Leverage the power of SQL Server 2017 Integration Services to build data integration solutions with ease Key Features Work with temporal tables to access information stored in a table at any time Get familiar with the latest features in SQL Server 2017 Integration Services Program and extend your packages to enhance their functionality Book Description Microsoft SQL Server 2017 uses the power of R and Python for machine learning and containerization-based deployment on Windows and Linux. By learning how to use the features of SQL Server 2017 effectively, you can build scalable apps and easily perform data integration and transformation. You'll start by brushing up on the features of SQL Server 2017. This Learning Path will then demonstrate how you can use Query Store, columnstore indexes, and In-Memory OLTP in your apps. You'll also learn to integrate Python code in SQL Server and graph database implementations for development and testing. Next, you'll get up to speed with designing and building SQL Server Integration Services (SSIS) data warehouse packages using SQL server data tools. Toward the concluding chapters, you'll discover how to develop SSIS packages designed to maintain a data warehouse using the data flow and other control flow tasks. By the end of this Learning Path, you'll be equipped with the skills you need to design efficient, high-performance database applications with confidence. This Learning Path includes content from the following Packt books: SQL Server 2017 Developer's Guide by Milos Radivojevic, Dejan Sarka, et. al SQL Server 2017 Integration Services Cookbook by Christian Cote, Dejan Sarka, et. al What you will learn Use columnstore indexes to make storage and performance improvements Extend database design solutions using temporal tables Exchange JSON data between applications and SQL Server Migrate historical data to Microsoft Azure by using Stretch Database Design the architecture of a modern Extract, Transform, and Load (ETL) solution Implement ETL solutions using Integration Services for both on-premise and Azure data Who this book is for This Learning Path is for database developers and solution architects looking to develop ETL solutions with SSIS, and explore the new features in SSIS 2017. Advanced analysis practitioners, business intelligence developers, and database consultants dealing with performance tuning will also find this book useful. Basic understanding of database concepts and T-SQL is required to get the best out of this Learning Path.

Start developing with Oracle SQL. This book is a one-stop introduction to everything you need to know about getting started developing an Oracle Database. You'll learn about foundational concepts, setting up a simple schema, adding data, reading data from the database, and making changes. No experience with databases is required to get started. Examples in the book are built around Oracle Live SQL, a freely available, online sandbox for practicing and experimenting with SQL statements, and Oracle Express Edition, a free version of Oracle Database that is available for download. A marquee feature of Beginning Oracle SQL for Oracle Database 18c is the small chapter size. Content is divided into easily digestible chunks that can be read and practiced in very short intervals of time, making this the ideal book for a busy professional to learn from. Even just a 15-20 minute block of free time can be put to good use. AuthorBen Brumm begins by helping you understand what a database is, and getting you set up with a sandbox in which to practice the SQL that you are learning. From there, easily digestible chapters cover, point-by-point, the different aspects of writing queries to get data out of a database. You’ll also learn about creating tables and getting data into the database. Crucial topics such as working with nulls and writing analytic queries are given the attention they deserve, helping you to avoid pitfalls when writing queries for production use. What You'll Learn Create, update, and delete tables in an Oracle database Add, update, delete data from those database tables Query and view data stored in your database Manipulate and transform data using in-built database functions and features Correctly choose when to use Oracle-specific syntax and features Who This Book Is For Those new to Oracle who are planning to develop software using Oracle as the back-end data store. The book is also for those who are getting started in software development and realize they need to learn some kind of database language. Those who are learning software development on the side of their normal job, or learning it as a college student, who are ready to learn what a database is and how to use it also will find this book useful.

This IBM Redpaper™ publication introduces the IBM Spectrum Scale immutability function. It shows how to set it up and presents different ways for managing immutable and append-only files. This publication also provides guidance for implementing IT security aspects in an IBM Spectrum Scale cluster by addressing regulatory requirements. It also describes two typical use cases for managing immutable files. One use case involves applications that manage file immutability; the other use case presents a solution to automatically set files to immutable within a IBM Spectrum Scale immutable fileset.

