti

      2 min read

    • UNLIMITED

      Herd In The Cloud

      Sep 21, 2021

      Matt Yonkovit is Percona’s Head of Open Source Strategy and a member of SHA (Silly Hats Anonymous). “Going ‘cloud native’ involves building applications in new ways. Traditional applications are generally designed with a two- or three-tier architectu

      1 min read

    • UNLIMITED

      Sophos Intercept X Advanced

      Nov 11, 2021

      Sophos Intercept X Advanced
      2 min read

    • UNLIMITED

      Mission Center

      Oct 17, 2023

      Mission Center
      1 min read

    • UNLIMITED

      Advanced Awareness

      Dec 5, 2023

      Advanced Awareness
      9 min read

    Related categories

    Reviews for Big Data Frameworks

    0 ratings

    0 ratings0 reviews

    What did you think?

    Tap to rate

    Review must be at least 10 words

      Book preview

      Big Data Frameworks - Mark Jackson

      Chapter 1: Apache Storm

      Introduction to Storm

      Apache Storm is a distributed real-time computation system designed to process large volumes of data streams with low latency. Initially developed by Nathan Marz and his team at BackType, it became an Apache project in 2014 and has since been widely adopted for real-time data processing tasks. Storm’s primary strength lies in its ability to handle high-throughput data streams and perform complex computations on-the-fly, making it a powerful tool for applications requiring immediate insights from data.

      At its core, Apache Storm follows a simple but effective architecture composed of several key components: Spouts, Bolts, and Topologies. Spouts are responsible for ingesting data into the Storm system, typically from various sources such as message queues or data streams. Once data is ingested, Bolts perform various operations on this data, such as filtering, aggregating, or enriching it. Topologies define the overall data processing workflow by specifying how data flows between Spouts and Bolts and how these components interact.

      Storm is designed for fault tolerance and scalability. It ensures that data is processed reliably even in the face of failures. This is achieved through its concept of acknowledgements and retries. When a tuple of data is processed, Storm tracks its progress and ensures that if any failure occurs, the data is reprocessed from the point of failure, maintaining data consistency. The system is also highly scalable, allowing users to add more nodes to the cluster to handle increased data loads without significant changes to the application.

      One of Storm’s key features is its ability to guarantee the processing of data with minimal latency, which is crucial for applications like real-time analytics, monitoring, and data-driven decision-making. Its design allows it to handle continuous data streams, making it suitable for scenarios such as social media analytics, network security monitoring, and live metrics tracking. Although newer technologies like Apache Flink and Apache Kafka Streams have emerged, Storm remains relevant for specific use cases where low-latency processing of unbounded data streams is required.

      Apache Storm offers a robust framework for real-time data processing with a focus on reliability and performance, making it a valuable tool for enterprises and developers working with real-time data streams.

      Core Components: Topologies, Bolts, Spouts

      Apache Storm is a real-time computation system that processes data streams through a set of core components: Topologies, Bolts, and Spouts. Understanding these components is crucial for designing and implementing effective Storm applications.

      Topologies

      A Topology in Apache Storm represents the entire data processing workflow. It is a directed acyclic graph (DAG) that defines how data flows through the system, specifying the sequence of operations and connections between components. A topology is composed of one or more Spouts and Bolts, and it outlines the complete computation logic required to process data. When a topology is submitted to the Storm cluster, it is distributed and executed across multiple nodes, enabling parallel and scalable processing of data streams. The topology dictates how data is ingested, processed, and emitted, ensuring that the system can handle high-throughput data efficiently.

      Spouts

      Spouts are responsible for data ingestion into the Storm system. They act as the sources of data streams and emit tuples of data into the topology. Spouts can pull data from various external sources, such as message queues (e.g., Kafka, RabbitMQ), databases, or other real-time data sources. Once data is emitted by a Spout, it becomes available for processing by Bolts. Spouts can also manage tasks such as reconnecting to data sources in case of failures and maintaining data integrity, ensuring continuous data flow into the topology.

      Bolts

      Bolts are the processing units within a topology. They receive tuples from Spouts or other Bolts, perform computations or transformations on the data, and then emit processed tuples to other Bolts or to external systems. Bolts can perform a wide range of operations, including filtering, aggregation, enrichment, and joining of data. They are designed to handle different types of processing tasks, making them versatile and adaptable to various data processing requirements. By chaining Bolts together in a topology, complex data processing pipelines can be built to achieve the desired data transformations and analyses.

      Topologies orchestrate the flow of data through the system, connecting Spouts for data ingestion and Bolts for data processing. This architecture allows Apache Storm to efficiently handle real-time data streams, providing scalable and fault-tolerant processing capabilities.

      Use Cases for Real-Time Processing

      Real-time processing has become increasingly critical across various industries as businesses seek to derive immediate insights from their data and respond swiftly to dynamic conditions. Here are some prominent use cases for real-time processing:

      1. Financial Services

      Fraud Detection: Real-time processing is essential for detecting fraudulent transactions as they occur. By analyzing transaction data in real time, financial institutions can identify unusual patterns, flag potentially fraudulent activities, and take immediate action to prevent losses.

      Algorithmic Trading: In stock and forex markets, algorithmic trading systems use real-time data to execute trades based on predefined criteria and market conditions. Real-time processing enables these systems to make split-second decisions and capitalize on market opportunities.

      2. E-Commerce

      Personalized Recommendations: E-commerce platforms use real-time processing to analyze user behavior and interactions, such as browsing history and recent purchases. This allows them to provide personalized product recommendations and offers instantly, enhancing the customer experience and driving sales.

      Dynamic Pricing: Real-time data processing helps e-commerce businesses adjust pricing dynamically based on factors such as demand, competition, and inventory levels. This allows for competitive pricing strategies and improved revenue optimization.

      3. Social Media and Advertising

      Ad Targeting and Campaign Optimization: Real-time processing enables platforms to analyze user interactions, engagement, and demographics to deliver targeted advertisements. Advertisers can adjust campaigns in real time based on performance metrics, improving ad effectiveness and ROI.

      Sentiment Analysis: Social media platforms use real-time data processing to analyze and gauge public sentiment about brands, products, or events. This helps companies understand public perception and respond to trends or issues promptly.

      4. Healthcare

      Patient Monitoring: Real-time processing is used in healthcare to monitor patient vitals and other critical data from medical devices. Immediate analysis of this data allows for prompt responses to abnormal conditions, enhancing patient care and safety.

      Emergency Response: In emergency situations, real-time data processing helps coordinate responses by analyzing incoming data from various sources, such as 911 calls and sensor networks, to prioritize resources and dispatch help effectively.

      5. IoT and Smart Cities

      Traffic Management: Real-time processing of traffic data from sensors and cameras enables smart traffic management systems to optimize traffic flow, reduce congestion, and improve safety. Real-time adjustments to traffic signals and route recommendations are possible with immediate data analysis.

      Environmental Monitoring: Smart cities use real-time data processing to monitor environmental conditions such as air quality, water levels, and weather patterns. This helps

      Enjoying the preview?
      Page 1 of 1
      pFad - Phonifier reborn

      Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

      Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


      Alternative Proxies:

      Alternative Proxy

      pFad Proxy

      pFad v3 Proxy

      pFad v4 Proxy