Organizations striving to build applications for streaming data have a new possibility to ponder: the use of ingestion engines at the front end of their Hadoop systems. With this O’Reilly report, you’ll learn how these fast data front ends process data before it reaches the Hadoop Data File System (HDFS), and provide intelligence and context in real time. This helps you reduce response times from hours to minutes, or even minutes to seconds. Author and independent consultant Akmal Chaudhri looks at several popular ingestion engines, including Apache Spark, Apache Storm, and the VoltDB in-memory database. Among them, VoltDB stands out by providing full Atomicity, Consistency, Isolation, and Durability (ACID) support. VoltDB also lets you build a fast data front-end that uses the familiar SQL language and standards. Learn the advantages of ingestion engines as well as the theoretical and practical problems that can come up in an implementation. You’ll discover how this option can handle streaming data, provide state, ensure durability, and support transactions and real-time decisions. Akmal B. Chaudhri is an Independent Consultant, specializing in big data, NoSQL, and NewSQL database technologies. He has previously held roles as a developer, consultant, product strategist, and technical trainer with several blue-chip companies and big data startups. Akmal regularly presents at international conferences and serves on program committees for several major conferences and workshops.

Designed to help developers and administrators harness the full potential of MongoDB, this book provides clear instruction and practical guidance no matter your level. By exploring both fundamental aspects like installation and configuration, and advanced topics like using cloud services, this book serves as a comprehensive reference for anyone navigating the modern NoSQL database capabilities of MongoDB. What this Book will help me do Understand how to install and configure MongoDB for different environments, enabling efficient setup and operation. Master database administration skills, including monitoring and backup strategies, which are essential for stability and performance. Develop applications with MongoDB using Java and Python, allowing integration into modern tech stacks. Leverage advanced querying and indexing techniques, improving data retrieval and operational efficiency. Integrate MongoDB with cloud platforms and tools like Hadoop, enhancing scalability and expanded use cases. Author(s) None Dasadia and None Nayak are seasoned database professionals with extensive experience in MongoDB and NoSQL database systems. Their practical approach to technical writing focuses on real-world applications and providing solutions to complex challenges. With backgrounds in software development and data management, they ensure that readers have a hands-on learning experience. Their passion for spreading knowledge makes this book both instructional and engaging. Who is it for? This book is ideal for database administrators and software developers interested in adopting or expanding their knowledge of MongoDB. If you're a complete novice or someone with experience who seeks hands-on solutions and examples, this book offers value. It's particularly suited for professionals working with Java or Python, as examples focus on these programming languages. Whether you're enhancing your skills for personal projects or looking to implement MongoDB at work, this resource equips you with the know-how.

This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance. Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution. When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it’s often necessary to delegate the load to Hadoop or Spark and use the No-SQL to serve processed data in real time. This book shows you how to choose a relevant combination of big data technologies available within the Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern is illustrated with practical examples, which use the different open sourceprojects such as Logstash, Spark, Kafka, and so on. Traditional data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book helps you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints imposed by dealing with the high throughput of Big data. Scalable Big Data Architecture is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a Big Data project and which tools to integrate into that pattern.

This is a book for enterprise architects, database administrators, and developers who need to understand the latest developments in database technologies. It is the book to help you choose the correct database technology at a time when concepts such as Big Data, NoSQL and NewSQL are making what used to be an easy choice into a complex decision with significant implications. The relational database (RDBMS) model completely dominated database technology for over 20 years. Today this "one size fits all" stability has been disrupted by a relatively recent explosion of new database technologies. These paradigm-busting technologies are powering the "Big Data" and "NoSQL" revolutions, as well as forcing fundamental changes in databases across the board. Deciding to use a relational database was once truly a no-brainer, and the various commercial relational databases competed on price, performance, reliability, and ease of use rather than on fundamental architectures. Today we are faced with choices between radically different database technologies. Choosing the right database today is a complex undertaking, with serious economic and technological consequences. Next Generation Databases demystifies today’s new database technologies. The book describes what each technology was designed to solve. It shows how each technology can be used to solve real word application and business problems. Most importantly, this book highlights the architectural differences between technologies that are the critical factors to consider when choosing a database platform for new and upcoming projects. Introduces the new technologies that have revolutionized the database landscape Describes how each technology can be used to solve specific application or business challenges Reviews the most popular new wave databases and how they use these new database technologies

