Scalable Big Data Architecture
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Scalable Big Data Architecture
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Author : Bahaaldine Azarmi
language : en
Publisher: Apress
Release Date : 2015-12-31
Scalable Big Data Architecture written by Bahaaldine Azarmi and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-31 with Computers categories.
This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance. Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution. When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it’s often necessary to delegate the load to Hadoop or Spark and use the No-SQLto serve processed data in real time. This book shows you how to choose a relevant combination of big data technologies available within the Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern is illustrated with practical examples, which use the different open sourceprojects such as Logstash, Spark, Kafka, and so on. Traditional data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book helps you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints imposed by dealing with the high throughput of Big data. Scalable Big Data Architecture is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a Big Data project and which tools tointegrate into that pattern.
Scalable Data Architecture With Java
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Author : Sinchan Banerjee
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-09-30
Scalable Data Architecture With Java written by Sinchan Banerjee and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-30 with Computers categories.
Orchestrate data architecting solutions using Java and related technologies to evaluate, recommend and present the most suitable solution to leadership and clients Key FeaturesLearn how to adapt to the ever-evolving data architecture technology landscapeUnderstand how to choose the best suited technology, platform, and architecture to realize effective business valueImplement effective data security and governance principlesBook Description Java architectural patterns and tools help architects to build reliable, scalable, and secure data engineering solutions that collect, manipulate, and publish data. This book will help you make the most of the architecting data solutions available with clear and actionable advice from an expert. You'll start with an overview of data architecture, exploring responsibilities of a Java data architect, and learning about various data formats, data storage, databases, and data application platforms as well as how to choose them. Next, you'll understand how to architect a batch and real-time data processing pipeline. You'll also get to grips with the various Java data processing patterns, before progressing to data security and governance. The later chapters will show you how to publish Data as a Service and how you can architect it. Finally, you'll focus on how to evaluate and recommend an architecture by developing performance benchmarks, estimations, and various decision metrics. By the end of this book, you'll be able to successfully orchestrate data architecture solutions using Java and related technologies as well as to evaluate and present the most suitable solution to your clients. What you will learnAnalyze and use the best data architecture patterns for problemsUnderstand when and how to choose Java tools for a data architectureBuild batch and real-time data engineering solutions using JavaDiscover how to apply security and governance to a solutionMeasure performance, publish benchmarks, and optimize solutionsEvaluate, choose, and present the best architectural alternativesUnderstand how to publish Data as a Service using GraphQL and a REST APIWho this book is for Data architects, aspiring data architects, Java developers and anyone who wants to develop or optimize scalable data architecture solutions using Java will find this book useful. A basic understanding of data architecture and Java programming is required to get the best from this book.
Scalable Big Data Analytics For Protein Bioinformatics
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Author : Dariusz Mrozek
language : en
Publisher: Springer
Release Date : 2018-09-25
Scalable Big Data Analytics For Protein Bioinformatics written by Dariusz Mrozek and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-25 with Computers categories.
This book presents a focus on proteins and their structures. The text describes various scalable solutions for protein structure similarity searching, carried out at main representation levels and for prediction of 3D structures of proteins. Emphasis is placed on techniques that can be used to accelerate similarity searches and protein structure modeling processes. The content of the book is divided into four parts. The first part provides background information on proteins and their representation levels, including a formal model of a 3D protein structure used in computational processes, and a brief overview of the technologies used in the solutions presented in the book. The second part of the book discusses Cloud services that are utilized in the development of scalable and reliable cloud applications for 3D protein structure similarity searching and protein structure prediction. The third part of the book shows the utilization of scalable Big Data computational frameworks, like Hadoop and Spark, in massive 3D protein structure alignments and identification of intrinsically disordered regions in protein structures. The fourth part of the book focuses on finding 3D protein structure similarities, accelerated with the use of GPUs and the use of multithreading and relational databases for efficient approximate searching on protein secondary structures. The book introduces advanced techniques and computational architectures that benefit from recent achievements in the field of computing and parallelism. Recent developments in computer science have allowed algorithms previously considered too time-consuming to now be efficiently used for applications in bioinformatics and the life sciences. Given its depth of coverage, the book will be of interest to researchers and software developers working in the fields of structural bioinformatics and biomedical databases.
