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.
Martin is a researcher in distributed systems at the University of Cambridge. Previously he was a software engineer and entrepreneur at Internet companies including LinkedIn and Rapportive, where he worked on large-scale data infrastructure. In the process he learned a few things the hard way, and he hopes this book will save you from repeating the same mistakes.
Martin is a regular conference speaker, blogger, and open source contributor. He believes that profound technical ideas should be accessible to everyone, and that deeper understanding will help us develop better software.
If you are a data scientist who wants to learn how to perform deep learning on Hadoop, this is the book for you. Knowledge of the basic machine learning concepts and some understanding of Hadoop is required to make the best use of this book.What You Will LearnExplore Deep Learning and various models associated with itUnderstand the challenges of implementing distributed deep learning with Hadoop and how to overcome itImplement Convolutional Neural Network (CNN) with deeplearning4jDelve into the implementation of Restricted Boltzmann Machines (RBM)Understand the mathematical explanation for implementing Recurrent Neural Networks (RNN)Get hands on practice of deep learning and their implementation with Hadoop.In Detail
This book will teach you how to deploy large-scale dataset in deep neural networks with Hadoop for optimal performance.
Starting with understanding what deep learning is, and what the various models associated with deep neural networks are, this book will then show you how to set up the Hadoop environment for deep learning. In this book, you will also learn how to overcome the challenges that you face while implementing distributed deep learning with large-scale unstructured datasets. The book will also show you how you can implement and parallelize the widely used deep learning models such as Deep Belief Networks, Convolutional Neural Networks, Recurrent Neural Networks, Restricted Boltzmann Machines and autoencoder using the popular deep learning library deeplearning4j.
Get in-depth mathematical explanations and visual representations to help you understand the design and implementations of Recurrent Neural network and Denoising AutoEncoders with deeplearning4j. To give you a more practical perspective, the book will also teach you the implementation of large-scale video processing, image processing and natural language processing on Hadoop.
By the end of this book, you will know how to deploy various deep neural networks in distributed systems using Hadoop.Style and approach
This book takes a comprehensive, step-by-step approach to implement efficient deep learning models on Hadoop. It starts from the basics and builds the readers' knowledge as they strengthen their understanding of the concepts. Practical examples are included in every step of the way to supplement the theory.
If you're interested in learning more about PostgreSQL - one of the most popular relational databases in the world, then this book is for you. Those looking to build solid database or data warehousing applications with PostgreSQL 10 will also find this book a useful resource. No prior knowledge of database programming or administration is required to get started with this book.What You Will LearnUnderstand the fundamentals of relational databases, relational algebra, and data modelingInstall a PostgreSQL cluster, create a database, and implement your data modelCreate tables and views, define indexes, and implement triggers, stored procedures, and other schema objectsUse the Structured Query Language (SQL) to manipulate data in the databaseImplement business logic on the server side with triggers and stored procedures using PL/pgSQLMake use of advanced data types supported by PostgreSQL 10: Arrays, hstore, JSONB, and othersDevelop OLAP database solutions using the most recent features of PostgreSQL 10Connect your Python applications to a PostgreSQL database and work with the data efficientlyTest your database code, find bottlenecks, improve performance, and enhance the reliability of the database applicationsIn Detail
PostgreSQL is one of the most popular open source databases in the world, and supports the most advanced features included in SQL standards and beyond. This book will familiarize you with the latest new features released in PostgreSQL 10, and get you up and running with building efficient PostgreSQL database solutions from scratch.
We'll start with the concepts of relational databases and their core principles. Then you'll get a thorough introduction to PostgreSQL and the new features introduced in PostgreSQL 10. We'll cover the Data Definition Language (DDL) with an emphasis on PostgreSQL, and the common DDL commands supported by ANSI SQL. You'll learn to create tables, define integrity constraints, build indexes, and set up views and other schema objects.
Moving on, you'll get to know the concepts of Data Manipulation Language (DML) and PostgreSQL server-side programming capabilities using PL/pgSQL. This will give you a very robust background to develop, tune, test, and troubleshoot your database application. We'll also explore the NoSQL capabilities of PostgreSQL and connect to your PostgreSQL database to manipulate data objects.
By the end of this book, you'll have a thorough understanding of the basics of PostgreSQL 10 and will have the necessary skills to build efficient database solutions.Style and approach
This book is a comprehensive beginner level tutorial on PostgreSQL and introduces the features of the newest version 10, along with explanation of concepts in a very easy to understand manner. Practical tips and examples are provided at every step to ensure you are able to grasp each topic as quickly as possible.
