Think big about your data! PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines. In Data Analysis with Python and PySpark you will learn how to: Manage your data as it scales across multiple machines Scale up your data programs with full confidence Read and write data to and from a variety of sources and formats Deal with messy data with PySpark’s data manipulation functionality Discover new data sets and perform exploratory data analysis Build automated data pipelines that transform, summarize, and get insights from data Troubleshoot common PySpark errors Creating reliable long-running jobs Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you’ve learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required. About the Technology The Spark data processing engine is an amazing analytics factory: raw data comes in, insight comes out. PySpark wraps Spark’s core engine with a Python-based API. It helps simplify Spark’s steep learning curve and makes this powerful tool available to anyone working in the Python data ecosystem. About the Book Data Analysis with Python and PySpark helps you solve the daily challenges of data science with PySpark. You’ll learn how to scale your processing capabilities across multiple machines while ingesting data from any source—whether that’s Hadoop clusters, cloud data storage, or local data files. Once you’ve covered the fundamentals, you’ll explore the full versatility of PySpark by building machine learning pipelines, and blending Python, pandas, and PySpark code. What's Inside Organizing your PySpark code Managing your data, no matter the size Scale up your data programs with full confidence Troubleshooting common data pipeline problems Creating reliable long-running jobs About the Reader Written for data scientists and data engineers comfortable with Python. About the Author As a ML director for a data-driven software company, Jonathan Rioux uses PySpark daily. He teaches the software to data scientists, engineers, and data-savvy business analysts. Quotes A clear and in-depth introduction for truly tackling big data with Python. - Gustavo Patino, Oakland University William Beaumont School of Medicine The perfect way to learn how to analyze and master huge datasets. - Gary Bake, Brambles Covers both basic and more advanced topics of PySpark, with a good balance between theory and hands-on. - Philippe Van Bergenl, P² Consulting For beginner to pro, a well-written book to help understand PySpark. - Raushan Kumar Jha, Microsoft

We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, author Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale. Dehghani guides practitioners, architects, technical leaders, and decision makers on their journey from traditional big data architecture to a distributed and multidimensional approach to analytical data management. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance. Get a complete introduction to data mesh principles and its constituents Design a data mesh architecture Guide a data mesh strategy and execution Navigate organizational design to a decentralized data ownership model Move beyond traditional data warehouses and lakes to a distributed data mesh

This IBM® RedpaperRedbooks® publication describes the concepts, architecture, and implementation of the IBM DS8900F family. The WhitepaperRedpaperbook provides reference information to assist readers who need to plan for, install, and configure the DS8900F systems. This edition applies to DS8900F systems with IBM DS8000® Licensed Machine Code (LMC) 7.9.20 (bundle version 89.20.xx.x), referred to as Release 9.2. The DS8900F is an all-flash system exclusively, and it offers three classes: DS8980F: Analytic Class: The DS8980F Analytic Class offers best performance for organizations that want to expand their workload possibilities to artificial intelligence (AI), Business Intelligence (BI), and machine learning (ML). IBM DS8950F: Agility Class all-flash: The Agility Class consolidates all your mission-critical workloads for IBM Z®, IBM LinuxONE, IBM Power Systems, and distributed environments under a single all-flash storage solution.. IBM DS8910F: Flexibility Class all-flash: The Flexibility Class reduces complexity while addressing various workloads at the lowest DS8900F family entry cost. . TThe DS8900F architecture relies on powerful IBM POWER9™ processor-based servers that manage the cache to streamline disk input/output (I/O), which maximizes performance and throughput. These capabilities are further enhanced by High-Performance Flash Enclosures (HPFE) Gen2. Like its predecessors, the DS8900F supports advanced disaster recovery (DR) solutions, business continuity solutions, and thin provisioning. The IBM DS8910F Rack-Mounted model 993 is described in IBM DS8910F Model 993 Rack-Mounted Storage System Release 9.1, REDP-5566.

Artificial intelligence (AI) enables computers and machines to mimic the perception, learning, problem-solving, and decision-making capabilities of the human mind. AI development is made possible by the availability of large amounts of data and the corresponding development and wide availability of computer systems that can process all that data faster and more accurately than humans can. What happens if you infuse AI with a world-class database management system, such as IBM Db2®? IBM® has done just that with Db2 AI for z/OS (Db2ZAI). Db2ZAI is built to infuse AI and data science to assist businesses in the use of AI to develop applications more easily. With Db2ZAI, the following benefits are realized: Data science functionality Better built applications Improved database performance (and DBA's time and efforts are saved) through simplification and automation of error reporting and routine tasks Machine learning (ML) optimizer to improve query access paths and reduce the need for manual tuning and query optimization Integrated data access that makes data available from various vendors including private cloud providers. This IBM Redpaper® publication helps to simplify your installation by tailoring and configuration of Db2 AI for z/OS®. It was written for system programmers, system administrators, and database administrators.

