Tabular Modeling with SQL Server 2016 Analysis Services Cookbook. Create better operational analytics for your users with these business solutions

Derek Wilson

SQL Server Analysis Service (SSAS) has been widely used across multiple businesses to build smart online analytical reporting solutions. It includes two different types of modeling for analysis services: Tabular and Multi Dimensional. This book covers Tabular modeling, which uses tables and relationships with a fast in-memory engine to provide state of the art compression algorithms and query performance.The book begins by quickly taking you through the concepts required to model tabular data and set up the necessary tools and services. As you learn to create tabular models using tools such as Excel and Power View, you’ll be shown various strategies to deploy your model on the server and choose a query mode (In-memory or DirectQuery) that best suits your reporting needs. You’ll also learn how to implement key and newly introduced DAX functions to create calculated columns and measures for your model data. Last but not least, you’ll be shown techniques that will help you administer and secure your BI implementation along with some widely used tips and tricks to optimize your reporting solution.By the end of this book, you’ll have gained hands-on experience with the powerful new features that have been added to Tabular models in SSAS 2016 and you’ll be able to improve user satisfaction with faster reports and analytical queries.

Big Data Analytics with Hadoop 3. Build highly effective analytics solutions to gain valuable insight into your big data

Sridhar Alla

Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples.Once you have taken a tour of Hadoop 3’s latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases.By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly.

Hands-On Reactive Programming in Spring 5. Build cloud-ready, reactive systems with Spring 5 and Project Reactor

Oleh Dokuka, Igor Lozynskyi

These days, businesses need a new type of system that can remain responsive at all times. This is achievable with reactive programming; however, the development of these kinds of systems is a complex task, requiring a deep understanding of the domain. In order to develop highly responsive systems, the developers of the Spring Framework came up with Project Reactor.Hands-On Reactive Programming in Spring 5 begins with the fundamentals of Spring Reactive programming. You’ll explore the endless possibilities of building efficient reactive systems with the Spring 5 Framework along with other tools such as WebFlux and Spring Boot. Further on, you’ll study reactive programming techniques and apply them to databases and cross-server communication. You will advance your skills in scaling up Spring Cloud Streams and run independent, high-performant reactive microservices.By the end of the book, you will be able to put your skills to use and get on board with the reactive revolution in Spring 5.1!

WSO2 Developer's Guide. SOA and data services with WSO2 Enterprise Integrator

Ramón Garrido, Fidel Prieto Estrada

WSO2 Enterprise Integrator brings together the most powerful servers provided by the WSO2 company for your SOA infrastructure. As an Enterprise Service Bus (ESB), WSO2 Enterprise Integrator provides greater flexibility and agility to meet growing enterprise demands, whereas, as a Data Services Server (DSS), it provides an easy-to-use platform for integrating data stores, creating composite views across different data sources, and hosting data services. Using real-world scenarios, this book helps you build a solid foundation in developing enterprise applications with powerful data integration capabilities using the WSO2 servers.The book gets you started by brushing up your knowledge about SOA architecture and how it can be implemented through WSO2. It will help build your expertise with the core concepts of ESB such as building proxies, sequences, endpoints, and how to work with these in WSO2.Going further, you will also get well-acquainted with DSS data service concepts such as configuring data services, tasks, events, testing, and much more. The book will also cover API management techniques. Along with ESB and DSS, you will also learn about business process servers, the rules server and other components that together provide the control and robustness your enterprise applications will need. With practical use cases, the book covers typical daily scenarios you will come across while using these servers to give you hands-on experience.

Learning Einstein Analytics. Unlock critical insights with Salesforce Einstein Analytics

Santosh Tukaram Chitalkar

Salesforce Einstein analytics aka Wave Analytics is a cloud-based platform which connects data from the multiple sources and explores it to uncover insights. It empowers sales reps, marketers, and analysts with the insights to make customer interactions smarter, without building mathematical models. You will learn to create app, lenses, dashboards and share dashboards with other users.This book starts off with explaining you fundamental concepts like lenses, step, measures and sets you up with Einstein Analytics platform. We then move on to creating an app and here you will learn to create datasets, dashboards and different ways to import data into Analytics. Moving on we look at Einstein for sales, services, and marketing individually. Here you will learn to manage your pipeline, understand important business drivers and visualize trends. You will also learn features related to data monitoring tools and embedding dashboards with lightning, visualforce page and mobile devices. Further, you will learn advanced features pertaining to recent advancements in Einstein which include machine learning constructs and getting predictions for events. By the end of this book, you will become proficient in the Einstein analytics, getting insights faster and understanding your customer in a better way.

Hands-On Deep Learning with Apache Spark. Build and deploy distributed deep learning applications on Apache Spark

Guglielmo Iozzia

Deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep learning solutions can be implemented on Apache Spark.The book starts with the fundamentals of Apache Spark and deep learning. You will set up Spark for deep learning, learn principles of distributed modeling, and understand different types of neural nets. You will then implement deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) on Spark.As you progress through the book, you will gain hands-on experience of what it takes to understand the complex datasets you are dealing with. During the course of this book, you will use popular deep learning frameworks, such as TensorFlow, Deeplearning4j, and Keras to train your distributed models.By the end of this book, you'll have gained experience with the implementation of your models on a variety of use cases.

Mastering MongoDB 3.x. An expert's guide to building fault-tolerant MongoDB applications

Alex Giamas

MongoDB has grown to become the de facto NoSQL database with millions of users—from small startups to Fortune 500 companies. Addressing the limitations of SQL schema-based databases, MongoDB pioneered a shift of focus for DevOps and offered sharding and replication maintainable by DevOps teams. The book is based on MongoDB 3.x and covers topics ranging from database querying using the shell, built in drivers, and popular ODM mappers to more advanced topics such as sharding, high availability, and integration with big data sources.You will get an overview of MongoDB and how to play to its strengths, with relevant use cases. After that, you will learn how to query MongoDB effectively and make use of indexes as much as possible. The next part deals with the administration of MongoDB installations on-premise or in the cloud. We deal with database internals in the next section, explaining storage systems and how they can affect performance. The last section of this book deals with replication and MongoDB scaling, along with integration with heterogeneous data sources. By the end this book, you will be equipped with all the required industry skills and knowledge to become a certified MongoDB developer and administrator.

Learning Data Mining with Python. Use Python to manipulate data and build predictive models - Second Edition

Robert Layton

This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK. You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now.With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations.