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Author: Sumit Pal
Publisher: Apress
ISBN: 1484222474
Category : Computers
Languages : en
Pages : 165
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Book Description
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
Author: Sumit Pal
Publisher: Apress
ISBN: 1484222474
Category : Computers
Languages : en
Pages : 165
Get Book
Book Description
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
Author: Benjamin Weissman
Publisher: Apress
ISBN: 1484259858
Category : Computers
Languages : en
Pages : 272
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Book Description
Use this guide to one of SQL Server 2019’s most impactful features—Big Data Clusters. You will learn about data virtualization and data lakes for this complete artificial intelligence (AI) and machine learning (ML) platform within the SQL Server database engine. You will know how to use Big Data Clusters to combine large volumes of streaming data for analysis along with data stored in a traditional database. For example, you can stream large volumes of data from Apache Spark in real time while executing Transact-SQL queries to bring in relevant additional data from your corporate, SQL Server database. Filled with clear examples and use cases, this book provides everything necessary to get started working with Big Data Clusters in SQL Server 2019. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You then are shown how to configure and deploy Big Data Clusters in on-premises environments or in the cloud. Next, you are taught about querying. You will learn to write queries in Transact-SQL—taking advantage of skills you have honed for years—and with those queries you will be able to examine and analyze data from a wide variety of sources such as Apache Spark. Through the theoretical foundation provided in this book and easy-to-follow example scripts and notebooks, you will be ready to use and unveil the full potential of SQL Server 2019: combining different types of data spread across widely disparate sources into a single view that is useful for business intelligence and machine learning analysis. What You Will LearnInstall, manage, and troubleshoot Big Data Clusters in cloud or on-premise environments Analyze large volumes of data directly from SQL Server and/or Apache Spark Manage data stored in HDFS from SQL Server as if it were relational data Implement advanced analytics solutions through machine learning and AI Expose different data sources as a single logical source using data virtualization Who This Book Is For Data engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environments
Author: Andreas Meier
Publisher: Springer
ISBN: 3658245492
Category : Computers
Languages : en
Pages : 229
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Book Description
This book offers a comprehensive introduction to relational (SQL) and non-relational (NoSQL) databases. The authors thoroughly review the current state of database tools and techniques, and examine coming innovations. The book opens with a broad look at data management, including an overview of information systems and databases, and an explanation of contemporary database types: SQL and NoSQL databases, and their respective management systems The nature and uses of Big Data A high-level view of the organization of data management Data Modeling and Consistency Chapter-length treatment is afforded Data Modeling in both relational and graph databases, including enterprise-wide data architecture, and formulas for database design. Coverage of languages extends from an overview of operators, to SQL and and QBE (Query by Example), to integrity constraints and more. A full chapter probes the challenges of Ensuring Data Consistency, covering: Multi-User Operation Troubleshooting Consistency in Massive Distributed Data Comparison of the ACID and BASE consistency models, and more System Architecture also gets from its own chapter, which explores Processing of Homogeneous and Heterogeneous Data; Storage and Access Structures; Multi-dimensional Data Structures and Parallel Processing with MapReduce, among other topics. Post-Relational and NoSQL Databases The chapter on post-relational databases discusses the limits of SQL – and what lies beyond, including Multi-Dimensional Databases, Knowledge Bases and and Fuzzy Databases. A final chapter covers NoSQL Databases, along with Development of Non-Relational Technologies, Key-Value, Column-Family and Document Stores XML Databases and Graphic Databases, and more The book includes more than 100 tables, examples and illustrations, and each chapter offers a list of resources for further reading. SQL & NoSQL Databases conveys the strengths and weaknesses of relational and non-relational approaches, and shows how to undertake development for big data applications. The book benefits readers including students and practitioners working across the broad field of applied information technology. This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland.
