Apache Solr Search Patterns. Leverage the power of Apache Solr to power up your business by navigating your users to their data quickly and efficiently

Jayant Kumar

Apache Spark 2: Data Processing and Real-Time Analytics. Master complex big data processing, stream analytics, and machine learning with Apache Spark

Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, ...

Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform.You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools.By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle.This Learning Path includes content from the following Packt products:• Mastering Apache Spark 2.x by Romeo Kienzler• Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla• Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook

Apache Spark 2.x Cookbook. Over 70 cloud-ready recipes for distributed Big Data processing and analytics

Rishi Yadav

While Apache Spark 1.x gained a lot of traction and adoption in the early years, Spark 2.x delivers notable improvements in the areas of API, schema awareness, Performance, Structured Streaming, and simplifying building blocks to build better, faster, smarter, and more accessible big data applications. This book uncovers all these features in the form of structured recipes to analyze and mature large and complex sets of data.Starting with installing and configuring Apache Spark with various cluster managers, you will learn to set up development environments. Further on, you will be introduced to working with RDDs, DataFrames and Datasets to operate on schema aware data, and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will also work through recipes on machine learning, including supervised learning, unsupervised learning & recommendation engines in Spark.Last but not least, the final few chapters delve deeper into the concepts of graph processing using GraphX, securing your implementations, cluster optimization, and troubleshooting.

Apache Spark 2.x for Java Developers. Explore big data at scale using Apache Spark 2.x Java APIs

Sourav Gulati, Sumit Kumar

Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the Spark features available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java, without stepping out of your comfort zone.The book starts with an introduction to the Apache Spark 2.x ecosystem, followed by explaining how to install and configure Spark, and refreshes the Java concepts that will be useful to you when consuming Apache Spark's APIs. You will explore RDD and its associated common Action and Transformation Java APIs, set up a production-like clustered environment, and work with Spark SQL. Moving on, you will perform near-real-time processing with Spark streaming, Machine Learning analytics with Spark MLlib, and graph processing with GraphX, all using various Java packages.By the end of the book, you will have a solid foundation in implementing components in the Spark framework in Java to build fast, real-time applications.

Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark

Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks.This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.

Apache Spark Deep Learning Cookbook. Over 80 best practice recipes for the distributed training and deployment of neural networks using Keras and TensorFlow

Ahmed Sherif, Amrith Ravindra

Organizations these days need to integrate popular big data tools such as Apache Spark with highly efficient deep learning libraries if they’re looking to gain faster and more powerful insights from their data. With this book, you’ll discover over 80 recipes to help you train fast, enterprise-grade, deep learning models on Apache Spark.Each recipe addresses a specific problem, and offers a proven, best-practice solution to difficulties encountered while implementing various deep learning algorithms in a distributed environment. The book follows a systematic approach, featuring a balance of theory and tips with best practice solutions to assist you with training different types of neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). You’ll also have access to code written in TensorFlow and Keras that you can run on Spark to solve a variety of deep learning problems in computer vision and natural language processing (NLP), or tweak to tackle other problems encountered in deep learning.By the end of this book, you'll have the skills you need to train and deploy state-of-the-art deep learning models on Apache Spark.

Apache Spark for Data Science Cookbook. Solve real-world analytical problems

Padma Priya Chitturi

Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark’s selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark’s data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work.

Apache Spark for Machine Learning. Build and deploy high-performance big data AI solutions for large-scale clusters

Deepak Gowda

In the world of big data, efficiently processing and analyzing massive datasets for machine learning can be a daunting task. Written by Deepak Gowda, a data scientist with over a decade of experience and 30+ patents, this book provides a hands-on guide to mastering Spark’s capabilities for efficient data processing, model building, and optimization. With Deepak’s expertise across industries such as supply chain, cybersecurity, and data center infrastructure, he makes complex concepts easy to follow through detailed recipes.This book takes you through core machine learning concepts, highlighting the advantages of Spark for big data analytics. It covers practical data preprocessing techniques, including feature extraction and transformation, supervised learning methods with detailed chapters on regression and classification, and unsupervised learning through clustering and recommendation systems. You’ll also learn to identify frequent patterns in data and discover effective strategies to deploy and optimize your machine learning models. Each chapter features practical coding examples and real-world applications to equip you with the knowledge and skills needed to tackle complex machine learning tasks.By the end of this book, you’ll be ready to handle big data and create advanced machine learning models with Apache Spark.

Apache Spark Graph Processing. Build, process and analyze large-scale graph data effectively with Spark

Rindra Ramamonjison, Rindra Ramamonjison

Apache Spark Machine Learning Blueprints. Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide

Alex Liu

There's a reason why Apache Spark has become one of the most popular tools in Machine Learning – its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data.Packed with a range of project blueprints that demonstrate some of the most interesting challenges that Spark can help you tackle, you'll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You'll also find out how to build a recommendation engine using Spark's parallel computing powers.

Apache Spark Quick Start Guide. Quickly learn the art of writing efficient big data applications with Apache Spark

Shrey Mehrotra, Akash Grade

Apache Spark is a ?exible framework that allows processing of batch and real-time data. Its unified engine has made it quite popular for big data use cases. This book will help you to get started with Apache Spark 2.0 and write big data applications for a variety of use cases.It will also introduce you to Apache Spark – one of the most popular Big Data processing frameworks. Although this book is intended to help you get started with Apache Spark, but it also focuses on explaining the core concepts. This practical guide provides a quick start to the Spark 2.0 architecture and its components. It teaches you how to set up Spark on your local machine. As we move ahead, you will be introduced to resilient distributed datasets (RDDs) and DataFrame APIs, and their corresponding transformations and actions. Then, we move on to the life cycle of a Spark application and learn about the techniques used to debug slow-running applications. You will also go through Spark’s built-in modules for SQL, streaming, machine learning, and graph analysis.Finally, the book will lay out the best practices and optimization techniques that are key for writing efficient Spark applications. By the end of this book, you will have a sound fundamental understanding of the Apache Spark framework and you will be able to write and optimize Spark applications.