Practical Guide to MongoDB: Architecting, Developing, and Administering MongoDB begins with a short introduction to the basics of NoSQL databases and then introduces readers to MongoDB—the leading document based NoSQL database, acquainting them step-by-step with all aspects of MongoDB. Practical Guide to MongoDB covers the data model, underlying architecture, coding with Mongo Shell, and administrating the MongoDB platform, among other topics. The book also provides clear guidelines and practical examples for architecting, developing, and deploying applications using the MongoDB platform. Database developers, architects, and administrators will find useful information covering all aspects of the MongoDB platform and how to put it to use practically. The "one-size-fits-all" thinking regarding traditional RDBMSs has been challenged in the last few years by the emergence of diversified NoSQL databases. More than 120 NoSQL databases are now available in the market, and the leader by far is MongoDB. With so many companies opting for MongoDB as their NoSQL database of choice, there's a need for a practical how-to combined with expert advice for getting the most out of the software. Practical Guide to MongoDB provides readers with: A solid understanding of NoSQL databases An understanding of how to get started with MongoDB Methodical coverage of the architecture, development, and administration of MongoDB A plethora of "How to’s" enabling you to use the technology most efficiently to solve the problems you face

Learn how to harness Python for business intelligence tasks with the 'Python Business Intelligence Cookbook.' This guide provides practical recipes that help transform raw data into actionable insights for better decision-making. From preparing and analyzing to visualizing data, you will acquire useful skills for implementing efficient BI systems within your organization. What this Book will help me do Master installing and setting up tools like Anaconda and MongoDB for BI work. Prepare datasets by cleaning, standardizing, and extracting essential data. Use Pandas and NoSQL databases to analyze data and extract insights. Build business dashboards utilizing visualization tools like Matplotlib. Gain the ability to create complete BI systems for various business needs. Author(s) None Dempsey has extensive experience in Python programming and data analysis. With a passion for teaching and applied business intelligence, Dempsey writes in a straightforward and approachable style, making complex topics accessible to readers. The recipes compiled in this book are built to be both practical and intuitive. Who is it for? This book is ideal for data analysts, managers, and professionals who have a basic understanding of Python and want to apply it to business intelligence tasks. It's also helpful for those familiar with BI concepts looking to enhance or modernize their workflows with Python-based tools. If you're seeking to gain actionable insights from data in your business, this book is for you.

The Definitive Guide to MongoDB, Third Edition, is updated for MongoDB 3 and includes all of the latest MongoDB features, including the aggregation framework introduced in version 2.2 and hashed indexes in version 2.4. The Third Edition also now includes Node.js along with Python. MongoDB is the most popular of the "Big Data" NoSQL database technologies, and it's still growing. David Hows from 10gen, along with experienced MongoDB authors Peter Membrey and Eelco Plugge, provide their expertise and experience in teaching you everything you need to know to become a MongoDB pro.

This new edition is a hands-on guide for developers and administrators who want to use the power and flexibility of Couchbase Server 4.0 in their applications. The second edition extends coverage of N1QL, the SQL-like query language for Couchbase. It also brings coverage of multiple new features, including the new generation of client SDKs, security and LDAP integration, secondary indexes, and multi-dimensional scaling. Pro Couchbase Server covers everything you need to develop Couchbase solutions and deploy them in production. The NoSQL movement has fundamentally changed the database world in recent years. Influenced by the growing needs of web-scale applications, NoSQL databases such as Couchbase Server provide new approaches to scalability, reliability, and performance. Never have document databases been so powerful and performant. With the power and flexibility of Couchbase Server, you can model your data however you want, and easily change the data model any time you want. Pro Couchbase Server shows what is possible and helps you take full advantage of Couchbase Server and all the performance and scalability that it offers. • Helps you design and develop a document database using Couchbase Server. • Covers the latest features such as the N1QL query language. • Gives you the tools to scale out your application as needed.