Author: Bob Ward
Publisher: Apress
ISBN: 1484254198
Category : Computers
Languages : en
Pages : 435
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Book Description
Get up to speed on the game-changing developments in SQL Server 2019. No longer just a database engine, SQL Server 2019 is cutting edge with support for machine learning (ML), big data analytics, Linux, containers, Kubernetes, Java, and data virtualization to Azure. This is not a book on traditional database administration for SQL Server. It focuses on all that is new for one of the most successful modernized data platforms in the industry. It is a book for data professionals who already know the fundamentals of SQL Server and want to up their game by building their skills in some of the hottest new areas in technology. SQL Server 2019 Revealed begins with a look at the project's team goal to integrate the world of big data with SQL Server into a major product release. The book then dives into the details of key new capabilities in SQL Server 2019 using a “learn by example” approach for Intelligent Performance, security, mission-critical availability, and features for the modern developer. Also covered are enhancements to SQL Server 2019 for Linux and gain a comprehensive look at SQL Server using containers and Kubernetes clusters. The book concludes by showing you how to virtualize your data access with Polybase to Oracle, MongoDB, Hadoop, and Azure, allowing you to reduce the need for expensive extract, transform, and load (ETL) applications. You will then learn how to take your knowledge of containers, Kubernetes, and Polybase to build a comprehensive solution called Big Data Clusters, which is a marquee feature of 2019. You will also learn how to gain access to Spark, SQL Server, and HDFS to build intelligence over your own data lake and deploy end-to-end machine learning applications. What You Will LearnImplement Big Data Clusters with SQL Server, Spark, and HDFS Create a Data Hub with connections to Oracle, Azure, Hadoop, and other sourcesCombine SQL and Spark to build a machine learning platform for AI applicationsBoost your performance with no application changes using Intelligent PerformanceIncrease security of your SQL Server through Secure Enclaves and Data ClassificationMaximize database uptime through online indexing and Accelerated Database RecoveryBuild new modern applications with Graph, ML Services, and T-SQL Extensibility with JavaImprove your ability to deploy SQL Server on Linux Gain in-depth knowledge to run SQL Server with containers and KubernetesKnow all the new database engine features for performance, usability, and diagnosticsUse the latest tools and methods to migrate your database to SQL Server 2019Apply your knowledge of SQL Server 2019 to Azure Who This Book Is For IT professionals and developers who understand the fundamentals of SQL Server and wish to focus on learning about the new, modern capabilities of SQL Server 2019. The book is for those who want to learn about SQL Server 2019 and the new Big Data Clusters and AI feature set, support for machine learning and Java, how to run SQL Server with containers and Kubernetes, and increased capabilities around Intelligent Performance, advanced security, and high availability.
Author: Antonio Badia
Publisher: Springer Nature
ISBN: 3030575926
Category : Computers
Languages : en
Pages : 290
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Book Description
This textbook explains SQL within the context of data science and introduces the different parts of SQL as they are needed for the tasks usually carried out during data analysis. Using the framework of the data life cycle, it focuses on the steps that are very often given the short shift in traditional textbooks, like data loading, cleaning and pre-processing. The book is organized as follows. Chapter 1 describes the data life cycle, i.e. the sequence of stages from data acquisition to archiving, that data goes through as it is prepared and then actually analyzed, together with the different activities that take place at each stage. Chapter 2 gets into databases proper, explaining how relational databases organize data. Non-traditional data, like XML and text, are also covered. Chapter 3 introduces SQL queries, but unlike traditional textbooks, queries and their parts are described around typical data analysis tasks like data exploration, cleaning and transformation. Chapter 4 introduces some basic techniques for data analysis and shows how SQL can be used for some simple analyses without too much complication. Chapter 5 introduces additional SQL constructs that are important in a variety of situations and thus completes the coverage of SQL queries. Lastly, chapter 6 briefly explains how to use SQL from within R and from within Python programs. It focuses on how these languages can interact with a database, and how what has been learned about SQL can be leveraged to make life easier when using R or Python. All chapters contain a lot of examples and exercises on the way, and readers are encouraged to install the two open-source database systems (MySQL and Postgres) that are used throughout the book in order to practice and work on the exercises, because simply reading the book is much less useful than actually using it. This book is for anyone interested in data science and/or databases. It just demands a bit of computer fluency, but no specific background on databases or data analysis. All concepts are introduced intuitively and with a minimum of specialized jargon. After going through this book, readers should be able to profitably learn more about data mining, machine learning, and database management from more advanced textbooks and courses.
