Leverage Splunk's operational intelligence capabilities to unlock new hidden business insights and drive success Key Features Tackle any problems related to searching and analyzing your data with Splunk Get the latest information and business insights on Splunk 7.x Explore the all new machine learning toolkit in Splunk 7.x Book DescriptionSplunk makes it easy for you to take control of your data, and with Splunk Operational Cookbook, you can be confident that you are taking advantage of the Big Data revolution and driving your business with the cutting edge of operational intelligence and business analytics. With more than 80 recipes that demonstrate all of Splunk's features, not only will you find quick solutions to common problems, but you'll also learn a wide range of strategies and uncover new ideas that will make you rethink what operational intelligence means to you and your organization. You'll discover recipes on data processing, searching and reporting, dashboards, and visualizations to make data shareable, communicable, and most importantly meaningful. You'll also find step-by-step demonstrations that walk you through building an operational intelligence application containing vital features essential to understanding data and to help you successfully integrate a data-driven way of thinking in your organization. Throughout the book, you'll dive deeper into Splunk, explore data models and pivots to extend your intelligence capabilities, and perform advanced searching with machine learning to explore your data in even more sophisticated ways. Splunk is changing the business landscape, so make sure you're taking advantage of it. What you will learn Learn how to use Splunk to gather, analyze, and report on data Create dashboards and visualizations that make data meaningful Build an intelligent application with extensive functionalities Enrich operational data with lookups and workflows Model and accelerate data and perform pivot-based reporting Apply ML algorithms for forecasting and anomaly detection Summarize data for long term trending, reporting, and analysis Integrate advanced JavaScript charts and leverage Splunk's API Who this book is forThis book is intended for data professionals who are looking to leverage the Splunk Enterprise platform as a valuable operational intelligence tool. The recipes provided in this book will appeal to individuals from all facets of business, IT, security, product, marketing, and many more! Even the existing users of Splunk who want to upgrade and get up and running with Splunk 7.x will find this book to be of great value.

A beginner's guide to get you up and running with Cassandra, DynamoDB, HBase, InfluxDB, MongoDB, Neo4j, and Redis Key Features Covers the basics of 7 NoSQL databases and how they are used in the enterprises Quick introduction to MongoDB, DynamoDB, Redis, Cassandra, Neo4j, InfluxDB, and HBase Includes effective techniques for database querying and management Book DescriptionThis is the golden age of open source NoSQL databases. With enterprises having to work with large amounts of unstructured data and moving away from expensive monolithic architecture, the adoption of NoSQL databases is rapidly increasing. Being familiar with the popular NoSQL databases and knowing how to use them is a must for budding DBAs and developers. This book introduces you to the different types of NoSQL databases and gets you started with seven of the most popular NoSQL databases used by enterprises today. We start off with a brief overview of what NoSQL databases are, followed by an explanation of why and when to use them. The book then covers the seven most popular databases in each of these categories: MongoDB, Amazon DynamoDB, Redis, HBase, Cassandra, InfluxDB, and Neo4j. The book doesn't go into too much detail about each database but teaches you enough to get started with them. By the end of this book, you will have a thorough understanding of the different NoSQL databases and their functionalities, empowering you to select and use the right database according to your needs. What you will learn Understand how MongoDB provides high-performance, high-availability, and automatic scaling Interact with your Neo4j instances via database queries, Python scripts, and Java application code Get familiar with common querying and programming methods to interact with Redis Study the different types of problems Cassandra can solve Work with HBase components to support common operations such as creating tables and reading/writing data Discover data models and work with CRUD operations using DynamoDB Discover what makes InfluxDB a great choice for working with time-series data Who this book is forIf you are a budding DBA or a developer who wants to get started with the fundamentals of NoSQL databases, this book is for you. Relational DBAs who want to get insights into the various offerings of popular NoSQL databases will also find this book to be very useful.