Embark on your journey to mastering Couchbase with this comprehensive guide designed for learners of all levels. By exploring the fundamentals of NoSQL databases and diving into Couchbase's functionality, you'll gain the skills to design, manage, and scale modern applications effectively. Learn practical solutions and techniques to leverage Couchbase as a powerful backend system. What this Book will help me do Understand the core concepts of NoSQL databases and configure a Couchbase database system from scratch. Design efficient document data schemas and use Couchbase SDKs for high-performance application development. Explore the integration of Couchbase with Elasticsearch to implement robust full-text search capabilities. Master advanced Couchbase features like XDCR for disaster recovery and N1QL for SQL-like application queries. Develop and scale a real-world e-commerce application using Couchbase as the backend database system. Author(s) Henry Potsangbam is an experienced software developer and database specialist with a focus on scalable NoSQL solutions. He has worked extensively with Couchbase in developing real-world applications and is passionate about teaching others the intricacies of database systems. Henry's writing style makes advanced concepts accessible and practical for readers of all levels. Who is it for? This book is crafted for developers, database administrators, and IT professionals who want to learn NoSQL database basics and Couchbase's capabilities. Beginners with no prior experience in NoSQL will find step-by-step guidance, and experienced developers can expand their skill set to include Couchbase. A familiarity with Java programming will be helpful but is not mandatory.

SQL is full of difficulties and traps for the unwary. You can avoid them if you understand relational theory, but only if you know how to put that theory into practice. In this book, Chris Date explains relational theory in depth, and demonstrates through numerous examples and exercises how you can apply it to your use of SQL. This third edition has been revised, extended, and improved throughout. Topics whose treatment has been expanded include data types and domains, table comparisons, image relations, aggregate operators and summarization, view updating, and subqueries. A special feature of this edition is a new appendix on NoSQL and relational theory.

Deliver continuous access to timely and accurate BI across your enterprise using the detailed information in this Oracle Press guide. Through clear explanations and practical examples, a team of Oracle experts shows how to assimilate data from disparate sources into a single, unified view. Find out how to transform data in real time, handle replication and migration, and deploy Oracle Data Integrator and Oracle GoldenGate. Oracle Data Integration: Tools for Harnessing Data offers complete coverage of the latest “big data” hardware and software solutions . · Efficiently move data both inside and outside an Oracle environment · Map sources to database fields using Data Merge and ETL · Export schema through transportable tablespaces and Oracle Data Pump · Capture and apply changes across heterogeneous systems with Oracle GoldenGate · Seamlessly exchange information between databases using Oracle Data Integrator · Correct errors and maximize quality through data cleansing and validation · Plan and execute successful Oracle Database migrations and replications · Handle high-volume transactions with Oracle Big Data Appliance, Oracle NoSQL, and third-party utilities

Cassandra Design Patterns is your guide to harnessing the full potential of Apache Cassandra's distributed database capabilities through advanced design practices. Whether you're migrating from an RDBMS or implementing scalable storage for big data, this book provides clear strategies, practical examples, and real-world use cases demonstrating effective design patterns. What this Book will help me do Learn to integrate Cassandra with existing RDBMS solutions, enabling hybrid data architecture. Understand and implement key design patterns for distributed, scalable databases. Master the transition from RDBMS or cache systems to Cassandra with minimal disruption. Dive into time-series and temporal data patterns unique to Cassandra's strengths. Apply learned design patterns directly to real-world big data scenarios for analytics. Author(s) Rajanarayanan Thottuvaikkatumana, the author of Cassandra Design Patterns, is an expert in distributed systems and holds extensive experience in designing and implementing big data solutions. His hands-on approach to Cassandra is evident throughout the book as he bridges theoretical knowledge with practical applications. Rajanarayanan's approachable writing style aims to make complex concepts accessible. Who is it for? This book is ideal for big data developers and system architects who are familiar with the basics of Cassandra and are looking to deepen their understanding of design patterns for robust applications. Readers should have experience with relational databases and desire to migrate or integrate these concepts with NoSQL systems. Whether you're building solutions for data scalability, high availability, or analytics, Cassandra Design Patterns positions itself as an essential resource.