Organizations of all sizes face the challenge of managing massive volumes of increasingly valuable data. But storing this data can be costly, and extracting value from the data is becoming more difficult. IT organizations have limited resources but must stay responsive to dynamic environments and act quickly to consolidate, simplify, and optimize their IT infrastructures. The IBM® Storwize® V5000 Gen2 system provides a smarter solution that is affordable, easy to use, and self-optimizing, which enables organizations to overcome these storage challenges. The Storwize V5000 Gen2 delivers efficient, entry-level configurations that are designed to meet the needs of small and midsize businesses. Designed to provide organizations with the ability to consolidate and share data at an affordable price, the Storwize V5000 Gen2 offers advanced software capabilities that are found in more expensive systems. This IBM Redbooks® publication is intended for pre-sales and post-sales technical support professionals and storage administrators. It applies to the Storwize V5030, V5020, and V5010, and to IBM Spectrum Virtualize™ V8.2.1.

As workloads are being offloaded to IBM® LinuxONE based cloud environments, it is important to ensure that these workloads and environments are secure. This IBM Redbooks® publication describes the necessary steps to secure your environment from the hardware level through all of the components that are involved in a LinuxONE cloud infrastructure that use Linux and IBM z/VM®. The audience for this book is IT architects, IT Specialists, and those users who plan to use LinuxONE for their cloud environments.

This IBM® Redpaper™ publication shows you how to deploy a database instance within a container using an IBM Cloud™ Private cluster on IBM Z®. A preinstalled IBM Spectrum™ Scale 5.0.3 cluster file system provides back-end storage for the persistent volumes bound to the database. A container is a standard unit of software that packages code and all its dependencies, so the application runs quickly and reliably from one computing environment to another. By default, containers are ephemeral. However, stateful applications, such as databases, require some type of persistent storage that can survive service restarts or container crashes. IBM provides several products helping organizations build an environment on an IBM Z infrastructure to develop and manage containerized applications, including dynamic provisioning of persistent volumes. As an example for a stateful application, this paper describes how to deploy the relational database MariaDB using a Helm chart. The IBM Spectrum Scale V5.0.3 cluster file system is providing back-end storage for the persistent volumes. This document provides step-by-step guidance regarding how to install and configure the following components: IBM Cloud Private 3.1.2 (including Kubernetes) Docker 18.03.1-ce IBM Storage Enabler for Containers 2.0.0 and 2.1.0 This Redpaper demonstrates how we set up the example for a stateful application in our lab. The paper gives you insights about planning for your implementation. IBM Z server hardware, the IBM Z hypervisor z/VM®, and the IBM Spectrum Scale cluster file system are prerequisites to set up the example environment. The Redpaper is written with the assumption that you have familiarity with and basic knowledge of the software products used in setting up the environment. The intended audience includes the following roles: Storage administrators IT/Cloud administrators Technologists IT specialists

Data professionals are confronting the most disruptive change since relational databases appeared in the 1980s. SQL is still a major tool for data analytics, but conventional relational database management systems can’t handle the increasing size and complexity of today’s datasets. This updated edition teaches you best practices for Greenplum Database, the open source massively parallel processing (MPP) database that accommodates large sets of nonrelational and relational data. Marshall Presser, field CTO at Pivotal, introduces Greenplum’s approach to data analytics and data-driven decisions, beginning with its shared-nothing architecture. IT managers, developers, data analysts, system architects, and data scientists will all gain from exploring data organization and storage, data loading, running queries, and learning to perform analytics in the database. Discover how MPP and Greenplum will help you go beyond the traditional data warehouse. This ebook covers: Greenplum features, use case examples, and techniques for optimizing use Four Greenplum deployment options to help you balance security, cost, and time to usability Why each networked node in Greenplum’s architecture includes an independent operating system, memory, and storage Additional tools for monitoring, managing, securing, and optimizing query responses in the Pivotal Greenplum commercial database

Many large organizations have accumulated dozens of disconnected data sources to serve different lines of business over the years. These applications might be useful to one area of the enterprise, but they’re usually inaccessible to other data consumers in the organization. In this short report, five data industry thought leaders explore DataOps—the automated, process-oriented methodology for making clean, reliable data available to teams throughout your company. Andy Palmer, Michael Stonebraker, Nik Bates-Haus, Liam Cleary, and Mark Marinelli from Tamr use real-world examples to explain how DataOps works. DataOps is as much about changing people’s relationship to data as it is about technology, infrastructure, and process. This report provides an organizational approach to implementing this discipline in your company—including various behavioral, process, and technology changes. Through individual essays, you’ll learn how to: Move toward scalable data unification (Michael Stonebraker) Understand DataOps as a discipline (Nik Bates-Haus) Explore the key principles of a DataOps ecosystem (Andy Palmer) Learn the key components of a DataOps ecosystem (Andy Palmer) Build a DataOps toolkit (Liam Cleary) Build a team and prepare for future trends (Mark Marinelli)