How can you get your data from frontend servers to Hadoop in near real time? With this complete reference guide, you'll learn Flume's rich set of features for collecting, aggregating, and writing large amounts of streaming data to the Hadoop Distributed File System (HDFS), Apache HBase, SolrCloud, Elastic Search, and other systems. Using Flume shows operations engineers how to configure, deploy, and monitor a Flume cluster, and teaches developers how to write Flume plugins and custom components for their specific use-cases. You'll learn about Flume's design and implementation, as well as various features that make it highly scalable, flexible, and reliable. Code examples and exercises are available on GitHub. Learn how Flume provides a steady rate of flow by acting as a buffer between data producers and consumers Dive into key Flume components, including sources that accept data and sinks that write and deliver it Write custom plugins to customize the way Flume receives, modifies, formats, and writes data Explore APIs for sending data to Flume agents from your own applications Plan and deploy Flume in a scalable and flexible way - and monitor your cluster once it's running

Leverage the capabilities of SAS to process and analyze Big Data About This Book * Combine SAS with platforms such as Hadoop, SAP HANA, and Cloud Foundry-based platforms for effecient Big Data analytics * Learn how to use the web browser-based SAS Studio and iPython Jupyter Notebook interfaces with SAS * Practical, real-world examples on predictive modeling, forecasting, optimizing and reporting your Big Data analysis with SAS Who This Book Is For SAS professionals and data analysts who wish to perform analytics on Big Data using SAS to gain actionable insights will find this book to be very useful. If you are a data science professional looking to perform large-scale analytics with SAS, this book will also help you. A basic understanding of SAS will be helpful, but is not mandatory. What You Will Learn * Configure a free version of SAS in order do hands-on exercises dealing with data management, analysis, and reporting. * Understand the basic concepts of the SAS language which consists of the data step (for data preparation) and procedures (or PROCs) for analysis. * Make use of the web browser based SAS Studio and iPython Jupyter Notebook interfaces for coding in the SAS, DS2, and FedSQL programming languages. * Understand how the DS2 programming language plays an important role in Big Data preparation and analysis using SAS * Integrate and work efficiently with Big Data platforms like Hadoop, SAP HANA, and cloud foundry based systems. In Detail SAS has been recognized by Money Magazine and Payscale as one of the top business skills to learn in order to advance one's career. Through innovative data management, analytics, and business intelligence software and services, SAS helps customers solve their business problems by allowing them to make better decisions faster. This book introduces the reader to the SAS and how they can use SAS to perform efficient analysis on any size data, including Big Data. The reader will learn how to prepare data for analysis, perform predictive, forecasting, and optimization analysis and then deploy or report on the results of these analyses. While performing the coding examples within this book the reader will learn how to use the web browser based SAS Studio and iPython Jupyter Notebook interfaces for working with SAS. Finally, the reader will learn how SAS's architecture is engineered and designed to scale up and/or out and be combined with the open source offerings such as Hadoop, Python, and R. By the end of this book, you will be able to clearly understand how you can efficiently analyze Big Data using SAS. Style and approach The book starts off by introducing the reader to SAS and the SAS programming language which provides data management, analytical, and reporting capabilities. Most chapters include hands on examples which highlights how SAS provides The Power to Know (c). The reader will learn that if they are looking to perform large-scale data analysis that SAS provides an open platform engineered and designed to scale both up and out which allows the power of SAS to combine with open source offerings such as Hadoop, Python, and R.

Sharpen your data visualization skills with Tableau 10 Bootcamp. About This Book * Make informed decisions using powerful visualizations in Tableau * Learn effective data storytelling to transform how your business uses ideas * Use this extensive bootcamp that makes you an efficient Tableau user in a short span of time Who This Book Is For This book caters to business, data, and analytics professionals who want to build rich interactive visualizations using Tableau Desktop. Familiarity with previous versions of Tableau will be helpful, but not necessary. What You Will Learn * Complete practical Tableau tasks with each chapter * Build different types of charts in Tableau with ease * Extend data using calculated fields and parameters * Prepare and refine data for analysis * Create engaging and interactive dashboards * Present data effectively using story points In Detail Tableau is a leading visual analytics software that can uncover insights for better and smarter decision-making. Tableau has an uncanny ability to beautify your data, compared to other BI tools, which makes it an ideal choice for performing fast and easy visual analysis. A military camp style fast-paced learning book that builds your understanding of Tableau 10 in no time. This day based learning guide contains the best elements from two of our published books, Learning Tableau 10 - Second Edition and Tableau 10 Business Intelligence Cookbook, and delivers practical, learning modules in manageable chunks. Each chunk is delivered in a "day", and each "day" is a productive day. Each day builds your competency in Tableau. You will increase your competence in integrating analytics and forecasting to enhance data analysis during the course of this Bootcamp. Each chapter presents core concepts and key takeaways about a topic in Tableau and provides a series of hands-on exercises. In addition to these exercises, at the end of the chapter, you will find self-check quizzes and extra drills to challenge you, to take what you learned to the next level. To summarize, this book will equip you with step-by-step instructions through rigorous tasks, practical callouts, and various real-world examples and assignments to reinforce your understanding of Tableau 10. Style and approach A fast paced book filled with highly-effective real-world examples to help you build new things and help you in solving problems in newer and unseen ways.

