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Author: Vladislav Shkapenyuk
Publisher:
ISBN:
Category : Computer networks
Languages : en
Pages : 167
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Book Description
Distributed Data Stream Management Systems (DSMS) are increasingly used for the processing of high-rate data streams in real-time. An effective query optimization mechanism is a critical component that allows DSMS to deal with extreme data rates and large numbers of long-running concurrent queries. This dissertation investigates how to utilize semantic query analysis to perform query optimizations that enable scalable and robust data stream processing. We address three technical challenges faced by streaming system: (1) monitoring and correlating large number of diverse data streams with significant variations in data rates; (2) the ability to remain stable and produce correct answers even under overload conditions, and (3) supporting efficient distributed query processing to easily scale with increases in the number of processing nodes and stream data rates. First, we propose a heartbeat mechanism to prevent the DSMS from blocking when some of the monitored streams temporarily stall or slow down. By generating special punctuation messages at low-level query nodes and propagating them throughout the entire query execution plan, our heartbeat mechanism effectively unblocks all stalled query nodes. The second contribution of this dissertation addresses the problem of DSMS robustness when a load on a system increases by orders of magnitude. We introduce a query-aware sampling mechanism for guaranteeing the system's stability and the correctness of its query output under overload conditions. The mechanism is generic and supports arbitrary complex query sets. Finally, we address the problem of scalable distributed evaluation of streaming queries. The key contribution of the dissertation is a query-aware partitioning mechanism that allows us to scale the performance of the streaming queries in a close to linear fashion. We propose a query analysis framework for determining the optimal partitioning and a partition-aware distributed query optimizer that takes advantage of existing partitions. In summary, the contributions made by this dissertation in the area of streaming query optimization enable Data Stream Management Systems to scale to extreme data rates, gracefully handle overload conditions and support a large number of diverse input streams, enabling industrial-scale applications of DSMS technology.
Author: Vladislav Shkapenyuk
Publisher:
ISBN:
Category : Computer networks
Languages : en
Pages : 167
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Book Description
Distributed Data Stream Management Systems (DSMS) are increasingly used for the processing of high-rate data streams in real-time. An effective query optimization mechanism is a critical component that allows DSMS to deal with extreme data rates and large numbers of long-running concurrent queries. This dissertation investigates how to utilize semantic query analysis to perform query optimizations that enable scalable and robust data stream processing. We address three technical challenges faced by streaming system: (1) monitoring and correlating large number of diverse data streams with significant variations in data rates; (2) the ability to remain stable and produce correct answers even under overload conditions, and (3) supporting efficient distributed query processing to easily scale with increases in the number of processing nodes and stream data rates. First, we propose a heartbeat mechanism to prevent the DSMS from blocking when some of the monitored streams temporarily stall or slow down. By generating special punctuation messages at low-level query nodes and propagating them throughout the entire query execution plan, our heartbeat mechanism effectively unblocks all stalled query nodes. The second contribution of this dissertation addresses the problem of DSMS robustness when a load on a system increases by orders of magnitude. We introduce a query-aware sampling mechanism for guaranteeing the system's stability and the correctness of its query output under overload conditions. The mechanism is generic and supports arbitrary complex query sets. Finally, we address the problem of scalable distributed evaluation of streaming queries. The key contribution of the dissertation is a query-aware partitioning mechanism that allows us to scale the performance of the streaming queries in a close to linear fashion. We propose a query analysis framework for determining the optimal partitioning and a partition-aware distributed query optimizer that takes advantage of existing partitions. In summary, the contributions made by this dissertation in the area of streaming query optimization enable Data Stream Management Systems to scale to extreme data rates, gracefully handle overload conditions and support a large number of diverse input streams, enabling industrial-scale applications of DSMS technology.
Author: Michael Roberts
Publisher: Richards Education
ISBN:
Category : Computers
Languages : en
Pages : 172
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Book Description
In the ever-evolving landscape of software development, building scalable and robust systems is crucial for success. "Software Architecture Patterns: Designing Scalable and Robust Systems" is a comprehensive guide that explores the key architectural patterns used to create resilient and high-performing software. This book delves into the principles, best practices, and real-world applications of various architectural patterns, providing valuable insights for software architects, developers, and IT professionals. From microservices and event-driven architectures to domain-driven design and serverless computing, this guide offers the tools and knowledge needed to architect systems that meet the demands of modern technology. Unlock the potential of your software with proven patterns and expert guidance.
Author: Martin Kleppmann
Publisher:
ISBN:
Category : Big data
Languages : en
Pages :
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Book Description
Author: Cybellium Ltd
Publisher: Cybellium Ltd
ISBN:
Category : Computers
Languages : en
Pages : 180
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Book Description
Harness the Power of Stream Processing and Batch Data Analytics Are you ready to dive into the world of stream processing and batch data analytics with Apache Flink? "Mastering Apache Flink" is your comprehensive guide to unlocking the full potential of this cutting-edge framework for real-time data processing. Whether you're a data engineer looking to optimize data flows or a data scientist aiming to derive insights from large datasets, this book equips you with the knowledge and tools to master the art of Flink-based data processing. Key Features: 1. In-Depth Exploration of Apache Flink: Immerse yourself in the core principles of Apache Flink, understanding its architecture, components, and capabilities. Build a solid foundation that empowers you to process data in both real-time and batch modes. 2. Installation and Configuration: Master the art of installing and configuring Apache Flink on various platforms. Learn about cluster setup, resource management, and configuration tuning for optimal performance. 3. Flink Data Streams: Dive into Flink's data stream processing capabilities. Explore event time processing, windowing, and stateful computations for real-time data analysis. 4. Flink Batch Processing: Uncover the power of Flink for batch data analytics. Learn how to process large datasets using Flink's batch processing mode for efficient analysis. 5. Flink SQL: Delve into Flink's SQL and Table API. Discover how to write SQL queries and perform transformations on structured and semi-structured data for intuitive data manipulation. 6. Flink's State Management: Master Flink's state management mechanisms. Learn how to manage application state for fault tolerance and how to work with savepoints and checkpoints. 7. Complex Event Processing with CEP: Explore Flink's complex event processing capabilities. Learn how to detect patterns, anomalies, and trends in data streams for real-time insights. 8. Machine Learning with FlinkML: Embark on a journey into machine learning with FlinkML. Learn how to implement predictive analytics and machine learning algorithms for data-driven models. 9. Flink Ecosystem and Integrations: Navigate Flink's ecosystem of libraries and integrations. From data ingestion with Apache Kafka to collaborative analytics with Zeppelin, explore tools that enhance Flink's functionalities. 10. Real-World Applications: Gain insights into real-world use cases of Apache Flink across industries. From IoT data processing to fraud detection, explore how organizations leverage Flink for real-time insights. Who This Book Is For: "Mastering Apache Flink" is an indispensable resource for data engineers, analysts, and IT professionals who want to excel in stream processing and batch data analytics using Flink. Whether you're new to Flink or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of this powerful framework.
Author: Ian Gorton
Publisher: "O'Reilly Media, Inc."
