Data Processing Systems Design


Book Description




Designing Data-Intensive Applications


Book Description

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures




Advances in Image and Data Processing Using VLSI Design


Book Description

VLSI is a well-established field of research that ignited the modern computing revolution. Serving as a guide to future developments, this book provides a framework for design, modeling concepts, and application of Image Processing based systems using VLSI design techniques.




Data Processing


Book Description




Sensor Modelling, Design and Data Processing for Autonomous Navigation


Book Description

This invaluable book presents an unbiased framework for modelling and using sensors to aid mobile robot navigation. It addresses the problem of accurate and reliable sensing in confined environments and makes a detailed analysis of the design and construction of a low cost optical range finder. This is followed by a quantitative model for determining the sources and propagation of noise within the sensor. The physics behind the causes of erroneous data is also used to derive a model for detecting and labelling such data as false. In addition, the author's data-processing algorithms are applied to the problem of environmental feature extraction. This forms the basis of a solution to the problem of mobile robot localisation. The book develops a relationship between the kinematics of a mobile robot during the execution of successive manoeuvres, and the sensed features. Results which update a mobile vehicle's position using features from 2D and 3D scans are presented.










Automatic Design of Data Processing Systems


Book Description

The design of data organization and data accessing procedures for data processing systems operating on large keyed files of data is a common and recurrent activity in modern data processing applications. A considerable amount of understanding and expertise in this area has been developed and it is time to begin codifying and automating this process. It should be possible to develop a system where the user has merely to specify the characteristics of his data objects and their interrelations and the system will automatically determine the data organizations and accessing procedures that are optimal for his application. The optimizer for Protosystem I (an automatic programming system prototype at MIT) provides an example of how such automation can be accomplished.




Big Data Processing with Apache Spark


Book Description

Apache Spark is a popular open-source big-data processing framework thatÕs built around speed, ease of use, and unified distributed computing architecture. Not only it supports developing applications in different languages like Java, Scala, Python, and R, itÕs also hundred times faster in memory and ten times faster even when running on disk compared to traditional data processing frameworks. Whether you are currently working on a big data project or interested in learning more about topics like machine learning, streaming data processing, and graph data analytics, this book is for you. You can learn about Apache Spark and develop Spark programs for various use cases in big data analytics using the code examples provided. This book covers all the libraries in Spark ecosystem: Spark Core, Spark SQL, Spark Streaming, Spark ML, and Spark GraphX.