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Sql Engines For Big Data Analytics
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Book Synopsis Data Analysis Using SQL and Excel by : Gordon S. Linoff
Download or read book Data Analysis Using SQL and Excel written by Gordon S. Linoff and published by John Wiley & Sons. This book was released on 2010-09-16 with total page 698 pages. Available in PDF, EPUB and Kindle. Book excerpt: Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like.
Book Synopsis Development Methodologies for Big Data Analytics Systems by : Manuel Mora
Download or read book Development Methodologies for Big Data Analytics Systems written by Manuel Mora and published by Springer Nature. This book was released on 2023-11-03 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents research in big data analytics (BDA) for business of all sizes. The authors analyze problems presented in the application of BDA in some businesses through the study of development methodologies based on the three approaches – 1) plan-driven, 2) agile and 3) hybrid lightweight. The authors first describe BDA systems and how they emerged with the convergence of Statistics, Computer Science, and Business Intelligent Analytics with the practical aim to provide concepts, models, methods and tools required for exploiting the wide variety, volume, and velocity of available business internal and external data - i.e. Big Data – and provide decision-making value to decision-makers. The book presents high-quality conceptual and empirical research-oriented chapters on plan-driven, agile, and hybrid lightweight development methodologies and relevant supporting topics for BDA systems suitable to be used for large-, medium-, and small-sized business organizations.
Book Synopsis SQL for Data Analysis by : Cathy Tanimura
Download or read book SQL for Data Analysis written by Cathy Tanimura and published by "O'Reilly Media, Inc.". This book was released on 2021-09-09 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the explosion of data, computing power, and cloud data warehouses, SQL has become an even more indispensable tool for the savvy analyst or data scientist. This practical book reveals new and hidden ways to improve your SQL skills, solve problems, and make the most of SQL as part of your workflow. You'll learn how to use both common and exotic SQL functions such as joins, window functions, subqueries, and regular expressions in new, innovative ways--as well as how to combine SQL techniques to accomplish your goals faster, with understandable code. If you work with SQL databases, this is a must-have reference. Learn the key steps for preparing your data for analysis Perform time series analysis using SQL's date and time manipulations Use cohort analysis to investigate how groups change over time Use SQL's powerful functions and operators for text analysis Detect outliers in your data and replace them with alternate values Establish causality using experiment analysis, also known as A/B testing
Book Synopsis Frank Kane's Taming Big Data with Apache Spark and Python by : Frank Kane
Download or read book Frank Kane's Taming Big Data with Apache Spark and Python written by Frank Kane and published by Packt Publishing Ltd. This book was released on 2017-06-30 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: Frank Kane's hands-on Spark training course, based on his bestselling Taming Big Data with Apache Spark and Python video, now available in a book. Understand and analyze large data sets using Spark on a single system or on a cluster. About This Book Understand how Spark can be distributed across computing clusters Develop and run Spark jobs efficiently using Python A hands-on tutorial by Frank Kane with over 15 real-world examples teaching you Big Data processing with Spark Who This Book Is For If you are a data scientist or data analyst who wants to learn Big Data processing using Apache Spark and Python, this book is for you. If you have some programming experience in Python, and want to learn how to process large amounts of data using Apache Spark, Frank Kane's Taming Big Data with Apache Spark and Python will also help you. What You Will Learn Find out how you can identify Big Data problems as Spark problems Install and run Apache Spark on your computer or on a cluster Analyze large data sets across many CPUs using Spark's Resilient Distributed Datasets Implement machine learning on Spark using the MLlib library Process continuous streams of data in real time using the Spark streaming module Perform complex network analysis using Spark's GraphX library Use Amazon's Elastic MapReduce service to run your Spark jobs on a cluster In Detail Frank Kane's Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you'll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python. Apache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses. Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease. Style and approach Frank Kane's Taming Big Data with Apache Spark and Python is a hands-on tutorial with over 15 real-world examples carefully explained by Frank in a step-by-step manner. The examples vary in complexity, and you can move through them at your own pace.
Download or read book SQL on Big Data written by Sumit Pal and published by Apress. This book was released on 2016-11-17 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn various commercial and open source products that perform SQL on Big Data platforms. You will understand the architectures of the various SQL engines being used and how the tools work internally in terms of execution, data movement, latency, scalability, performance, and system requirements. This book consolidates in one place solutions to the challenges associated with the requirements of speed, scalability, and the variety of operations needed for data integration and SQL operations. After discussing the history of the how and why of SQL on Big Data, the book provides in-depth insight into the products, architectures, and innovations happening in this rapidly evolving space. SQL on Big Data discusses in detail the innovations happening, the capabilities on the horizon, and how they solve the issues of performance and scalability and the ability to handle different data types. The book covers how SQL on Big Data engines are permeating the OLTP, OLAP, and Operational analytics space and the rapidly evolving HTAP systems. You will learn the details of: Batch Architectures—Understand the internals and how the existing Hive engine is built and how it is evolving continually to support new features and provide lower latency on queries Interactive Architectures—Understanding how SQL engines are architected to support low latency on large data sets Streaming Architectures—Understanding how SQL engines are architected to support queries on data in motion using in-memory and lock-free data structures Operational Architectures—Understanding how SQL engines are architected for transactional and operational systems to support transactions on Big Data platforms Innovative Architectures—Explore the rapidly evolving newer SQL engines on Big Data with innovative ideas and concepts Who This Book Is For: Business analysts, BI engineers, developers, data scientists and architects, and quality assurance professionals/div
Book Synopsis Big Data Analytics with Java by : Rajat Mehta
Download or read book Big Data Analytics with Java written by Rajat Mehta and published by Packt Publishing Ltd. This book was released on 2017-07-31 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the basics of analytics on big data using Java, machine learning and other big data tools About This Book Acquire real-world set of tools for building enterprise level data science applications Surpasses the barrier of other languages in data science and learn create useful object-oriented codes Extensive use of Java compliant big data tools like apache spark, Hadoop, etc. Who This Book Is For This book is for Java developers who are looking to perform data analysis in production environment. Those who wish to implement data analysis in their Big data applications will find this book helpful. What You Will Learn Start from simple analytic tasks on big data Get into more complex tasks with predictive analytics on big data using machine learning Learn real time analytic tasks Understand the concepts with examples and case studies Prepare and refine data for analysis Create charts in order to understand the data See various real-world datasets In Detail This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naive Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world. Style and approach The approach of book is to deliver practical learning modules in manageable content. Each chapter is a self-contained unit of a concept in big data analytics. Book will step by step builds the competency in the area of big data analytics. Examples using real world case studies to give ideas of real applications and how to use the techniques mentioned. The examples and case studies will be shown using both theory and code.
Book Synopsis Seven Databases in Seven Weeks by : Luc Perkins
Download or read book Seven Databases in Seven Weeks written by Luc Perkins and published by Pragmatic Bookshelf. This book was released on 2018-04-05 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data is getting bigger and more complex by the day, and so are your choices in handling it. Explore some of the most cutting-edge databases available - from a traditional relational database to newer NoSQL approaches - and make informed decisions about challenging data storage problems. This is the only comprehensive guide to the world of NoSQL databases, with in-depth practical and conceptual introductions to seven different technologies: Redis, Neo4J, CouchDB, MongoDB, HBase, Postgres, and DynamoDB. This second edition includes a new chapter on DynamoDB and updated content for each chapter. While relational databases such as MySQL remain as relevant as ever, the alternative, NoSQL paradigm has opened up new horizons in performance and scalability and changed the way we approach data-centric problems. This book presents the essential concepts behind each database alongside hands-on examples that make each technology come alive. With each database, tackle a real-world problem that highlights the concepts and features that make it shine. Along the way, explore five database models - relational, key/value, columnar, document, and graph - from the perspective of challenges faced by real applications. Learn how MongoDB and CouchDB are strikingly different, make your applications faster with Redis and more connected with Neo4J, build a cluster of HBase servers using cloud services such as Amazon's Elastic MapReduce, and more. This new edition brings a brand new chapter on DynamoDB, updated code samples and exercises, and a more up-to-date account of each database's feature set. Whether you're a programmer building the next big thing, a data scientist seeking solutions to thorny problems, or a technology enthusiast venturing into new territory, you will find something to inspire you in this book. What You Need: You'll need a *nix shell (Mac OS or Linux preferred, Windows users will need Cygwin), Java 6 (or greater), and Ruby 1.8.7 (or greater). Each chapter will list the downloads required for that database.