Pipelines can be challenging to manage, especially when your data has to flow through a collection of application components, servers, and cloud services. Airflow lets you schedule, restart, and backfill pipelines, and its easy-to-use UI and workflows with Python scripting has users praising its incredible flexibility. Data Pipelines with Apache Airflow takes you through best practices for creating pipelines for multiple tasks, including data lakes, cloud deployments, and data science. Data Pipelines with Apache Airflow teaches you the ins-and-outs of the Directed Acyclic Graphs (DAGs) that power Airflow, and how to write your own DAGs to meet the needs of your projects. With complete coverage of both foundational and lesser-known features, when you're done you'll be set to start using Airflow for seamless data pipeline development and management. Key Features Framework foundation and best practices Airflow's execution and dependency system Testing Airflow DAGs Running Airflow in production For data-savvy developers, DevOps and data engineers, and system administrators with intermediate Python skills. About the technology Data pipelines are used to extract, transform and load data to and from multiple sources, routing it wherever it's needed -- whether that's visualisation tools, business intelligence dashboards, or machine learning models. Airflow streamlines the whole process, giving you one tool for programmatically developing and monitoring batch data pipelines, and integrating all the pieces you use in your data stack. Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies including Heineken, Unilever, and Booking.com. Bas is a committer, and both Bas and Julian are active contributors to Apache Airflow.

Unleash the data processing and analytics capability of Apache Spark with the language of choice: Java About This Book * Perform big data processing with Spark-without having to learn Scala! * Use the Spark Java API to implement efficient enterprise-grade applications for data processing and analytics * Go beyond mainstream data processing by adding querying capability, Machine Learning, and graph processing using Spark Who This Book Is For If you are a Java developer interested in learning to use the popular Apache Spark framework, this book is the resource you need to get started. Apache Spark developers who are looking to build enterprise-grade applications in Java will also find this book very useful. What You Will Learn * Process data using different file formats such as XML, JSON, CSV, and plain and delimited text, using the Spark core Library. * Perform analytics on data from various data sources such as Kafka, and Flume using Spark Streaming Library * Learn SQL schema creation and the analysis of structured data using various SQL functions including Windowing functions in the Spark SQL Library * Explore Spark Mlib APIs while implementing Machine Learning techniques to solve real-world problems * Get to know Spark GraphX so you understand various graph-based analytics that can be performed with Spark In Detail 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. Style and approach This practical guide teaches readers the fundamentals of the Apache Spark framework and how to implement components using the Java language. It is a unique blend of theory and practical examples, and is written in a way that will gradually build your knowledge of Apache Spark.

This book collects research works of data-driven medical diagnosis done via Artificial Intelligence based solutions, such as Machine Learning, Deep Learning and Intelligent Optimization. Physical devices powered with Artificial Intelligence are gaining importance in diagnosis and healthcare. Medical data from different sources can also be analyzed via Artificial Intelligence techniques for more effective results.