Author: Benjamin Weissman
Publisher: Apress
ISBN: 1484251105
Category : Computers
Languages : en
Pages : 255
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Book Description
Get a head-start on learning one of SQL Server 2019’s latest and most impactful features—Big Data Clusters—that combines large volumes of non-relational data for analysis along with data stored relationally inside a SQL Server database. This book provides a first look at Big Data Clusters based upon SQL Server 2019 Release Candidate 1. Start now and get a jump on your competition in learning this important new feature. Big Data Clusters is a feature set covering data virtualization, distributed computing, and relational databases and provides a complete AI platform across the entire cluster environment. This book shows you how to deploy, manage, and use Big Data Clusters. For example, you will learn how to combine data stored on the HDFS file system together with data stored inside the SQL Server instances that make up the Big Data Cluster. Filled with clear examples and use cases, this book provides everything necessary to get started working with Big Data Clusters in SQL Server 2019 using Release Candidate 1. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You then are shown how to configure and deploy Big Data Clusters in on-premises environments or in the cloud. Next, you are taught about querying. You will learn to write queries in Transact-SQL—taking advantage of skills you have honed for years—and with those queries you will be able to examine and analyze data from a wide variety of sources such as Apache Spark. Through the theoretical foundation provided in this book and easy-to-follow example scripts and notebooks, you will be ready to use and unveil the full potential of SQL Server 2019: combining different types of data spread across widely disparate sources into a single view that is useful for business intelligence and machine learning analysis. What You Will LearnInstall, manage, and troubleshoot Big Data Clusters in cloud or on-premise environments Analyze large volumes of data directly from SQL Server and/or Apache Spark Manage data stored in HDFS from SQL Server as if it were relational data Implement advanced analytics solutions through machine learning and AI Expose different data sources as a single logical source using data virtualization Who This Book Is For For data engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environment
Author: Cathy Tanimura
Publisher: "O'Reilly Media, Inc."
ISBN: 1492088730
Category : Computers
Languages : en
Pages : 360
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Book Description
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
Author: Shabbir Challawala
Publisher: Packt Publishing Ltd
ISBN: 1788390423
Category : Computers
Languages : en
Pages : 291
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Book Description
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.
Author: James Lee
Publisher: Packt Publishing Ltd
ISBN: 1788626087
Category : Computers
Languages : en
Pages : 293
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Book Description
Solve all big data problems by learning how to create efficient data models Key FeaturesCreate effective models that get the most out of big dataApply your knowledge to datasets from Twitter and weather data to learn big dataTackle different data modeling challenges with expert techniques presented in this bookBook Description Modeling and managing data is a central focus of all big data projects. In fact, a database is considered to be effective only if you have a logical and sophisticated data model. This book will help you develop practical skills in modeling your own big data projects and improve the performance of analytical queries for your specific business requirements. To start with, you’ll get a quick introduction to big data and understand the different data modeling and data management platforms for big data. Then you’ll work with structured and semi-structured data with the help of real-life examples. Once you’ve got to grips with the basics, you’ll use the SQL Developer Data Modeler to create your own data models containing different file types such as CSV, XML, and JSON. You’ll also learn to create graph data models and explore data modeling with streaming data using real-world datasets. By the end of this book, you’ll be able to design and develop efficient data models for varying data sizes easily and efficiently. What you will learnGet insights into big data and discover various data modelsExplore conceptual, logical, and big data modelsUnderstand how to model data containing different file typesRun through data modeling with examples of Twitter, Bitcoin, IMDB and weather data modelingCreate data models such as Graph Data and Vector SpaceModel structured and unstructured data using Python and RWho this book is for This book is great for programmers, geologists, biologists, and every professional who deals with spatial data. If you want to learn how to handle GIS, GPS, and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful.
Author: Ajit Singh
Publisher: GRIN Verlag
ISBN: 3346079090
Category : Computers
Languages : en
Pages : 60
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Book Description
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.
