Oracle And Open Source
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Oracle and Open Source
The first book to tie together the commercial world of Oracle and the free-wheeling world of open source software, this guide describes nearly 100 open source tools, from the wide applied (Linux, Apache) to the Oracle-specific (Orasoft, Orac). Readers learn where to get them, their advantages to Oracle developers and DBAs, and how to create and release new open source Oracle tools.
Data Science Using Oracle Data Miner and Oracle R Enterprise
Automate the predictive analytics process using Oracle Data Miner and Oracle R Enterprise. This book talks about how both these technologies can provide a framework for in-database predictive analytics. You'll see a unified architecture and embedded workflow to automate various analytics steps such as data preprocessing, model creation, and storing final model output to tables. You'll take a deep dive into various statistical models commonly used in businesses and how they can be automated for predictive analytics using various SQL, PLSQL, ORE, ODM, and native R packages. You'll get to know various options available in the ODM workflow for driving automation. Also, you'll get an understanding of various ways to integrate ODM packages, ORE, and native R packages using PLSQL for automating the processes. Data Science Automation Using Oracle Data Miner and Oracle R Enterprise starts with an introduction to business analytics, covering why automation is necessary and the level of complexity in automation at each analytic stage. Then, it focuses on how predictive analytics can be automated by using Oracle Data Miner and Oracle R Enterprise. Also, it explains when and why ODM and ORE are to be used together for automation. The subsequent chapters detail various statistical processes used for predictive analytics such as calculating attribute importance, clustering methods, regression analysis, classification techniques, ensemble models, and neural networks. In these chapters you will also get to understand the automation processes for each of these statistical processes using ODM and ORE along with their application in a real-life business use case. What you'll learn Discover the functionality of Oracle Data Miner and Oracle R Enterprise Gain methods to perform in-database predictive analytics Use Oracle's SQL and PLSQL APIs for building analytical solutions Acquire knowledge ofcommon and widely-used business statistical analysis techniques Who this book is for IT executives, BI architects, Oracle architects and developers, R users and statisticians.
IBM vs Oracle
""IBM vs Oracle"" explores the intense competition between these two tech giants, focusing on how they shape business technology through cloud computing, artificial intelligence, and enterprise software. This book uniquely dissects their rivalry, highlighting their distinct strategies and impacts on digital transformation. For instance, IBM emphasizes hybrid cloud solutions and open-source initiatives, while Oracle focuses on database-centric cloud infrastructure. Understanding these approaches is crucial for navigating today's tech-driven business landscape. The book progresses by examining each company's historical context, tracing their evolution and foundational technologies. It contrasts IBM's hybrid cloud vision with Oracle's Gen2 Cloud Infrastructure and explores their AI initiatives, comparing IBM Watson's cognitive computing with Oracle's AI-driven applications. The book further evaluates their enterprise software portfolios, providing a comprehensive analysis of their rivalry's implications for businesses and best practices for technology selection.