Exploit the visualization capabilities of Kibana and build powerful interactive dashboards About This Book * Introduction to data-driven architecture and the Elastic stack * Build effective dashboards for data visualization and explore datasets with Elastic Graph * A comprehensive guide to learning scalable data visualization techniques in Kibana Who This Book Is For If you are a developer, data visualization engineer, or data scientist who wants to get the best of data visualization at scale then this book is perfect for you. A basic understanding of Elasticsearch and Logstash is required to make the best use of this book. What You Will Learn * How to create visualizations in Kibana * Ingest log data, structure an Elasticsearch cluster, and create visualization assets in Kibana * Embed Kibana visualization on web pages * Scaffold, develop, and deploy new Kibana & Timelion customizations * Build a metrics dashboard in Timelion based on time series data * Use the Graph plugin visualization feature and leverage a graph query * Create, implement, package, and deploy a new custom plugin * Use Prelert to solve anomaly detection challenges In Detail Kibana is an open source data visualization platform that allows you to interact with your data through stunning, powerful graphics. Its simple, browser-based interface enables you to quickly create and share dynamic dashboards that display changes to Elasticsearch queries in real time. In this book, you'll learn how to use the Elastic stack on top of a data architecture to visualize data in real time. All data architectures have different requirements and expectations when it comes to visualizing the data, whether it's logging analytics, metrics, business analytics, graph analytics, or scaling them as per your business requirements. This book will help you master Elastic visualization tools and adapt them to the requirements of your project. You will start by learning how to use the basic visualization features of Kibana 5. Then you will be shown how to implement a pure metric analytics architecture and visualize it using Timelion, a very recent and trendy feature of the Elastic stack. You will learn how to correlate data using the brand-new Graph visualization and build relationships between documents. Finally, you will be familiarized with the setup of a Kibana development environment so that you can build a custom Kibana plugin. By the end of this book you will have all the information needed to take your Elastic stack skills to a new level of data visualization. Style and approach This book takes a comprehensive, step-by-step approach to working with the visualization aspects of the Elastic stack. Every concept is presented in a very easy-to-follow manner that shows you both the logic and method of implementation. Real world cases are referenced to highlight how each of the key concepts can be put to practical use.

This digital electronics text focuses on "how to" design, build, operate and adapt data acquisition systems. The material begins with basic logic gates and ends with a 40 KHz voltage measurer. The approach aims to cover a minimal number of topics in detail. The data acquisition circuits described communicate with a host computer through parallel I/O ports. The fundamental idea of the book is that parallel I/O ports (available for all popular computers) offer a superior balance of simplicity, low cost, speed, flexibility and adaptability. All circuits and software are thoroughly tested. Construction details and troubleshooting guidelines are included. This book is intended to serve people who teach or study one of the following: digital electronics, circuit design, software that interacts outside hardware, the process of computer based acquisition, and the design, adaptation, construction and testing of measurement systems.

Over insightful 90 recipes to get lightning-fast analytics with Apache Spark About This Book * Use Apache Spark for data processing with these hands-on recipes * Implement end-to-end, large-scale data analysis better than ever before * Work with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your data Who This Book Is For This book is for novice and intermediate level data science professionals and data analysts who want to solve data science problems with a distributed computing framework. Basic experience with data science implementation tasks is expected. Data science professionals looking to skill up and gain an edge in the field will find this book helpful. What You Will Learn * Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning. * Solve real-world analytical problems with large data sets. * Address data science challenges with analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale. * Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package. * Learn about numerical and scientific computing using NumPy and SciPy on Spark. * Use Predictive Model Markup Language (PMML) in Spark for statistical data mining models. In Detail 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. Style and approach This book contains a comprehensive range of recipes designed to help you learn the fundamentals and tackle the difficulties of data science. This book outlines practical steps to produce powerful insights into Big Data through a recipe-based approach.

Einleitung.- I. Web 2.0 = Kommunikation 2.0?.- Web 2.0 - Einfluss auf Kommunikation und Marke.- Mehr gewinnen als verlieren.- Einfluss auf Mitarbeiter und einzelne Geschaftsbereiche.- Der Faktor Vertrauen.- Kommunikationsqualitat in sozialen Medien als Unternehmenskultuelement.- Chancen und Risiken im Unternehmenseinsatz.- Bildkommunikation - Visualisierung von Brands.- II. Social Media in der Praxis.- "In drei Jahren weg von der E-Mail" - Wie kunftig in Unternehmen kommuniziert wird.- Social Security - Gefahren auf Facebook & Co.- Social Media @ IT Governance.- Analyst Relations im Web 2.0-Zeitalter.- Social Media: "... we can't rewind we've gone too far...".- Im Web 2.0-Zeitalter mussen Unternehmn selbst zu Medien werden.- Social Media Measurement.- Appendix 1: Branchenbeispiele.- Abhangigkeit von Branchen.- IT-Services / System-Integration.- Social Media in pharmazeutischen Unternehmen.- Appendix 2: Internaitionale Entwicklungen.- Im Social Web der Mitte.- Die Autoren.- Index.

All social and policy researchers need to synthesize data into a visual representation. Producing good visualizations combines creativity and technique. This book teaches the techniques and basics to produce a variety of visualizations, allowing readers to communicate data and analyses in a creative and effective way. Visuals for tables, time series, maps, text, and networks are carefully explained and organized, showing how to choose the right plot for the type of data being analysed and displayed. Examples are drawn from public policy, public safety, education, political tweets, and public health. The presentation proceeds step by step, starting from the basics, in the programming languages R and Python so that readers learn the coding skills while simultaneously becoming familiar with the advantages and disadvantages of each visualization. No prior knowledge of either Python or R is required. Code for all the visualizations are available from the book's website.

Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists.

This self-contained tutorial presents a unified treatment of single- and multi-user problems in Shannon's information theory considering in particular the cases that depart from the requirement that the error probability decays asymptotically in the blocklength. Instead, the error probabilities for various problems are bounded above by a non-vanishing constant and the spotlight is shone on achievable coding rates as functions of the growing blocklengths. This represents the study of asymptotic estimates with non-vanishing error probabilities. Divided into three parts, the monograph begins with an introduction to binary hypothesis testing. From there the author develops the theme for point-to-point communication systems. Finally, Network Information Theory problems such as channels with random state, the multiple-encoder distributed lossless source coding (Slepian-Wolf) problem and special cases of the Gaussian interference and multiple-access channels are considered. The monograph is written in a didactic nature that makes it accessible for students, researchers and engineers building practical communication systems.

Gold Refining - by Donald Clark - This book covers the methods and systems of gold refining. Chapters Include - Occurrence of Native Gold - Refining Gold with Oxidising and Chloridising Agents - Sulfur Refining - Refining by Cementation Processes - Refining Gold Bullion by means of Oxygen - Miller's Process of Refining - Parting with Nitric Acid - Recovery of Silver from Nitrate Solutions - Refining by means of Sulphuric Acid - Parting gold by Electrolysis - Electrolytic Refining of Gold - The Treatment of Cyanide Precipitates - Other methods of refining gold slimes - The Nitre Cake method of Purifying Slimes - and more

Every day, more and more kinds of historical data become available, opening exciting new avenues of inquiry but also new challenges. This updated and expanded book describes and demonstrates the ways these data can be explored to construct cultural heritage knowledge, for research and in teaching and learning. It helps humanities scholars to grasp Big Data in order to do their work, whether that means understanding the underlying algorithms at work in search engines or designing and using their own tools to process large amounts of information.Demonstrating what digital tools have to offer and also what 'digital' does to how we understand the past, the authors introduce the many different tools and developing approaches in Big Data for historical and humanistic scholarship, show how to use them, what to be wary of, and discuss the kinds of questions and new perspectives this new macroscopic perspective opens up. Originally authored 'live' online with ongoing feedback from the wider digital history community, Exploring Big Historical Data breaks new ground and sets the direction for the conversation into the future.Exploring Big Historical Data should be the go-to resource for undergraduate and graduate students confronted by a vast corpus of data, and researchers encountering these methods for the first time. It will also offer a helping hand to the interested individual seeking to make sense of genealogical data or digitized newspapers, and even the local historical society who are trying to see the value in digitizing their holdings.

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.

This book is a digital electronics text focused on 'how to' design, build, operate and adapt data acquisition systems. The book is intended to serve people whose goals include teaching or learning one or more of the following: digital electronics, circuit design for computer expansion slots, software which interacts with outside hardware, the process of computer based data acquisition, and the design, adaptation, construction and testing of measurement systems. The fundamental idea of the book is that parallel I/O ports (available for all popular computers) offer a superior balance of simplicity, low cost, speed, flexibility and adaptability.