Big Data In Action From Algorithms To Scalable Product Solutions 2025 Author 1 Dr Mehraj Ali Usman Ali
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Author : AUTHOR:1-Dr. Mehraj Ali Usman Ali, AUTHOR:2 -Dr. Shakeb Khan
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :
Big Data In Action From Algorithms To Scalable Product Solutions 2025 Author 1 Dr Mehraj Ali Usman Ali written by AUTHOR:1-Dr. Mehraj Ali Usman Ali, AUTHOR:2 -Dr. Shakeb Khan and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
PREFACE In an era dominated by technological advancements, the ability to extract meaningful insights from the ever-expanding volume of data has become a competitive advantage for organizations worldwide. Big Data, with its vast scope, provides companies with unprecedented opportunities to understand consumer behavior, optimize operations, and forecast future trends. Yet, despite its potential, raw data alone is insufficient; it needs to be processed, analyzed, and interpreted in a way that yields actionable insights. This is where Predictive Analytics comes into play. Predictive analytics is the practice of using historical data, machine learning algorithms, and statistical models to forecast future outcomes and trends. By leveraging Big Data, predictive analytics allows organizations to anticipate future behaviors, market shifts, and operational needs with remarkable accuracy. This predictive power is transforming industries, from retail and healthcare to finance and manufacturing, by providing businesses with tools to make data-driven decisions rather than relying solely on intuition or past experience. The goal of this book is to explore the intersection of Big Data and Predictive Analytics, providing readers with both theoretical insights and practical approaches to harnessing predictive models in Big Data environments. Throughout the chapters, we will cover the various types of predictive models, including regression analysis, time-series forecasting, decision trees, and neural networks, highlighting how these models can be applied to Big Data to solve real-world challenges. These methodologies are essential for applications ranging from demand forecasting and fraud detection to personalized marketing and healthcare diagnostics. Data preparation plays a pivotal role in predictive analytics, and this book will delve into the critical process of cleaning, transforming, and normalizing Big Data to ensure accurate and reliable predictions. Additionally, we will explore the implementation of machine learning algorithms, such as supervised and unsupervised learning, which form the backbone of many predictive models used in modern business applications. One of the core themes of this book is to demonstrate how predictive analytics is not just a tool for data scientists but a crucial component of decision support systems, helping organizations make informed choices across various departments, including marketing, operations, and finance. The book will also address the challenges that come with predictive analytics, such as data quality, overfitting, and model interpretability, providing solutions to these common obstacles. Through detailed case studies, particularly in the financial, retail, and healthcare sectors, this book highlights the transformative impact of predictive analytics in Big Data. By the end of this book, readers will not only gain an understanding of the core principles of predictive analytics but will also be equipped with the knowledge to apply these techniques in their own organizations to drive meaningful business outcomes. We hope this book serves as both an academic resource and a practical guide, empowering professionals, researchers, and students to fully leverage predictive analytics in the context of Big Data. Authors Dr. Mehraj Ali Usman Ali Dr. Shakeb Khan
Understanding Big Data Scalability
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Author : Cory Isaacson
language : en
Publisher: Prentice Hall
Release Date : 2014-07-11
Understanding Big Data Scalability written by Cory Isaacson and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-11 with Computers categories.
Get Started Scaling Your Database Infrastructure for High-Volume Big Data Applications “Understanding Big Data Scalability presents the fundamentals of scaling databases from a single node to large clusters. It provides a practical explanation of what ‘Big Data’ systems are, and fundamental issues to consider when optimizing for performance and scalability. Cory draws on many years of experience to explain issues involved in working with data sets that can no longer be handled with single, monolithic relational databases.... His approach is particularly relevant now that relational data models are making a comeback via SQL interfaces to popular NoSQL databases and Hadoop distributions.... This book should be especially useful to database practitioners new to scaling databases beyond traditional single node deployments.” —Brian O’Krafka, software architect Understanding Big Data Scalability presents a solid foundation for scaling Big Data infrastructure and helps you address each crucial factor associated with optimizing performance in scalable and dynamic Big Data clusters. Database expert Cory Isaacson offers practical, actionable insights for every technical professional who must scale a database tier for high-volume applications. Focusing on today’s most common Big Data applications, he introduces proven ways to manage unprecedented data growth from widely diverse sources and to deliver real-time processing at levels that were inconceivable until recently. Isaacson explains why databases slow down, reviews each major technique for scaling database applications, and identifies the key rules of database scalability that every architect should follow. You’ll find insights and techniques proven with all types of database engines and environments, including SQL, NoSQL, and Hadoop. Two start-to-finish case studies walk you through planning and implementation, offering specific lessons for formulating your own scalability strategy. Coverage includes Understanding the true causes of database performance degradation in today’s Big Data environments Scaling smoothly to petabyte-class databases and beyond Defining database clusters for maximum scalability and performance Integrating NoSQL or columnar databases that aren’t “drop-in” replacements for RDBMSes Scaling application components: solutions and options for each tier Recognizing when to scale your data tier—a decision with enormous consequences for your application environment Why data relationships may be even more important in non-relational databases Why virtually every database scalability implementation still relies on sharding, and how to choose the best approach How to set clear objectives for architecting high-performance Big Data implementations The Big Data Scalability Series is a comprehensive, four-part series, containing information on many facets of database performance and scalability. Understanding Big Data Scalability is the first book in the series. Learn more and join the conversation about Big Data scalability at bigdatascalability.com.
Redis Mastery Advanced Techniques For Scalable Data Architecture
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Author : Adam Jones
language : en
Publisher: Walzone Press
Release Date : 2025-01-03
Redis Mastery Advanced Techniques For Scalable Data Architecture written by Adam Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-03 with Computers categories.
Unleash the full potential of your data with "Redis Mastery: Advanced Techniques for Scalable Data Architecture," the ultimate guide to mastering Redis—an essential in-memory database that amplifies application performance. This comprehensive tome takes you beyond the basics, delving into sophisticated techniques that propel your data architecture to new heights. Explore the intricacies of Redis's robust data structures, secure and fine-tune your deployment, and tackle advanced challenges like seamless scaling and ensuring high availability. Discover diverse applications from powering real-time analytics to managing complex message queues. Whether you're a developer seeking to optimize application efficiency, a system administrator focused on resilient data stores, or a tech enthusiast eager to master cutting-edge database solutions, this book is your indispensable resource. With lucid explanations, practical demonstrations, and seasoned advice, "Redis Mastery" equips you to harness Redis's full capabilities. Revolutionize your data handling with advanced techniques that augment durability and enable effortless scalability. Elevate your data strategy and build high-performance, future-ready applications with "Redis Mastery: Advanced Techniques for Scalable Data Architecture." Redis is your gateway to crafting applications that excel under pressure and adapt seamlessly to ever-evolving demands. Secure your copy today and embark on your journey to becoming a Redis virtuoso!
Software Architecture For Big Data And The Cloud
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Author : Ivan Mistrik
language : en
Publisher: Morgan Kaufmann
Release Date : 2017-06-12
Software Architecture For Big Data And The Cloud written by Ivan Mistrik and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-12 with Computers categories.
Software Architecture for Big Data and the Cloud is designed to be a single resource that brings together research on how software architectures can solve the challenges imposed by building big data software systems. The challenges of big data on the software architecture can relate to scale, security, integrity, performance, concurrency, parallelism, and dependability, amongst others. Big data handling requires rethinking architectural solutions to meet functional and non-functional requirements related to volume, variety and velocity. The book's editors have varied and complementary backgrounds in requirements and architecture, specifically in software architectures for cloud and big data, as well as expertise in software engineering for cloud and big data. This book brings together work across different disciplines in software engineering, including work expanded from conference tracks and workshops led by the editors. - Discusses systematic and disciplined approaches to building software architectures for cloud and big data with state-of-the-art methods and techniques - Presents case studies involving enterprise, business, and government service deployment of big data applications - Shares guidance on theory, frameworks, methodologies, and architecture for cloud and big data
Big Data On Kubernetes
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Author : Neylson Crepalde
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-07-19
Big Data On Kubernetes written by Neylson Crepalde and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-19 with Computers categories.
Gain hands-on experience in building efficient and scalable big data architecture on Kubernetes, utilizing leading technologies such as Spark, Airflow, Kafka, and Trino Key Features Leverage Kubernetes in a cloud environment to integrate seamlessly with a variety of tools Explore best practices for optimizing the performance of big data pipelines Build end-to-end data pipelines and discover real-world use cases using popular tools like Spark, Airflow, and Kafka Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn today's data-driven world, organizations across different sectors need scalable and efficient solutions for processing large volumes of data. Kubernetes offers an open-source and cost-effective platform for deploying and managing big data tools and workloads, ensuring optimal resource utilization and minimizing operational overhead. If you want to master the art of building and deploying big data solutions using Kubernetes, then this book is for you. Written by an experienced data specialist, Big Data on Kubernetes takes you through the entire process of developing scalable and resilient data pipelines, with a focus on practical implementation. Starting with the basics, you’ll progress toward learning how to install Docker and run your first containerized applications. You’ll then explore Kubernetes architecture and understand its core components. This knowledge will pave the way for exploring a variety of essential tools for big data processing such as Apache Spark and Apache Airflow. You’ll also learn how to install and configure these tools on Kubernetes clusters. Throughout the book, you’ll gain hands-on experience building a complete big data stack on Kubernetes. By the end of this Kubernetes book, you’ll be equipped with the skills and knowledge you need to tackle real-world big data challenges with confidence.What you will learn Install and use Docker to run containers and build concise images Gain a deep understanding of Kubernetes architecture and its components Deploy and manage Kubernetes clusters on different cloud platforms Implement and manage data pipelines using Apache Spark and Apache Airflow Deploy and configure Apache Kafka for real-time data ingestion and processing Build and orchestrate a complete big data pipeline using open-source tools Deploy Generative AI applications on a Kubernetes-based architecture Who this book is for If you’re a data engineer, BI analyst, data team leader, data architect, or tech manager with a basic understanding of big data technologies, then this big data book is for you. Familiarity with the basics of Python programming, SQL queries, and YAML is required to understand the topics discussed in this book.
Big Data Architect S Handbook
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Author : Syed Muhammad Fahad Akhtar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-06-21
Big Data Architect S Handbook written by Syed Muhammad Fahad Akhtar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-21 with Computers categories.
A comprehensive end-to-end guide that gives hands-on practice in big data and Artificial Intelligence Key Features Learn to build and run a big data application with sample code Explore examples to implement activities that a big data architect performs Use Machine Learning and AI for structured and unstructured data Book Description The big data architects are the “masters” of data, and hold high value in today’s market. Handling big data, be it of good or bad quality, is not an easy task. The prime job for any big data architect is to build an end-to-end big data solution that integrates data from different sources and analyzes it to find useful, hidden insights. Big Data Architect’s Handbook takes you through developing a complete, end-to-end big data pipeline, which will lay the foundation for you and provide the necessary knowledge required to be an architect in big data. Right from understanding the design considerations to implementing a solid, efficient, and scalable data pipeline, this book walks you through all the essential aspects of big data. It also gives you an overview of how you can leverage the power of various big data tools such as Apache Hadoop and ElasticSearch in order to bring them together and build an efficient big data solution. By the end of this book, you will be able to build your own design system which integrates, maintains, visualizes, and monitors your data. In addition, you will have a smooth design flow in each process, putting insights in action. What you will learn Learn Hadoop Ecosystem and Apache projects Understand, compare NoSQL database and essential software architecture Cloud infrastructure design considerations for big data Explore application scenario of big data tools for daily activities Learn to analyze and visualize results to uncover valuable insights Build and run a big data application with sample code from end to end Apply Machine Learning and AI to perform big data intelligence Practice the daily activities performed by big data architects Who this book is for Big Data Architect’s Handbook is for you if you are an aspiring data professional, developer, or IT enthusiast who aims to be an all-round architect in big data. This book is your one-stop solution to enhance your knowledge and carry out easy to complex activities required to become a big data architect.
Mastering Big Data Engineering Aws Gcp Azure Showdown
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Author : Muthuraman Saminathan
language : en
Publisher: Libertatem Media Private Limited
Release Date : 2024-02-16
Mastering Big Data Engineering Aws Gcp Azure Showdown written by Muthuraman Saminathan and has been published by Libertatem Media Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-16 with Business & Economics categories.
In the rapidly evolving field of AI, operationalizing large language models (LLMs) has become a defining challenge. The LLMOps Advantage: Navigating the Future of AI is your comprehensive guide to mastering the deployment, monitoring, and scaling of LLMs in real-world applications. This book bridges the gap between model development and production, introducing readers to the specialized domain of LLMOps—a subset of MLOps tailored to the unique demands of large language models. From building scalable pipelines and optimizing inference workflows to ensuring compliance and security, this guide covers every aspect of operationalizing LLMs. Explore deployment strategies across platforms like AWS, Azure, GCP, and Hugging Face, learn about containerization and serverless architectures, and dive into tools for monitoring and observability such as Prometheus and Grafana. Through practical frameworks and case studies, the book provides actionable insights into managing performance metrics, addressing model drift, and leveraging distributed systems for scalability. Designed for data scientists, LLM engineers, and AI practitioners, The LLMOps Advantage also delves into ethical considerations, emerging trends like multi-modal models, and best practices for integrating LLMs with existing workflows. Whether you ' re fine-tuning models for specific tasks or scaling solutions to meet enterprise needs, this book equips you with the expertise to harness the full potential of LLMs. Stay ahead in the AI revolution with The LLMOps Advantage—your essential roadmap to mastering the future of large language model operations.