Discover how to effectively build and manage data engineering pipelines using AWS with "Data Engineering with AWS". In this hands-on book, you'll explore the foundational principles of data engineering, learn to architect data pipelines, and work with essential AWS services to process, transform, and analyze data. What this Book will help me do Understand and implement modern data engineering pipelines with AWS services. Gain proficiency in automating data ingestion and transformation using Amazon tools. Perform efficient data queries and analysis leveraging Amazon Athena and Redshift. Create insightful data visualizations using Amazon QuickSight. Apply machine learning techniques to enhance data engineering processes. Author(s) None Eagar, a Senior Data Architect with over twenty-five years of experience, specializes in modern data architectures and cloud solutions. With a rich background in applying data engineering to real-world problems, None Eagar shares expertise in a clear and approachable way for readers. Who is it for? This book is perfect for data engineers and data architects aiming to grow their expertise in AWS-based solutions. It's also geared towards beginners in data engineering wanting to adopt the best practices. Those with a basic understanding of big data and cloud platforms will find it particularly valuable, but prior AWS experience is not required.

Master the new features in PySpark 3.1 to develop data-driven, intelligent applications. This updated edition covers topics ranging from building scalable machine learning models, to natural language processing, to recommender systems. Machine Learning with PySpark, Second Edition begins with the fundamentals of Apache Spark, including the latest updates to the framework. Next, you will learn the full spectrum of traditional machine learning algorithm implementations, along with natural language processing and recommender systems. You’ll gain familiarity with the critical process of selecting machine learning algorithms, data ingestion, and data processing to solve business problems. You’ll see a demonstration of how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forests. You’ll also learn how to automate the steps using Spark pipelines, followed by unsupervised models such as K-means and hierarchical clustering. A section on Natural Language Processing (NLP) covers text processing, text mining, and embeddings for classification. This new edition also introduces Koalas in Spark and how to automate data workflow using Airflow and PySpark’s latest ML library. After completing this book, you will understand how to use PySpark’s machine learning library to build and train various machine learning models, along with related components such as data ingestion, processing and visualization to develop data-driven intelligent applications What you will learn: Build a spectrum of supervised and unsupervised machine learning algorithms Use PySpark's machine learning library to implement machine learning and recommender systems Leverage the new features in PySpark’s machine learning library Understand data processing using Koalas in Spark Handle issues around feature engineering, class balance, bias andvariance, and cross validation to build optimally fit models Who This Book Is For Data science and machine learning professionals.

Dive into the world of scalable data processing with 'Essential PySpark for Scalable Data Analytics'. This book is a comprehensive guide that helps beginners understand and utilize PySpark to process, analyze, and draw insights from large datasets effectively. With hands-on tutorials and clear explanations, you will gain the confidence to tackle big data analytics challenges. What this Book will help me do Understand and apply the distributed computing paradigm for big data. Learn to perform scalable data ingestion, cleansing, and preparation using PySpark. Create and utilize data lakes and the Lakehouse paradigm for efficient data storage and access. Develop and deploy machine learning models with scalability in mind. Master real-time analytics pipelines and create impactful data visualizations. Author(s) None Nudurupati is an experienced data engineer and educator, specializing in distributed systems and big data technologies. With years of practical experience in the field, None brings a clear and approachable teaching style to technical topics. Passionate about empowering readers, the author has designed this book to be both practical and inspirational for aspiring data practitioners. Who is it for? This book is ideal for data professionals including data scientists, engineers, and analysts looking to scale their data analytics processes. It assumes familiarity with basic data science concepts and Python, as well as some experience with SQL-like data analysis. This is particularly suitable for individuals aiming to expand their knowledge in distributed computing and PySpark to handle big data challenges. Achieving scalable and efficient data solutions is at the core of this guide.

