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Author: Tyler Akidau
Publisher: "O'Reilly Media, Inc."
ISBN: 1491983825
Category : Computers
Languages : en
Pages : 362
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Book Description
Streaming data is a big deal in big data these days. As more and more businesses seek to tame the massive unbounded data sets that pervade our world, streaming systems have finally reached a level of maturity sufficient for mainstream adoption. With this practical guide, data engineers, data scientists, and developers will learn how to work with streaming data in a conceptual and platform-agnostic way. Expanded from Tyler Akidau’s popular blog posts "Streaming 101" and "Streaming 102", this book takes you from an introductory level to a nuanced understanding of the what, where, when, and how of processing real-time data streams. You’ll also dive deep into watermarks and exactly-once processing with co-authors Slava Chernyak and Reuven Lax. You’ll explore: How streaming and batch data processing patterns compare The core principles and concepts behind robust out-of-order data processing How watermarks track progress and completeness in infinite datasets How exactly-once data processing techniques ensure correctness How the concepts of streams and tables form the foundations of both batch and streaming data processing The practical motivations behind a powerful persistent state mechanism, driven by a real-world example How time-varying relations provide a link between stream processing and the world of SQL and relational algebra
Author: Tyler Akidau
Publisher: "O'Reilly Media, Inc."
ISBN: 1491983825
Category : Computers
Languages : en
Pages : 362
Get Book Here
Book Description
Streaming data is a big deal in big data these days. As more and more businesses seek to tame the massive unbounded data sets that pervade our world, streaming systems have finally reached a level of maturity sufficient for mainstream adoption. With this practical guide, data engineers, data scientists, and developers will learn how to work with streaming data in a conceptual and platform-agnostic way. Expanded from Tyler Akidau’s popular blog posts "Streaming 101" and "Streaming 102", this book takes you from an introductory level to a nuanced understanding of the what, where, when, and how of processing real-time data streams. You’ll also dive deep into watermarks and exactly-once processing with co-authors Slava Chernyak and Reuven Lax. You’ll explore: How streaming and batch data processing patterns compare The core principles and concepts behind robust out-of-order data processing How watermarks track progress and completeness in infinite datasets How exactly-once data processing techniques ensure correctness How the concepts of streams and tables form the foundations of both batch and streaming data processing The practical motivations behind a powerful persistent state mechanism, driven by a real-world example How time-varying relations provide a link between stream processing and the world of SQL and relational algebra
Author: Anindita Basak
Publisher: Packt Publishing Ltd
ISBN: 1788390628
Category : Computers
Languages : en
Pages : 314
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Book Description
Develop and manage effective real-time streaming solutions by leveraging the power of Microsoft Azure About This Book Analyze your data from various sources using Microsoft Azure Stream Analytics Develop, manage and automate your stream analytics solution with Microsoft Azure A practical guide to real-time event processing and performing analytics on the cloud Who This Book Is For If you are looking for a resource that teaches you how to process continuous streams of data in real-time, this book is what you need. A basic understanding of the concepts in analytics is all you need to get started with this book What You Will Learn Perform real-time event processing with Azure Stream Analysis Incorporate the features of Big Data Lambda architecture pattern in real-time data processing Design a streaming pipeline for storage and batch analysis Implement data transformation and computation activities over stream of events Automate your streaming pipeline using Powershell and the .NET SDK Integrate your streaming pipeline with popular Machine Learning and Predictive Analytics modelling algorithms Monitor and troubleshoot your Azure Streaming jobs effectively In Detail Microsoft Azure is a very popular cloud computing service used by many organizations around the world. Its latest analytics offering, Stream Analytics, allows you to process and get actionable insights from different kinds of data in real-time. This book is your guide to understanding the basics of how Azure Stream Analytics works, and building your own analytics solution using its capabilities. You will start with understanding what Stream Analytics is, and why it is a popular choice for getting real-time insights from data. Then, you will be introduced to Azure Stream Analytics, and see how you can use the tools and functions in Azure to develop your own Streaming Analytics. Over the course of the book, you will be given comparative analytic guidance on using Azure Streaming with other Microsoft Data Platform resources such as Big Data Lambda Architecture integration for real time data analysis and differences of scenarios for architecture designing with Azure HDInsight Hadoop clusters with Storm or Stream Analytics. The book also shows you how you can manage, monitor, and scale your solution for optimal performance. By the end of this book, you will be well-versed in using Azure Stream Analytics to develop an efficient analytics solution that can work with any type of data. Style and approach A comprehensive guidance on developing real-time event processing with Azure Stream Analysis
Author: Jonas Traub