If you are new to the NoSQL document system or have little or no experience in NoSQL development and administration and are planning to deploy Couchbase for your next project, then this book is for you. It would be helpful to have a bit of familiarity with Java.What You Will LearnGet acquainted with the concept of NoSQL databases and configure your Couchbase database clusterMaintain Couchbase effectively using the web-based administrative console with easeEnable partition capabilities by making use of BucketsAnalyze important design considerations for maintaining relationship between various documentsUse Couchbase SDK Java API to store and retrieve documentWrite views using map/reduce to retrieve documents efficientlyGet familiar with N1QL and how to use it in Java applicationsIntegrate Couchbase with Elasticsearch to implement full text searchConfigure XDCR for disaster recovery and develop ecommerce application using CouchbaseIn Detail
NoSQL database systems have changed application development in terms of adaptability to dynamics schema and scalability. Compared with the currently available NoSQL database systems, Couchbase is the fastest. Its ease of configuration and powerful features for storing different schema structures, retrieval using map reduce and inbuilt disaster recovery by replicating document across the geographical region, make it one of the most powerful, scalable and comprehensive NoSQL in the market. Couchbase also introduces smart client API for various programming language to integrate the database with the application easily, yet providing very complex features like cluster health awareness.
This book achieves its goal by taking up an end-to-end development structure, right from understanding NOSQL document design to implementing full fledged eCommerce application design using Couchbase as a backend.
Starting with the architecture of Couchbase to get you up and running, this book quickly takes you through designing a NoSQL document and implementing highly scalable applications using Java API. You will then be introduced to document design and get to know the various ways to administer Couchbase. Followed by this, learn to store documents using bucket. Moving on, you will then learn to store, retrieve and delete documents using smart client base on Java API. You will then retrieve documents using SQL like syntax call N1QL. Next, you will learn how to write map reduce base views. Finally, you will configure XDCR for disaster recovery and implement an eCommerce application using Couchbase.Style and approach
The book starts from absolute basics and slowly moves to more advanced topics ensuring at every step that all concepts and terms are understood by the reader to have complete understanding at every stage. Technical and complex terms are explained in clear and simple language, thus making this book a perfect companion for those who have started their journey to NoSQL using Couchbase
You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—including classification, clustering, collaborative filtering, and anomaly detection—to fields such as genomics, security, and finance.
If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find the book’s patterns useful for working on your own data applications.
With this book, you will:Familiarize yourself with the Spark programming modelBecome comfortable within the Spark ecosystemLearn general approaches in data scienceExamine complete implementations that analyze large public data setsDiscover which machine learning tools make sense for particular problemsAcquire code that can be adapted to many uses
Patterns of Enterprise Application Architecture is written in direct response to the stiff challenges that face enterprise application developers. The author, noted object-oriented designer Martin Fowler, noticed that despite changes in technology--from Smalltalk to CORBA to Java to .NET--the same basic design ideas can be adapted and applied to solve common problems. With the help of an expert group of contributors, Martin distills over forty recurring solutions into patterns. The result is an indispensable handbook of solutions that are applicable to any enterprise application platform.
This book is actually two books in one. The first section is a short tutorial on developing enterprise applications, which you can read from start to finish to understand the scope of the book's lessons. The next section, the bulk of the book, is a detailed reference to the patterns themselves. Each pattern provides usage and implementation information, as well as detailed code examples in Java or C#. The entire book is also richly illustrated with UML diagrams to further explain the concepts.
Armed with this book, you will have the knowledge necessary to make important architectural decisions about building an enterprise application and the proven patterns for use when building them.
The topics covered include
· Dividing an enterprise application into layers
· The major approaches to organizing business logic
· An in-depth treatment of mapping between objects and relational databases
· Using Model-View-Controller to organize a Web presentation
· Handling concurrency for data that spans multiple transactions
· Designing distributed object interfaces
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates
Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you’ll learn Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.Understand publish-subscribe messaging and how it fits in the big data ecosystem.Explore Kafka producers and consumers for writing and reading messagesUnderstand Kafka patterns and use-case requirements to ensure reliable data deliveryGet best practices for building data pipelines and applications with KafkaManage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasksLearn the most critical metrics among Kafka’s operational measurementsExplore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems
Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.Use the IPython shell and Jupyter notebook for exploratory computingLearn basic and advanced features in NumPy (Numerical Python)Get started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples