Beginning Apache Spark Using Azure Databricks
Analyze vast amounts of data in record time using Apache Spark with Databricks in the Cloud. Learn the fundamentals, and more, of running analytics on large clusters in Azure and AWS, using Apache Spark with Databricks on top. Discover how to squeeze the most value out of your data at a mere fraction of what classical analytics solutions cost, while at the same time getting the results you need, incrementally faster.
This book explains how the confluence of these pivotal technologies gives you enormous power, and cheaply, when it comes to huge datasets. You will begin by learning how cloud infrastructure makes it possible to scale your code to large amounts of processing units, without having to pay for the machinery in advance. From there you will learn how Apache Spark, an open source framework, can enable all those CPUs for data analytics use. Finally, you will see how services such as Databricks provide the power of Apache Spark, without you having to ...
Spark in Action, 2nd Edition
The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, 2nd Edition, you'll learn to take advantage of Spark's core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Spark skills are a hot commodity in enterprises worldwide, and with Spark's powerful and flexible Java APIs, you can reap all the benefits without first learning Scala or Hadoop.
Analyzing enterprise data starts by reading, filtering, and merging files and streams from many sources. The Spark data processing engine handles this varied volume like a champ, delivering speeds 100 times faster than Hadoop systems. Thanks to SQL support, an intuitive interface, and a straightforward multilanguage API, you can use Spark without learning a complex new ecosystem.
Spark in Action, 2nd Edition, teaches you to create end-to-end analytics ...
Harness the power of PolyBase data virtualization software to make data from a variety of sources easily accessible through SQL queries while using the T-SQL skills you already know and have mastered.
PolyBase Revealed shows you how to use the PolyBase feature of SQL Server 2019 to integrate SQL Server with Azure Blob Storage, Apache Hadoop, other SQL Server instances, Oracle, Cosmos DB, Apache Spark, and more. You will learn how PolyBase can help you reduce storage and other costs by avoiding the need for ETL processes that duplicate data in order to make it accessible from one source. PolyBase makes SQL Server into that one source, and T-SQL is your golden ticket. The book also covers PolyBase scale-out clusters, allowing you to distribute PolyBase queries among several SQL Server instances, thus improving performance.
With great flexibility comes great complexity, and this book shows you where to look when queries fail, complete with coverage of internals, troubl ...
Pro Apache NetBeans
Take a detailed look at the NetBeans IDE and new features in the NetBeans Platform. Learn about support for JShell, the Jigsaw Module System, and Local Variable Type Inference, focusing on what this new version of NetBeans brings to developers who are working in Java and other supported languages. The book is a practical, hands-on guide providing a number of step-by-step recipes that help you take advantage of the power in the latest Java (and other) software platforms, and gives a good grounding on using NetBeans IDE for your projects. This book has been written by Apache community members who both use the IDE and actively contribute and develop Apache NetBeans as an open source project.
Pro Apache NetBeans consists of three parts. The first part describes how to use the IDE as well as the new features that it brings to support the latest Java versions. The second part describes how you can extend NetBeans by creating plugins and writing your own applications u ...
Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine learning models. With this practical guide,
developers and data scientists will discover how graph analytics deliver value, whether they're used for building dynamic network models or forecasting real-world behavior.
Mark Needham and Amy Hodler from Neo4j explain how graph algorithms describe complex structures and reveal difficult-to-find patterns - from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. You'll walk through hands-on examples that show you how to use graph algorithms in Apache Spark and Neo4j, two of the most common choices for graph analytics.
Learn how graph analytics reveal more predictive elements in today's data; Understand how popular graph algorithms work and how they're applied; Use sample code and tips from more than 20 graph algorithm examples; Learn wh ...
Programmer's Guide to Apache Thrift
Programmer's Guide to Apache Thrift provides comprehensive coverage of the Apache Thrift framework along with a developer's-eye view of modern distributed application architecture.
Programmer's Guide to Apache Thrift provides comprehensive coverage of distributed application communication using the Thrift framework. Packed with code examples and useful insight, this book presents best practices for multi-language distributed development. You'll take a guided tour through transports, protocols, IDL, and servers as you explore programs in C++, Java, and Python. You'll also learn how to work ...
Machine Learning with Apache Spark Quick Start Guide
Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently.
But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it?
Apache Kafka Quick Start Guide
Apache Kafka is a great open source platform for handling your real-time data pipeline to ensure high-speed filtering and pattern matching on the ﬂy. In this book, you will learn how to use Apache Kafka for efficient processing of distributed applications and will get familiar with solving everyday problems in fast data and processing pipelines.
This book focuses on programming rather than the configuration management of Kafka clusters or DevOps. It starts off with the installation and setting up the development environment, before quickly moving on to performing fundamental messaging operations such as validation and enrichment.
Here you will learn about message composition with pure Kafka API and Kafka Streams. You will look into the transformation of messages in different formats, such asext, binary, XML, JSON, and AVRO. Next, you will learn how to expose the schemas contained in Kafka with the Schema Registry. You will then learn how to work with all relevant ...
Apache Spark 2: Data Processing and Real-Time Analytics
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. ...
Apache Superset Quick Start Guide
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 geograph ...
Apache Ignite Quick Start Guide
Apache Ignite is a distributed in-memory platform designed to scale and process large volume of data. It can be integrated with microservices as well as monolithic systems, and can be used as a scalable, highly available and performant deployment platform for microservices. This book will teach you to use Apache Ignite for building a high-performance, scalable, highly available system architecture with data integrity.
The book takes you through the basics of Apache Ignite and in-memory technologies. You will learn about installation and clustering Ignite nodes, caching topologies, and various caching strategies, such as cache aside, read and write through, and write behind. Next, you will delve into detailed aspects of Ignite's data grid: web session clustering and querying data.
You will learn how to process large volumes of data using compute grid and Ignite's map-reduce and executor service. You will learn about the memory architecture of Apache Ign ...