Publisher: BoD – Books on Demand
ISBN: 3753488941
Category : Computers
Languages : en
Pages : 206
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Book Description
This book presents an end-to-end architecture for demand-based data stream gathering, processing, and transmission. The Internet of Things (IoT) consists of billions of devices which form a cloud of network connected sensor nodes. These sensor nodes supply a vast number of data streams with massive amounts of sensor data. Real-time sensor data enables diverse applications including traffic-aware navigation, machine monitoring, and home automation. Current stream processing pipelines are demand-oblivious, which means that they gather, transmit, and process as much data as possible. In contrast, a demand-based processing pipeline uses requirement specifications of data consumers, such as failure tolerances and latency limitations, to save resources. Our solution unifies the way applications express their data demands, i.e., their requirements with respect to their input streams. This unification allows for multiplexing the data demands of all concurrently running applications. On sensor nodes, we schedule sensor reads based on the data demands of all applications, which saves up to 87% in sensor reads and data transfers in our experiments with real-world sensor data. Our demand-based control layer optimizes the data acquisition from thousands of sensors. We introduce time coherence as a fundamental data characteristic. Time coherence is the delay between the first and the last sensor read that contribute values to a tuple. A large scale parameter exploration shows that our solution scales to large numbers of sensors and operates reliably under varying latency and coherence constraints. On stream analysis systems, we tackle the problem of efficient window aggregation. We contribute a general aggregation technique, which adapts to four key workload characteristics: Stream (dis)order, aggregation types, window types, and window measures. Our experiments show that our solution outperforms alternative solutions by an order of magnitude in throughput, which prevents expensive system scale-out. We further derive data demands from visualization needs of applications and make these data demands available to streaming systems such as Apache Flink. This enables streaming systems to pre-process data with respect to changing visualization needs. Experiments show that our solution reliably prevents overloads when data rates increase.
Author: Alexander Gray
Publisher: Springer
ISBN: 354070504X
Category : Computers
Languages : en
Pages : 303
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Book Description
Since 1981, the British National Conferences on Databases (BNCOD) have p- vided a forum for database researchers to report the latest progress and explore new ideas. Over the last 28 years, BNCOD has evolved from a predominantly national conference into one that is truly international, attracting research c- tributions from all over the world. This volume contains the proceedings of BNCOD 2008. We received 45 s- missions from 22 countries. Each paper was reviewed by three referees, and 14 full papers and 7 posters were accepted. All the research papers and posters are included in this volume, and they are organized into ?ve sections: data mining and privacy, data integration, stream and event data processing, query proce- ing and optimization, and posters. The keynote was delivered by Monica Marinucci, EMEA Programme Dir- tor for Oracle in R&D. She has been involved in various advanced developments concerning Oracle, and participated in EC-funded projects as an expert, es- cially the CHALLENGERS special support action to propose the future of grid computing. In her keynote presentation,she addressedthe audience on the topic of the power of data, emphasizing that the ability to store, handle, manipulate, distribute and replicate data and information can provide a tremendous asset to organizations. She also explored some of the latest directions and developments in the database ?eld, and described how Oracle contributes to them partnering up with other leading organizations in di?erent sectors.
Author: Jon Luini
Publisher: New Riders
ISBN: 9780735712805
Category : Computers
Languages : en
Pages : 340
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Book Description
This book contains case studies that show how streaming audio is used on various sites. It begins by giving a comprehensive overview of the most up-to-date streaming technologies available and the process of preparing audio for streaming. Then, it walks readers through encoding for the various players and types of streaming (on-demand vs. live).
Author: G. Pascal Zachary
Publisher: Open Road Media
ISBN: 1480494844
Category : Business & Economics
Languages : en
Pages : 239
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Book Description
This “inside account captures the energy—and the madness—of the software giant’s race to develop a critical new program. . . . Gripping” (Fortune Magazine). Showstopper is the dramatic, inside story of the creation of Windows NT, told by Wall Street Journal reporter G. Pascal Zachary. Driven by the legendary David Cutler, a picked band of software engineers sacrifices almost everything in their lives to build a new, stable, operating system aimed at giving Microsoft a platform for growth through the next decade of development in the computing business. Comparable in many ways to the Pulitzer Prize–winning book The Soul of a New Machine by Tracy Kidder, Showstopper gets deep inside the process of software development, the lives and motivations of coders and the pressure to succeed coupled with the drive for originality and perfection that can pull a diverse team together to create a program consisting of many hundreds of thousands of lines of code.
Author: Dan Sullivan
Publisher: John Wiley & Sons
ISBN: 1119618452
Category : Computers
Languages : en
Pages : 357
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Book Description
The proven Study Guide that prepares you for this new Google Cloud exam The Google Cloud Certified Professional Data Engineer Study Guide, provides everything you need to prepare for this important exam and master the skills necessary to land that coveted Google Cloud Professional Data Engineer certification. Beginning with a pre-book assessment quiz to evaluate what you know before you begin, each chapter features exam objectives and review questions, plus the online learning environment includes additional complete practice tests. Written by Dan Sullivan, a popular and experienced online course author for machine learning, big data, and Cloud topics, Google Cloud Certified Professional Data Engineer Study Guide is your ace in the hole for deploying and managing analytics and machine learning applications. Build and operationalize storage systems, pipelines, and compute infrastructure Understand machine learning models and learn how to select pre-built models Monitor and troubleshoot machine learning models Design analytics and machine learning applications that are secure, scalable, and highly available. This exam guide is designed to help you develop an in depth understanding of data engineering and machine learning on Google Cloud Platform.
Author: Zdravko Galić
Publisher: Springer
ISBN: 1493965751
Category : Computers
Languages : en
Pages : 116
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Book Description
This SpringerBrief presents the fundamental concepts of a specialized class of data stream, spatio-temporal data streams, and demonstrates their distributed processing using Big Data frameworks and platforms. It explores a consistent framework which facilitates a thorough understanding of all different facets of the technology, from basic definitions to state-of-the-art techniques. Key topics include spatio-temporal continuous queries, distributed stream processing, SQL-like language embedding, and trajectory stream clustering. Over the course of the book, the reader will become familiar with spatio-temporal data streams management and data flow processing, which enables the analysis of huge volumes of location-aware continuous data streams. Applications range from mobile object tracking and real-time intelligent transportation systems to traffic monitoring and complex event processing. Spatio-Temporal Data Streams is a valuable resource for researchers studying spatio-temporal data streams and Big Data analytics, as well as data engineers and data scientists solving data management and analytics problems associated with this class of data.
Author: Shima Zahmatkesh
Publisher: Springer Nature
ISBN: 3030383393
Category : Computers
Languages : en
Pages : 128
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Book Description
This book examines the problem of relevant query answering over the Web and provides a comprehensive overview of relevant query answering over streaming and distributed data. In recent years, Web applications that combine highly dynamic data streams with data distributed over the Web to provide relevant answers have attracted increasing attention. Answering in a timely fashion, i.e., reactively, is one of the most important performance indicators, especially when the distributed data is evolving. The book proposes a solution that retains a local replica of the distributed data and offers various maintenance policies to refresh the replica over time. A limited refresh budget guarantees the reactiveness of the system. Focusing on stream processing and Semantic Web, it appeals to scientists and graduate students in the field.
Author: Saba Shah
Publisher: Packt Publishing Ltd
ISBN: 1804616206
Category : Computers
Languages : en
Pages : 274
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Book Description
Learn the concepts and exercises needed to confidently prepare for the Databricks Associate Developer for Apache Spark 3.0 exam and validate your Spark skills with an industry-recognized credential Key Features Understand the fundamentals of Apache Spark to design robust and fast Spark applications Explore various data manipulation components for each phase of your data engineering project Prepare for the certification exam with sample questions and mock exams Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionSpark has become a de facto standard for big data processing. Migrating data processing to Spark saves resources, streamlines your business focus, and modernizes workloads, creating new business opportunities through Spark’s advanced capabilities. Written by a senior solutions architect at Databricks, with experience in leading data science and data engineering teams in Fortune 500s as well as startups, this book is your exhaustive guide to achieving the Databricks Certified Associate Developer for Apache Spark certification on your first attempt. You’ll explore the core components of Apache Spark, its architecture, and its optimization, while familiarizing yourself with the Spark DataFrame API and its components needed for data manipulation. You’ll also find out what Spark streaming is and why it’s important for modern data stacks, before learning about machine learning in Spark and its different use cases. What’s more, you’ll discover sample questions at the end of each section along with two mock exams to help you prepare for the certification exam. By the end of this book, you’ll know what to expect in the exam and gain enough understanding of Spark and its tools to pass the exam. You’ll also be able to apply this knowledge in a real-world setting and take your skillset to the next level.What you will learn Create and manipulate SQL queries in Apache Spark Build complex Spark functions using Spark's user-defined functions (UDFs) Architect big data apps with Spark fundamentals for optimal design Apply techniques to manipulate and optimize big data applications Develop real-time or near-real-time applications using Spark Streaming Work with Apache Spark for machine learning applications Who this book is for This book is for data professionals such as data engineers, data analysts, BI developers, and data scientists looking for a comprehensive resource to achieve Databricks Certified Associate Developer certification, as well as for individuals who want to venture into the world of big data and data engineering. Although working knowledge of Python is required, no prior knowledge of Spark is necessary. Additionally, experience with Pyspark will be beneficial.