Book Synopsis Big Data Analytics with R and Hadoop by : Vignesh Prajapati
Download or read book Big Data Analytics with R and Hadoop written by Vignesh Prajapati and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics with R and Hadoop is a tutorial style book that focuses on all the powerful big data tasks that can be achieved by integrating R and Hadoop.This book is ideal for R developers who are looking for a way to perform big data analytics with Hadoop. This book is also aimed at those who know Hadoop and want to build some intelligent applications over Big data with R packages. It would be helpful if readers have basic knowledge of R.
Book Synopsis Readings in Database Systems by : Joseph M. Hellerstein
Download or read book Readings in Database Systems written by Joseph M. Hellerstein and published by MIT Press. This book was released on 2005 with total page 884 pages. Available in PDF, EPUB and Kindle. Book excerpt: The latest edition of a popular text and reference on database research, with substantial new material and revision; covers classical literature and recent hot topics. Lessons from database research have been applied in academic fields ranging from bioinformatics to next-generation Internet architecture and in industrial uses including Web-based e-commerce and search engines. The core ideas in the field have become increasingly influential. This text provides both students and professionals with a grounding in database research and a technical context for understanding recent innovations in the field. The readings included treat the most important issues in the database area--the basic material for any DBMS professional. This fourth edition has been substantially updated and revised, with 21 of the 48 papers new to the edition, four of them published for the first time. Many of the sections have been newly organized, and each section includes a new or substantially revised introduction that discusses the context, motivation, and controversies in a particular area, placing it in the broader perspective of database research. Two introductory articles, never before published, provide an organized, current introduction to basic knowledge of the field; one discusses the history of data models and query languages and the other offers an architectural overview of a database system. The remaining articles range from the classical literature on database research to treatments of current hot topics, including a paper on search engine architecture and a paper on application servers, both written expressly for this edition. The result is a collection of papers that are seminal and also accessible to a reader who has a basic familiarity with database systems.
Book Synopsis SQL Engines for Big Data Analytics by : Ajit Singh
Download or read book SQL Engines for Big Data Analytics written by Ajit Singh and published by GRIN Verlag. This book was released on 2019-12-09 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2018 in the subject Computer Science - Internet, New Technologies, grade: 8, , course: Master of Computer Application, language: English, abstract: This book aims to describe how data analytics works for big data and how they are used in business. It gives an overview of existing technologies and ap-proaches to building data analytics infrastructures. It also defines points that should be taken into consideration while choosing the most suitable software solution for a particular use case. The research is done by studying architectural principles of big data sys-tems and investigating the market of data analytics software. The result of this work is a composite report including comparison of several technologies and a list of criteria considered. The final report can be used as a guideline for choosing the most suitable technology for implementing an analytical platform in a broad variety of organizations. With a growing amount of data generated, their changing and evolving, the concept of big data has become incredibly popular in last years. It provides a set of new approaches and techniques allowing to work e ciently with huge volumes of records. Nowadays, information is one of the most important resources; it can help with decision making and business processes optimization. However, to get actual insights and unlock a potential of data, it is necessary to process them and discover the information hidden inside it which is a goal of data analytics. Data analytic platforms allow to manipulate with raw data in order to find out what exactly they contain. These systems are complex and includes multiple components therefore their designing requires comprehensive analysis of available options.