Sams Teach Yourself Big Data Analytics with Microsoft HDInsight in 24 Hours In just 24 lessons of one hour or less, Sams Teach Yourself Big Data Analytics with Microsoft HDInsight in 24 Hours helps you leverage Hadoop’s power on a flexible, scalable cloud platform using Microsoft’s newest business intelligence, visualization, and productivity tools. This book’s straightforward, step-by-step approach shows you how to provision, configure, monitor, and troubleshoot HDInsight and use Hadoop cloud services to solve real analytics problems. You’ll gain more of Hadoop’s benefits, with less complexity–even if you’re completely new to Big Data analytics. Every lesson builds on what you’ve already learned, giving you a rock-solid foundation for real-world success. Practical, hands-on examples show you how to apply what you learn Quizzes and exercises help you test your knowledge and stretch your skills Notes and tips point out shortcuts and solutions Learn how to… Master core Big Data and NoSQL concepts, value propositions, and use cases Work with key Hadoop features, such as HDFS2 and YARN Quickly install, configure, and monitor Hadoop (HDInsight) clusters in the cloud Automate provisioning, customize clusters, install additional Hadoop projects, and administer clusters Integrate, analyze, and report with Microsoft BI and Power BI Automate workflows for data transformation, integration, and other tasks Use Apache HBase on HDInsight Use Sqoop or SSIS to move data to or from HDInsight Perform R-based statistical computing on HDInsight datasets Accelerate analytics with Apache Spark Run real-time analytics on high-velocity data streams Write MapReduce, Hive, and Pig programs Register your book at informit.com/register for convenient access to downloads, updates, and corrections as they become available.

Data Analysis in the Cloud introduces and discusses models, methods, techniques, and systems to analyze the large number of digital data sources available on the Internet using the computing and storage facilities of the cloud. Coverage includes scalable data mining and knowledge discovery techniques together with cloud computing concepts, models, and systems. Specific sections focus on map-reduce and NoSQL models. The book also includes techniques for conducting high-performance distributed analysis of large data on clouds. Finally, the book examines research trends such as Big Data pervasive computing, data-intensive exascale computing, and massive social network analysis. Introduces data analysis techniques and cloud computing concepts Describes cloud-based models and systems for Big Data analytics Provides examples of the state-of-the-art in cloud data analysis Explains how to develop large-scale data mining applications on clouds Outlines the main research trends in the area of scalable Big Data analysis

Pro MongoDB Development is a critical reference for anyone using MongoDB, a NoSQL database based on the BSON (binary JSON) document model. The book explores many aspects of implementing MongoDB in web applications, whether you are using Java, PHP, Ruby, and JavaScript. Noted expert Deepak Vohra walks you through accessing MongoDB databases with all these languages and working with various other technologies and databases. Vohra discusses using Java EE frameworks Kundera and Spring Data with MongoDB. You learn the nuts and bolts of migrating data from other NoSQL databases (Apache Cassandra and Couchbase) and from relational databases (Oracle Database). And, because NoSQL databases are commonly used with the Hadoop ecosystem, the book also covers using MongoDB with Apache Hive. Each chapter includes details about the software you need and hands on examples of working with MongoDB and these technologies so you know exactly what to do, whatever your MongoDB implementation requires.

This comprehensive guide introduces you to Amazon's DynamoDB, a NoSQL database designed for high scalability and performance. Using this book, you will learn how to build robust web and mobile applications on DynamoDB and integrate it seamlessly with other AWS services for a complete cloud solution. What this Book will help me do Understand the key design concepts of DynamoDB and leverage its performance and scalability in your projects. Learn best practices for operating and managing DynamoDB tables, including optimizing throughput and designing efficient indexes. Master techniques for securing data in DynamoDB, including encryption and access management approaches. Explore integration strategies with other AWS services such as S3, EMR, and Lambda, to develop complex, real-world applications. Learn cost-effective solutions and tips for managing DynamoDB usage to avoid unnecessary expenses while maximizing resources. Author(s) None Deshpande, an expert in AWS and NoSQL databases, brings years of practical experience and engineering best practices to this book. With a strong focus on clear and actionable insights, Deshpande is dedicated to enabling developers to unlock the full potential of DynamoDB and related services for scalable application development. Who is it for? This book is most suited for developers and architects familiar with AWS who aim to deepen their understanding of DynamoDB. It is ideal for individuals looking to harness NoSQL databases for robust and scalable application solutions. The topics covered range from foundational knowledge to advanced integrations, making the book approachable yet comprehensive for both learners and seasoned practitioners.