ISBN: 1098106016
Category : Computers
Languages : en
Pages : 339
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Book Description
In many systems, scalability becomes the primary driver as the user base grows. Attractive features and high utility breed success, which brings more requests to handle and more data to manage. But organizations reach a tipping point when design decisions that made sense under light loads suddenly become technical debt. This practical book covers design approaches and technologies that make it possible to scale an application quickly and cost-effectively. Author Ian Gorton takes software architects and developers through the foundational principles of distributed systems. You'll explore the essential ingredients of scalable solutions, including replication, state management, load balancing, and caching. Specific chapters focus on the implications of scalability for databases, microservices, and event-based streaming systems. You will focus on: Foundations of scalable systems: Learn basic design principles of scalability, its costs, and architectural tradeoffs Designing scalable services: Dive into service design, caching, asynchronous messaging, serverless processing, and microservices Designing scalable data systems: Learn data system fundamentals, NoSQL databases, and eventual consistency versus strong consistency Designing scalable streaming systems: Explore stream processing systems and scalable event-driven processing
Author: Sreeram Nudurupati
Publisher: Packt Publishing Ltd
ISBN: 1800563094
Category : Data mining
Languages : en
Pages : 322
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Book Description
Get started with distributed computing using PySpark, a single unified framework to solve end-to-end data analytics at scale Key FeaturesDiscover how to convert huge amounts of raw data into meaningful and actionable insightsUse Spark's unified analytics engine for end-to-end analytics, from data preparation to predictive analyticsPerform data ingestion, cleansing, and integration for ML, data analytics, and data visualizationBook Description Apache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework. Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that help you gain insights faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas. By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems. What you will learnUnderstand the role of distributed computing in the world of big dataGain an appreciation for Apache Spark as the de facto go-to for big data processingScale out your data analytics process using Apache SparkBuild data pipelines using data lakes, and perform data visualization with PySpark and Spark SQLLeverage the cloud to build truly scalable and real-time data analytics applicationsExplore the applications of data science and scalable machine learning with PySparkIntegrate your clean and curated data with BI and SQL analysis toolsWho this book is for This book is for practicing data engineers, data scientists, data analysts, and data enthusiasts who are already using data analytics to explore distributed and scalable data analytics. Basic to intermediate knowledge of the disciplines of data engineering, data science, and SQL analytics is expected. General proficiency in using any programming language, especially Python, and working knowledge of performing data analytics using frameworks such as pandas and SQL will help you to get the most out of this book.
Author: Aurobindo Sarkar
Publisher: Packt Publishing Ltd
ISBN: 1785887351
Category : Computers
Languages : en
Pages : 445
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Book Description
Design, implement, and deliver successful streaming applications, machine learning pipelines and graph applications using Spark SQL API About This Book Learn about the design and implementation of streaming applications, machine learning pipelines, deep learning, and large-scale graph processing applications using Spark SQL APIs and Scala. Learn data exploration, data munging, and how to process structured and semi-structured data using real-world datasets and gain hands-on exposure to the issues and challenges of working with noisy and "dirty" real-world data. Understand design considerations for scalability and performance in web-scale Spark application architectures. Who This Book Is For If you are a developer, engineer, or an architect and want to learn how to use Apache Spark in a web-scale project, then this is the book for you. It is assumed that you have prior knowledge of SQL querying. A basic programming knowledge with Scala, Java, R, or Python is all you need to get started with this book. What You Will Learn Familiarize yourself with Spark SQL programming, including working with DataFrame/Dataset API and SQL Perform a series of hands-on exercises with different types of data sources, including CSV, JSON, Avro, MySQL, and MongoDB Perform data quality checks, data visualization, and basic statistical analysis tasks Perform data munging tasks on publically available datasets Learn how to use Spark SQL and Apache Kafka to build streaming applications Learn key performance-tuning tips and tricks in Spark SQL applications Learn key architectural components and patterns in large-scale Spark SQL applications In Detail In the past year, Apache Spark has been increasingly adopted for the development of distributed applications. Spark SQL APIs provide an optimized interface that helps developers build such applications quickly and easily. However, designing web-scale production applications using Spark SQL APIs can be a complex task. Hence, understanding the design and implementation best practices before you start your project will help you avoid these problems. This book gives an insight into the engineering practices used to design and build real-world, Spark-based applications. The book's hands-on examples will give you the required confidence to work on any future projects you encounter in Spark SQL. It starts by familiarizing you with data exploration and data munging tasks using Spark SQL and Scala. Extensive code examples will help you understand the methods used to implement typical use-cases for various types of applications. You will get a walkthrough of the key concepts and terms that are common to streaming, machine learning, and graph applications. You will also learn key performance-tuning details including Cost Based Optimization (Spark 2.2) in Spark SQL applications. Finally, you will move on to learning how such systems are architected and deployed for a successful delivery of your project. Style and approach This book is a hands-on guide to designing, building, and deploying Spark SQL-centric production applications at scale.
Author: Tomer Shiran
Publisher: "O'Reilly Media, Inc."