This IBM® Redbooks Product Guide provides an overview of the features and functions that are available with the IBM DS8900F models that run microcode Release 9.2 (Bundle 89.20 / Licensed Machine Code 7.9.20). As of August 2021, the DS8900F with DS8000 Release 9.2 is the latest addition. The DS8900F is an all-flash system exclusively, and it offers three classes: IBM DS8980F: Analytic Class: The DS8980F Analytic Class offers best performance for organizations that want to expand their workload possibilities to artificial intelligence (AI), Business Intelligence, and Machine Learning. IBM DS8950F: Agility Class: The agility class is efficiently designed to consolidate all your mission-critical workloads for IBM Z, IBM LinuxONE, IBM Power Systems, and distributed environments under a single all-flash storage solution.. IBM DS8910F: Flexibility Class: The flexibility class delivers significant performance for midrange organizations that are looking to meet storage challenges with advanced functionality delivered as a single rack solution.

Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure. In Data Engineering on Azure you will learn how to: Pick the right Azure services for different data scenarios Manage data inventory Implement production quality data modeling, analytics, and machine learning workloads Handle data governance Using DevOps to increase reliability Ingesting, storing, and distributing data Apply best practices for compliance and access control Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning. About the Technology Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify. About the Book In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms. What's Inside Data inventory and data governance Assure data quality, compliance, and distribution Build automated pipelines to increase reliability Ingest, store, and distribute data Production-quality data modeling, analytics, and machine learning About the Reader For data engineers familiar with cloud computing and DevOps. About the Author Vlad Riscutia is a software architect at Microsoft. Quotes A definitive and complete guide on data engineering, with clear and easy-to-reproduce examples. - Kelum Prabath Senanayake, Echoworx An all-in-one Azure book, covering all a solutions architect or engineer needs to think about. - Albert Nogués, Danone A meaningful journey through the Azure ecosystem. You’ll be building pipelines and joining components quickly! - Todd Cook, Appen A gateway into the world of Azure for machine learning and DevOps engineers. - Krzysztof Kamyczek, Luxoft

Learn about business intelligence (BI) features in T-SQL and how they can help you with data science and analytics efforts without the need to bring in other languages such as R and Python. This book shows you how to compute statistical measures using your existing skills in T-SQL. You will learn how to calculate descriptive statistics, including centers, spreads, skewness, and kurtosis of distributions. You will also learn to find associations between pairs of variables, including calculating linear regression formulas and confidence levels with definite integration. No analysis is good without data quality. Advanced Analytics with Transact-SQL introduces data quality issues and shows you how to check for completeness and accuracy, and measure improvements in data quality over time. The book also explains how to optimize queries involving temporal data, such as when you search for overlapping intervals. More advanced time-oriented information in the book includes hazard and survival analysis. Forecasting with exponential moving averages and autoregression is covered as well. Every web/retail shop wants to know the products customers tend to buy together. Trying to predict the target discrete or continuous variable with few input variables is important for practically every type of business. This book helps you understand data science and the advanced algorithms use to analyze data, and terms such as data mining, machine learning, and text mining. Key to many of the solutions in this book are T-SQL window functions. Author Dejan Sarka demonstrates efficient statistical queries that are based on window functions and optimized through algorithms built using mathematical knowledge and creativity. The formulas and usage of those statistical procedures are explained so you can understand and modify the techniques presented. T-SQL is supported in SQL Server,Azure SQL Database, and in Azure Synapse Analytics. There are so many BI features in T-SQL that it might become your primary analytic database language. If you want to learn how to get information from your data with the T-SQL language that you already are familiar with, then this is the book for you. What You Will Learn Describe distribution of variables with statistical measures Find associations between pairs of variables Evaluate the quality of the data you are analyzing Perform time-series analysis on your data Forecast values of a continuous variable Perform market-basket analysis to predict customer purchasing patterns Predict target variable outcomes from one or more input variables Categorize passages of text by extracting and analyzing keywords Who This Book Is For Database developers and database administrators who want to translate their T-SQL skills into the world of business intelligence (BI) and data science. For readers who want to analyze large amounts of data efficiently by using their existing knowledge of T-SQL and Microsoft’s various database platforms such as SQL Server and Azure SQL Database. Also for readers who want to improve their querying by learning new and original optimization techniques.

Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail

Database developers and administrators will use this book to learn how to deploy machine learning models in Oracle Database and in Oracle’s Autonomous Database cloud offering. The book covers the technologies that make up the Oracle Machine Learning (OML) platform, including OML4SQL, OML Notebooks, OML4R, and OML4Py. The book focuses on Oracle Machine Learning as part of the Oracle Autonomous Database collaborative environment. Also covered are advanced topics such as delivery and automation pipelines. Throughout the book you will find practical details and hand-on examples showing you how to implement machine learning and automate deployment of machine learning. Discussion around the examples helps you gain a conceptual understanding of machine learning. Important concepts discussed include the methods involved, the algorithms to choose from, and mechanisms for process and deployment. Seasoned database professionals looking to make the leap into machine learning as a growth path will find much to like in this book as it helps you step up and use your current knowledge of Oracle Database to transition into providing machine learning solutions. What You Will Learn Use the Oracle Machine Learning (OML) Notebooks for data visualization and machine learning model building and evaluation Understand Oracle offerings for machine learning Develop machine learning with Oracle database using the built-in machine learning packages Develop and deploy machine learning models using OML4SQL and OML4R Leverage the Oracle Autonomous Database and its collaborative environment for Oracle Machine Learning Develop and deploy machine learning projects in Oracle Autonomous Database Build an automated pipeline that can detect and handle changes in data/model performance Who This Book Is For Database developers and administrators who want to learn about machine learning, developers who want to build models and applications using Oracle Database’s built-in machine learning feature set, and administrators tasked with supporting applications on Oracle Database that make use of the Oracle Machine Learning feature set

Through explosive growth in the past decade, data now drives significant portions of our lives, from crowdsourced restaurant recommendations to AI systems identifying effective medical treatments. Software developers have unprecedented opportunity to build data applications that generate value from massive datasets across use cases such as customer 360, application health and security analytics, the IoT, machine learning, and embedded analytics. With this report, product managers, architects, and engineering teams will learn how to make key technical decisions when building data-intensive applications, including how to implement extensible data pipelines and share data securely. The report includes design considerations for making these decisions and uses the Snowflake Data Cloud to illustrate best practices. This report explores: Why data applications matter: Get an introduction to data applications and some of the most common use cases Evaluating platforms for building data apps: Evaluate modern data platforms to confidently consider the merits of potential solutions Building scalable data applications: Learn design patterns and best practices for storage, compute, and security Handling and processing data: Explore techniques and real-world examples for building data pipelines to support data applications Designing for data sharing: Learn best practices for sharing data in modern data applications

In 'Distributed Data Systems with Azure Databricks', you will explore the capabilities of Microsoft Azure Databricks as a platform for building and managing big data pipelines. Learn how to process, transform, and analyze data at scale while developing expertise in training distributed machine learning models and integrating them into enterprise workflows. What this Book will help me do Design and implement Extract, Transform, Load (ETL) pipelines using Azure Databricks. Conduct distributed training of machine learning models using TensorFlow and Horovod. Integrate Azure Databricks with Azure Data Factory for optimized data pipeline orchestration. Utilize Delta Engine for efficient querying and analysis of data within Delta Lake. Employ Databricks Structured Streaming to manage real-time production-grade data flows. Author(s) None Palacio is an experienced data engineer and cloud computing specialist, with extensive knowledge of the Microsoft Azure platform. With years of practical application of Databricks in enterprise settings, Palacio provides clear, actionable insights through relatable examples. They bring a passion for innovative solutions to the field of big data automation. Who is it for? This book is ideal for data engineers, machine learning engineers, and software developers looking to master Azure Databricks for large-scale data processing and analysis. Readers should have basic familiarity with cloud platforms, understanding of data pipelines, and a foundational grasp of Python and machine learning concepts. It is perfect for those wanting to create scalable and manageable data workflows.

This IBM® Redpaper publication is a comprehensive guide that covers the IBM Power System IC922 (9183-22X) server that uses IBM POWER9™ processor-based technology and supports Linux operating systems (OSs). The objective of this paper is to introduce the system offerings and their capacities and available features. The Power IC922 server is built to deliver powerful computing, scaling efficiency, and storage capacity in a cost-optimized design to meet the evolving data challenges of the artificial intelligence (AI) era. It includes the following features: High throughput and performance for high-value Linux workloads, such as inferencing data or storage-rich workloads, or cloud. Potentially low acquisition cost through system optimization, such as using industry standard memory and warranty. Two IBM POWER9 processor-based single-chip module (SCM) devices that provide high performance with 24, 32, or 40 fully activated cores and a maximum 2 TB of memory. Up to six NVIDIA T4 graphics processing unit (GPU) accelerators. Up to twenty-four 2.5-inch SAS/SATA drives. One dedicated and one shared 1 Gb Intelligent Platform Management Interface (IPMI) port.. This publication is for professionals who want to acquire a better understanding of IBM Power Systems products. The intended audience includes: Clients Sales and marketing professionals Technical support professionals IBM Business Partners Independent software vendors (ISVs) This paper expands the current set of IBM Power Systems documentation by providing a desktop reference that offers a detailed technical description of the Power IC922 server.

BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically. This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 1 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.

BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically. This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 2 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.

A successful pipeline moves data efficiently, minimizing pauses and blockages between tasks, keeping every process along the way operational. Apache Airflow provides a single customizable environment for building and managing data pipelines, eliminating the need for a hodgepodge collection of tools, snowflake code, and homegrown processes. Using real-world scenarios and examples, Data Pipelines with Apache Airflow teaches you how to simplify and automate data pipelines, reduce operational overhead, and smoothly integrate all the technologies in your stack. About the Technology Data pipelines manage the flow of data from initial collection through consolidation, cleaning, analysis, visualization, and more. Apache Airflow provides a single platform you can use to design, implement, monitor, and maintain your pipelines. Its easy-to-use UI, plug-and-play options, and flexible Python scripting make Airflow perfect for any data management task. About the Book Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines. You’ll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. Part reference and part tutorial, this practical guide covers every aspect of the directed acyclic graphs (DAGs) that power Airflow, and how to customize them for your pipeline’s needs. What's Inside Build, test, and deploy Airflow pipelines as DAGs Automate moving and transforming data Analyze historical datasets using backfilling Develop custom components Set up Airflow in production environments About the Reader For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills. About the Authors Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies. Bas is also an Airflow committer. Quotes An Airflow bible. Useful for all kinds of users, from novice to expert. - Rambabu Posa, Sai Aashika Consultancy An easy-to-follow exploration of the benefits of orchestrating your data pipeline jobs with Airflow. - Daniel Lamblin, Coupang The one reference you need to create, author, schedule, and monitor workflows with Apache Airflow. Clear recommendation. - Thorsten Weber, bbv Software Services AG By far the best resource for Airflow. - Jonathan Wood, LexisNexis

This IBM® Redbooks® publication provides options and best practices for deploying SAS Viya 3.5 on IBM POWER9™ servers. SAS Viya is a complex set of artificial intelligence (AI) and analytics solutions that require a properly planned infrastructure to meet the needs of the data scientists, business analysts, and application developers who use Viya capabilities in their daily work activities. Regardless of the user role, the underlying infrastructure matters to ensure performance expectations and service level agreement (SLA) requirements are met or exceeded. Although the general planning process is similar for deploying SAS Viya on any platform, key IBM POWER9 differentiators must be considered to ensure that an optimized infrastructure deployment is achieved. This guide provides useful information that is needed during the planning, sizing, ordering, installing, configuring, and tuning phases of your SAS Viya deployment on POWER9 processor-based servers. This book addresses topics for IT architects, IT specialists, developers, sellers, and anyone who wants to implement SAS Viya 3.5 on IBM POWER9 servers. Moreover, this publication provides documentation to transfer the how-to-skills to the technical teams, and solution guidance to the sales team. This book compliments the documentation that is available in IBM Knowledge Center and aligns with the educational materials that are provided by the IBM Systems Software Education (SSE).

Get started using Apache Spark via C# or F# and the .NET for Apache Spark bindings. This book is an introduction to both Apache Spark and the .NET bindings. Readers new to Apache Spark will get up to speed quickly using Spark for data processing tasks performed against large and very large datasets. You will learn how to combine your knowledge of .NET with Apache Spark to bring massive computing power to bear by distributed processing of extremely large datasets across multiple servers. This book covers how to get a local instance of Apache Spark running on your developer machine and shows you how to create your first .NET program that uses the Microsoft .NET bindings for Apache Spark. Techniques shown in the book allow you to use Apache Spark to distribute your data processing tasks over multiple compute nodes. You will learn to process data using both batch mode and streaming mode so you can make the right choice depending on whether you are processing an existing dataset or are working against new records in micro-batches as they arrive. The goal of the book is leave you comfortable in bringing the power of Apache Spark to your favorite .NET language. What You Will Learn Install and configure Spark .NET on Windows, Linux, and macOS Write Apache Spark programs in C# and F# using the .NET bindings Access and invoke the Apache Spark APIs from .NET with the same high performance as Python, Scala, and R Encapsulate functionality in user-defined functions Transform and aggregate large datasets Execute SQL queries against files through Apache Hive Distribute processing of large datasets across multiple servers Create your own batch, streaming, and machine learning programs Who This Book Is For .NETdevelopers who want to perform big data processing without having to migrate to Python, Scala, or R; and Apache Spark developers who want to run natively on .NET and take advantage of the C# and F# ecosystems