In Beginning Big Data with Power BI and Excel 2013, you will learn to solve business problems by tapping the power of Microsoft’s Excel and Power BI to import data from NoSQL and SQL databases and other sources, create relational data models, and analyze business problems through sophisticated dashboards and data-driven maps. While Beginning Big Data with Power BI and Excel 2013 covers prominent tools such as Hadoop and the NoSQL databases, it recognizes that most small and medium-sized businesses don’t have the Big Data processing needs of a Netflix, Target, or Facebook. Instead, it shows how to import data and use the self-service analytics available in Excel with Power BI. As you’ll see through the book’s numerous case examples, these tools—which you already know how to use—can perform many of the same functions as the higher-end Apache tools many people believe are required to carry out in Big Data projects. Through instruction, insight, advice, and case studies, Beginning Big Data with Power BI and Excel 2013 will show you how to: Import and mash up data from web pages, SQL and NoSQL databases, the Azure Marketplace and other sources. Tap into the analytical power of PivotTables and PivotCharts and develop relational data models to track trends and make predictions based on a wide range of data. Understand basic statistics and use Excel with PowerBI to do sophisticated statistical analysis—including identifying trends and correlations. Use SQL within Excel to do sophisticated queries across multiple tables, including NoSQL databases. Create complex formulas to solve real-world business problems using Data Analysis Expressions (DAX).

The WWW era made billions of people dramatically dependent on the progress of data technologies, out of which Internet search and Big Data are arguably the most notable. Structured Search paradigm connects them via a fundamental concept of key-objects evolving out of keywords as the units of search. The key-object data model and KeySQL revamp the data independence principle making it applicable for Big Data and complement NoSQL with full-blown structured querying functionality. The ultimate goal is extracting Big Information from the Big Data. As a Big Data Consultant, Mikhail Gilula combines academic background with 20 years of industry experience in the database and data warehousing technologies working as a Sr. Data Architect for Teradata, Alcatel-Lucent, and PayPal, among others. He has authored three books, including The Set Model for Database and Information Systems and holds four US Patents in Structured Search and Data Integration. Conceptualizes structured search as a technology for querying multiple data sources in an independent and scalable manner. Explains how NoSQL and KeySQL complement each other and serve different needs with respect to big data Shows the place of structured search in the internet evolution and describes its implementations including the real-time structured internet search

What is JavaScript Object Notation (JSON) and how can you put it to work? This concise guide helps busy IT professionals get up and running quickly with this popular data interchange format, and provides a deep understanding of how JSON works. Author Lindsay Bassett begins with an overview of JSON syntax, data types, formatting, and security concerns before exploring the many ways you can apply JSON today. From Web APIs and server-side language libraries to NoSQL databases and client-side frameworks, JSON has emerged as a viable alternative to XML for exchanging data between different platforms. If you have some programming experience and understand HTML and JavaScript, this is your book. Learn why JSON syntax represents data in name-value pairs Explore JSON data types, including object, string, number, and array Find out how you can combat common security concerns Learn how the JSON schema verifies that data is formatted correctly Examine the relationship between browsers, web APIs, and JSON Understand how web servers can both request and create data Discover how jQuery and other client-side frameworks use JSON Learn why the CouchDB NoSQL database uses JSON to store data

Pro Couchbase Development: A NoSQL Platform for the Enterprise discusses programming for Couchbase using Java and scripting languages, querying and searching, handling migration, and integrating Couchbase with Hadoop, HDFS, and JSON. It also discusses migration from other NoSQL databases like MongoDB. This book is for big data developers who use Couchbase NoSQL database or want to use Couchbase for their web applications as well as for those migrating from other NoSQL databases like MongoDB and Cassandra. For example, a reason to migrate from Cassandra is that it is not based on the JSON document model with support for a flexible schema without having to define columns and supercolumns. The target audience is largely Java developers but the book also supports PHP and Ruby developers who want to learn about Couchbase. The author supplies examples in Java, PHP, Ruby, and JavaScript. After reading and using this hands-on guide for developing with Couchbase, you'll be able to build complex enterprise, database and cloud applications that leverage this powerful platform.