Book Synopsis Knowledge Graphs and Big Data Processing by : Valentina Janev
Download or read book Knowledge Graphs and Big Data Processing written by Valentina Janev and published by Springer Nature. This book was released on 2020-07-15 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.
Book Synopsis Big Data Processing with Apache Spark by : Srini Penchikala
Download or read book Big Data Processing with Apache Spark written by Srini Penchikala and published by Lulu.com. This book was released on 2018-03-13 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: Apache Spark is a popular open-source big-data processing framework thatÕs built around speed, ease of use, and unified distributed computing architecture. Not only it supports developing applications in different languages like Java, Scala, Python, and R, itÕs also hundred times faster in memory and ten times faster even when running on disk compared to traditional data processing frameworks. Whether you are currently working on a big data project or interested in learning more about topics like machine learning, streaming data processing, and graph data analytics, this book is for you. You can learn about Apache Spark and develop Spark programs for various use cases in big data analytics using the code examples provided. This book covers all the libraries in Spark ecosystem: Spark Core, Spark SQL, Spark Streaming, Spark ML, and Spark GraphX.
Book Synopsis Big Data Analytics by : Venkat Ankam
Download or read book Big Data Analytics written by Venkat Ankam and published by Packt Publishing Ltd. This book was released on 2016-09-28 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clusters About This Book This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools. Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming, DataFrame based ML Pipelines and SparkR. Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall. Who This Book Is For Though this book is primarily aimed at data analysts and data scientists, it will also help architects, programmers, and practitioners. Knowledge of either Spark or Hadoop would be beneficial. It is assumed that you have basic programming background in Scala, Python, SQL, or R programming with basic Linux experience. Working experience within big data environments is not mandatory. What You Will Learn Find out and implement the tools and techniques of big data analytics using Spark on Hadoop clusters with wide variety of tools used with Spark and Hadoop Understand all the Hadoop and Spark ecosystem components Get to know all the Spark components: Spark Core, Spark SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx See batch and real-time data analytics using Spark Core, Spark SQL, and Conventional and Structured Streaming Get to grips with data science and machine learning using MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall. In Detail Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data. Style and approach This step-by-step pragmatic guide will make life easy no matter what your level of experience. You will deep dive into Apache Spark on Hadoop clusters through ample exciting real-life examples. Practical tutorial explains data science in simple terms to help programmers and data analysts get started with Data Science
Book Synopsis MySQL 8 for Big Data by : Shabbir Challawala
Download or read book MySQL 8 for Big Data written by Shabbir Challawala and published by Packt Publishing Ltd. This book was released on 2017-10-20 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncover the power of MySQL 8 for Big Data About This Book Combine the powers of MySQL and Hadoop to build a solid Big Data solution for your organization Integrate MySQL with different NoSQL APIs and Big Data tools such as Apache Sqoop A comprehensive guide with practical examples on building a high performance Big Data pipeline with MySQL Who This Book Is For This book is intended for MySQL database administrators and Big Data professionals looking to integrate MySQL 8 and Hadoop to implement a high performance Big Data solution. Some previous experience with MySQL will be helpful, although the book will highlight the newer features introduced in MySQL 8. What You Will Learn Explore the features of MySQL 8 and how they can be leveraged to handle Big Data Unlock the new features of MySQL 8 for managing structured and unstructured Big Data Integrate MySQL 8 and Hadoop for efficient data processing Perform aggregation using MySQL 8 for optimum data utilization Explore different kinds of join and union in MySQL 8 to process Big Data efficiently Accelerate Big Data processing with Memcached Integrate MySQL with the NoSQL API Implement replication to build highly available solutions for Big Data In Detail With organizations handling large amounts of data on a regular basis, MySQL has become a popular solution to handle this structured Big Data. In this book, you will see how DBAs can use MySQL 8 to handle billions of records, and load and retrieve data with performance comparable or superior to commercial DB solutions with higher costs. Many organizations today depend on MySQL for their websites and a Big Data solution for their data archiving, storage, and analysis needs. However, integrating them can be challenging. This book will show you how to implement a successful Big Data strategy with Apache Hadoop and MySQL 8. It will cover real-time use case scenario to explain integration and achieve Big Data solutions using technologies such as Apache Hadoop, Apache Sqoop, and MySQL Applier. Also, the book includes case studies on Apache Sqoop and real-time event processing. By the end of this book, you will know how to efficiently use MySQL 8 to manage data for your Big Data applications. Style and approach Step by Step guide filled with real-world practical examples.
Book Synopsis The Internet of Things and Big Data Analytics by : Pethuru Raj
Download or read book The Internet of Things and Big Data Analytics written by Pethuru Raj and published by CRC Press. This book was released on 2020-06-07 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprehensively conveys the theoretical and practical aspects of IoT and big data analytics with the solid contributions from practitioners as well as academicians. This book examines and expounds the unique capabilities of the big data analytics platforms in capturing, cleansing and crunching IoT device/sensor data in order to extricate actionable insights. A number of experimental case studies and real-world scenarios are incorporated in this book in order to instigate our book readers. This book Analyzes current research and development in the domains of IoT and big data analytics Gives an overview of latest trends and transitions happening in the IoT data analytics space Illustrates the various platforms, processes, patterns, and practices for simplifying and streamlining IoT data analytics The Internet of Things and Big Data Analytics: Integrated Platforms and Industry Use Cases examines and accentuates how the multiple challenges at the cusp of IoT and big data can be fully met. The device ecosystem is growing steadily. It is forecast that there will be billions of connected devices in the years to come. When these IoT devices, resource-constrained as well as resource-intensive, interact with one another locally and remotely, the amount of multi-structured data generated, collected, and stored is bound to grow exponentially. Another prominent trend is the integration of IoT devices with cloud-based applications, services, infrastructures, middleware solutions, and databases. This book examines the pioneering technologies and tools emerging and evolving in order to collect, pre-process, store, process and analyze data heaps in order to disentangle actionable insights.
Book Synopsis Big Data Analytics with Spark by : Mohammed Guller
Download or read book Big Data Analytics with Spark written by Mohammed Guller and published by Apress. This book was released on 2015-12-29 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics with Spark is a step-by-step guide for learning Spark, which is an open-source fast and general-purpose cluster computing framework for large-scale data analysis. You will learn how to use Spark for different types of big data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. In addition, this book will help you become a much sought-after Spark expert. Spark is one of the hottest Big Data technologies. The amount of data generated today by devices, applications and users is exploding. Therefore, there is a critical need for tools that can analyze large-scale data and unlock value from it. Spark is a powerful technology that meets that need. You can, for example, use Spark to perform low latency computations through the use of efficient caching and iterative algorithms; leverage the features of its shell for easy and interactive Data analysis; employ its fast batch processing and low latency features to process your real time data streams and so on. As a result, adoption of Spark is rapidly growing and is replacing Hadoop MapReduce as the technology of choice for big data analytics. This book provides an introduction to Spark and related big-data technologies. It covers Spark core and its add-on libraries, including Spark SQL, Spark Streaming, GraphX, and MLlib. Big Data Analytics with Spark is therefore written for busy professionals who prefer learning a new technology from a consolidated source instead of spending countless hours on the Internet trying to pick bits and pieces from different sources. The book also provides a chapter on Scala, the hottest functional programming language, and the program that underlies Spark. You’ll learn the basics of functional programming in Scala, so that you can write Spark applications in it. What's more, Big Data Analytics with Spark provides an introduction to other big data technologies that are commonly used along with Spark, like Hive, Avro, Kafka and so on. So the book is self-sufficient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to know is programming in any language. There is a critical shortage of people with big data expertise, so companies are willing to pay top dollar for people with skills in areas like Spark and Scala. So reading this book and absorbing its principles will provide a boost—possibly a big boost—to your career.
Book Synopsis Scalable Big Data Architecture by : Bahaaldine Azarmi
Download or read book Scalable Big Data Architecture written by Bahaaldine Azarmi and published by Apress. This book was released on 2015-12-31 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