This IBM® Redpaper® publication is intended to facilitate the deployment and configuration of the IBM Spectrum® Scale based high-performance storage solutions for the scalable data and AI solutions on Amazon Web Services (AWS). Configuration, testing results, and tuning guidelines for running the IBM Spectrum Scale based high-performance storage solutions for the data and AI workloads on AWS are the focus areas of the paper. The LAB Validation was conducted with the Red Hat Linux nodes to IBM Spectrum Scale by using the various Amazon Elastic Compute Cloud (EC2) instances. Simultaneous workloads are simulated across multiple Amazon EC2 nodes running with Red Hat Linux to determine scalability against the IBM Spectrum Scale clustered file system. Solution architecture, configuration details, and performance tuning demonstrate how to maximize data and AI application performance with IBM Spectrum Scale on AWS.

The opportunity to modernize and improve the enterprise data warehouse is one of the best reasons for moving your application to the cloud. A data warehouse can access a greater diversity of use cases and practices than is possible in an existing environment. In this report, researcher and analyst Stephen Swoyer offers a comprehensive overview of the benefits and challenges of implementing a cloud-based data warehouse. Senior IT decision makers, chief data officers, and data professionals will learn about the shifts and new trends in the data management landscape. Explore ways to improve data management, build a data warehouse strategy, and learn how to modernize a data warehouse effectively. Understand how AI, machine learning, self-service data integration, and built-in developer-oriented services have transformed the data warehouse role Use data warehouses to work with cloud-based data lakes for end-to-end data management and data governance Explore how data warehouse platforms as a service (PaaS) pave the way to automation Migrate, manage, and secure a data warehouse in a hybrid or multicloud environment

Businesses manage data to understand the connections between their customers, products or services, features, markets, and anything else that affects the business. With a knowledge graph, you can represent these connections directly to analyze and understand the compound relationships that drive business innovation. This report introduces knowledge graphs and examines their ability to weave business data and business knowledge into an architecture known as a data fabric . Authors Sean Martin, Ben Szekely, and Dean Allemang explain graph data and knowledge representation and demonstrate the value of combining these two things in a knowledge graph. You'll learn how knowledge graphs enable an enterprise-scale data fabric and discover what to expect in the near future as this technology evolves. This report also examines the evolution of databases, data integration, and data analysis to help you understand how the industry reached this point. Learn how graph technology enables you to represent knowledge and link it to data Understand how graph technology emphasizes the connected nature of data Use a data fabric to support other data-intensive tasks, including machine learning and data analysis Examine how a data fabric supports intense data-driven business initiatives more robustly than a simple database or data architecture

Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. This book teaches you how to select variables, optimize hyper parameters, develop pipelines, and train, test, and validate machine and deep learning models. Each chapter includes a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model. The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification problems using artificial neural networks such as restricted Boltzmann machines, multi-layer perceptrons, and deep belief networks. The book discusses unsupervised learning clustering techniques such as the K-means method, agglomerative and Dbscan approaches, and dimension reduction techniques such as Feature Importance, Principal Component Analysis, and Linear Discriminant Analysis. And it introduces driverless artificial intelligence using H2O. After reading this book, you will be able to develop, test, validate, and optimize statistical machine learning and deep learning models, and engineer, visualize, and interpret sets of data. What You Will Learn Design, develop, train, and validate machine learning and deep learning models Find optimal hyper parameters for superior model performance Improve model performance using techniques such as dimension reduction and regularization Extract meaningful insights for decision making using data visualization Who This Book Is For Beginning and intermediate level data scientists and machine learning engineers

This IBM® Redpaper publication focuses on data orchestration in enterprise data pipelines. It provides details about data orchestration and how to address typical challenges that customers face when dealing with large and ever-growing amounts of data for data analytics. While the amount of data increases steadily, artificial intelligence (AI) workloads must speed up to deliver insights and business value in a timely manner. This paper provides a solution that addresses these needs: Data Accelerator for AI and Analytics (DAAA). A proof of concept (PoC) is described in detail. This paper focuses on the functions that are provided by the Data Accelerator for AI and Analytics solution, which simplifies the daily work of data scientists and system administrators. This solution helps increase the efficiency of storage systems and data processing to obtain results faster while eliminating unnecessary data copies and associated data management.