Apache Superset Quick Start Guide. Develop interactive visualizations by creating user-friendly dashboards

Shashank Shekhar

Apache Superset is a modern, open source, enterprise-ready business intelligence (BI) web application. With the help of this book, you will see how Superset integrates with popular databases like Postgres, Google BigQuery, Snowflake, and MySQL. You will learn to create real time data visualizations and dashboards on modern web browsers for your organization using Superset.First, we look at the fundamentals of Superset, and then get it up and running. You'll go through the requisite installation, configuration, and deployment. Then, we will discuss different columnar data types, analytics, and the visualizations available. You'll also see the security tools available to the administrator to keep your data safe.You will learn how to visualize relationships as graphs instead of coordinates on plain orthogonal axes. This will help you when you upload your own entity relationship dataset and analyze the dataset in new, different ways. You will also see how to analyze geographical regions by working with location data.Finally, we cover a set of tutorials on dashboard designs frequently used by analysts, business intelligence professionals, and developers.

Apache Tomcat 7 Essentials. This book takes you from beginner to expert in logical stages, covering all the essentials of Tomcat 7 from trouble-free installation to building your own middleware servers. Packed with examples and illustrations

Tanuj Khare

Apache Tomcat (or simply Tomcat) is an open source servlet container developed by the Apache Software Foundation (ASF). The latest major stable release, Apache Tomcat version 7 implements the Servlet 3 and JavaServer Pages 2 specifications from the Java Community Process, and includes many additional features that make it a useful platform for developing and deploying web applications and web services.Apache Tomcat 7 Essentials follows a practical approach to teach installing, configuring, and maintaining Tomcat. It helps you to understand the middle architecture for hosting multiple websites and also provides the confidence to implement middleware support. It imparts to you the capacity to resolve migration issues and also provides regular maintenance solutions. This is the first and only book to cover upgrading to Tomcat 7 from previous versions.The journey of the reader starts at the beginner level and ends at the expert level. The content is designed in such a way that it balances the theory and practical approach for understanding concepts related to handling middle ware and web issues.In this book, you will go through a three-phase life cycle. The first cycle consists of installation, configuration of Tomcat 7 on different OS, and other configurations related to JDBC, port, deployment etc. The second phase deals with the building of enterprise application setup and high availability architecture (clustering load balancing). The third and critical phase will teach you to handle critical issues, performance tuning, and best practices for various environment stacks like dev/QA/stage/production.This book gives you a wider vision of using Tomcat 7 in web technologies and the skill to optimize their performance using Apache Tomcat 7.

Apache ZooKeeper Essentials. A fast-paced guide to using Apache ZooKeeper to coordinate services in distributed systems

Saurav Haloi

Aparatura przemysłowa

Mirosław Nizielski, Krzysztof Urbaniec

Praca powstała jako rozszerzenie materiałów dydaktycznych przeznaczonych dla studentów kierunku studiów mechanika i budowa maszyn na Wydziale Budownictwa, Mechaniki i Petrochemii Politechniki Warszawskiej.   Zakres pracy obejmuje podstawową wiedzę przydatną w projektowaniu, budowie i eksploatacji aparatów o często spotykanych cechach konstrukcyjnych, z uwzględnieniem ich zastosowań w różnych procesach przemysłowych. Rozdziały poświęcone różnym typom aparatów nie zawierają kompletnego przeglądu aparatury przemysłowej, a jedynie zbiór przykładowych rozwiązań konstrukcyjnych. Są to rozwiązania aparatów stosowanych m.in. w energetyce, przemyśle chemicznym i spożywczym oraz instalacjach ochrony środowiska.   W stosunku do pierwszego wydania, z 2010 roku, w pracy: zaktualizowano dane o aktach prawnych i normach o istotnym znaczeniu dla projektowania i eksploatacji urządzeń przemysłowych, rozszerzono zakres tematyczny przeglądu rozwiązań konstrukcyjnych o informacje na temat bioreaktorów, uzupełniono informacje o filtrach do ciekłych zawiesin i piecach przemysłowych oraz zaktualizowano wykazy literatury.   Praca może być wykorzystana jako lektura uzupełniająca dla studentów różnych kierunków studiów, w tym chemii, inżynierii chemicznej i inżynierii środowiska.

Apartament 23

Marek Wróbel

Apartament 23 - podróż uliczkami i knajpkami Krakowa. Niekiedy mrocznymi, niekiedy zabawnymi. Innym razem, ocierając się o absurd, sytuacje dwuznaczne malują nowy nieznany pejzaż. W tle porwanie, morderstwo, szaleńczy guru i miłość. Marek Wróbel, ur. 1976 w Warszawie. Wychowany w Białej Podlaskiej. Mieszkał kilka lat w Krakowie. Obecnie zamieszkuje w Warszawie. Doktorant filozofii w Krakowie. Studiował na AWF i Akademii Muz. Lider kilku składów muzycznych. Lubi stare filmy, biografie świętych, dobre buty.