ISBN: 1098148584
Category : Computers
Languages : en
Pages : 352
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Book Description
Traditional data architecture patterns are severely limited. To use these patterns, you have to ETL data into each tool—a cost-prohibitive process for making warehouse features available to all of your data. The lack of flexibility with these patterns requires you to lock into a set of priority tools and formats, which creates data silos and data drift. This practical book shows you a better way. Apache Iceberg provides the capabilities, performance, scalability, and savings that fulfill the promise of an open data lakehouse. By following the lessons in this book, you'll be able to achieve interactive, batch, machine learning, and streaming analytics with this high-performance open source format. Authors Tomer Shiran, Jason Hughes, and Alex Merced from Dremio show you how to get started with Iceberg. With this book, you'll learn: The architecture of Apache Iceberg tables What happens under the hood when you perform operations on Iceberg tables How to further optimize Apache Iceberg tables for maximum performance How to use Iceberg with popular data engines such as Apache Spark, Apache Flink, and Dremio How Apache Iceberg can be used in streaming and batch ingestion Discover why Apache Iceberg is a foundational technology for implementing an open data lakehouse.
Author: Matthias Giese
Publisher: Springer Science & Business Media
ISBN: 3709114195
Category : Medical
Languages : en
Pages : 460
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Book Description
This book gives a comprehensive overview to all aspects of global molecular vaccine research. It introduces concepts of vaccine immunology and molecular vaccine development for viral, bacterial, parasitic and fungal infections. Furthermore, the broad field of research and development in molecular cancer vaccines is discussed in detail. This book is a must have for scientists and clinicians interested in new developments in molecular vaccine research and application in infections and cancer.
Author: Cybellium Ltd
Publisher: Cybellium Ltd
ISBN:
Category : Computers
Languages : en
Pages : 140
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Book Description
Unleash the Power of Distributed Streaming Platform for Real-Time Data Are you ready to delve into the realm of distributed streaming and real-time data processing with Apache Kafka? "Mastering Apache Kafka" is your definitive guide to harnessing the full potential of this cutting-edge platform for building scalable, fault-tolerant, and high-performance data pipelines. Whether you're a data engineer looking to optimize data flows or a software architect aiming to build robust event-driven systems, this book equips you with the knowledge and tools to master the art of Kafka-based data streaming. Key Features: 1. Deep Dive into Apache Kafka: Immerse yourself in the core principles of Apache Kafka, comprehending its architecture, components, and dynamic capabilities. Construct a sturdy foundation that empowers you to manage and process real-time data streams with precision. 2. Installation and Configuration: Master the art of installing and configuring Apache Kafka on diverse platforms. Learn about cluster setup, topic creation, and configuration tuning for optimal performance. 3. Publishing and Consuming Data: Uncover the power of Kafka for publishing and consuming data streams. Explore producer and consumer APIs, message serialization, and different messaging patterns for building resilient data pipelines. 4. Data Streams and Processing: Delve into Kafka Streams for real-time data processing. Learn how to perform transformations, aggregations, and enrichments on data streams without the need for external processing engines. 5. Fault Tolerance and Scalability: Master Kafka's inherent fault tolerance and scalability features. Explore replication, partitioning, and high availability mechanisms that ensure data integrity and system reliability. 6. Connectors and Ecosystem: Explore Kafka's rich ecosystem of connectors and integrations. Learn how to connect Kafka with databases, cloud services, and other systems to facilitate seamless data exchange. 7. Security and Authentication: Discover strategies for securing your Kafka cluster. Learn about encryption, access controls, authentication mechanisms, and best practices to safeguard your data streams. 8. Monitoring and Management: Uncover techniques for monitoring and managing Kafka clusters. Explore tools for tracking performance metrics, diagnosing issues, and ensuring optimal system health. 9. Event Sourcing and Stream Processing Architectures: Embark on a journey into event-driven architectures and stream processing. Learn how Kafka can serve as the backbone for building scalable and responsive systems. 10. Real-World Applications: Gain insights into real-world use cases of Apache Kafka across industries. From IoT data integration to real-time analytics, discover how organizations leverage Kafka for innovative data-driven solutions. Who This Book Is For: "Mastering Apache Kafka" is an indispensable resource for data engineers, software architects, and IT professionals poised to excel in the domain of real-time data streaming with Kafka. Whether you're new to Kafka or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of this transformative platform.