Big data and advanced analytics have increasingly moved to the cloud as organizations pursue actionable insights and data-driven products using the growing amounts of information they collect. But few companies have truly operationalized data so it’s usable for the entire organization. With this pragmatic ebook, engineers, architects, and data managers will learn how to build and extract value from a data lake in the cloud and leverage the compute power and scalability of a cloud-native data platform to put your company’s vast data trove into action. Holden Ackerman and Jon King of Qubole take you through the basics of building a data lake operation, from people to technology, employing multiple technologies and frameworks in a cloud-native data platform. You'll dive into the tools and processes you need for the entire lifecycle of a data lake, from data preparation, storage, and management to distributed computing and analytics. You’ll also explore the unique role that each member of your data team needs to play as you migrate to your cloud-native data platform. Leverage your data effectively through a single source of truth Understand the importance of building a self-service culture for your data lake Define the structure you need to build a data lake in the cloud Implement financial governance and data security policies for your data lake through a cloud-native data platform Identify the tools you need to manage your data infrastructure Delineate the scope, usage rights, and best tools for each team working with a data lake—analysts, data scientists, data engineers, and security professionals, among others

When data-driven applications fail, identifying the cause is both challenging and time-consuming—especially as data pipelines become more and more complex. Hunting for the root cause of application failure from messy, raw, and distributed logs is difficult for performance experts and a nightmare for data operations teams. This report examines DataOps processes and tools that enable you to manage modern data pipelines efficiently. Author Ted Malaska describes a data operations framework and shows you the importance of testing and monitoring to plan, rebuild, automate, and then manage robust data pipelines—whether it’s in the cloud, on premises, or in a hybrid configuration. You’ll also learn ways to apply performance monitoring software and AI to your data pipelines in order to keep your applications running reliably. You’ll learn: How performance management software can reduce the risk of running modern data applications Methods for applying AI to provide insights, recommendations, and automation to operationalize big data systems and data applications How to plan, migrate, and operate big data workloads and data pipelines in the cloud and in hybrid deployment models

Professional Azure SQL Database Administration serves as your comprehensive guide to mastering the management and optimization of cloud-based Azure SQL Database solutions. With the differences and unique features of Azure SQL Database compared to the on-premise SQL Server, this book offers a clear roadmap to efficiently migrate, secure, scale, and maintain these databases in the cloud. What this Book will help me do Understand the differences between Azure SQL Database and on-premise SQL Server and their practical implications. Learn techniques to migrate existing SQL Server databases to Azure SQL Database seamlessly. Discover advanced ways to optimize database performance and scalability leveraging cloud capabilities. Master security strategies for Azure SQL databases, including backup, disaster recovery, and automated tasks. Develop proficiency in using tools such as PowerShell to automate and manage routine database administration tasks. Author(s) Ahmad Osama is an experienced database professional and author specializing in SQL Server and Azure SQL Database administration. With a robust background in database migration, maintenance, and performance tuning, Ahmad expertly bridges the gap between theory and practice. His approachable writing style makes complex database topics accessible to professionals seeking to expand their expertise. Who is it for? Professional Azure SQL Database Administration is an essential resource for database administrators, developers, and IT professionals keen on developing their knowledge about Azure SQL Database administration and cloud database solutions. Whether you're transitioning from traditional SQL Server environments or looking to optimize your database strategies in the cloud, this book caters to professionals with intermediate to advanced experience in database management and programming with SQL.

The use of N_Port ID Virtualization (NPIV) to provide host-only ports (NPIV target ports) and spare nodes improves the host failover characteristics by separating out host communications from communication tasks on the same port and providing standby hardware, which can be automatically introduced into the cluster to reintroduce redundancy. Because the host ports are not used for internode communications, they can freely move between nodes, and this includes spare nodes that are added to the cluster automatically. This IBM® Redpaper™ publication describes the use of the IBM Spectrum™ Virtualize Hot-Spare Node function to provide a high availability storage infrastructure. This paper focuses on the functional behavior of hot-spare node when subjected to various failure conditions. This paper does not provide the details necessary to implement the reference architectures (although some implementation detail is provided).

This IBM® Redguide™ publication describes big data and analytics deployments that are built on IBM Spectrum Scale™. IBM Spectrum Scale is a proven enterprise-level distributed file system that is a high-performance and cost-effective alternative to Hadoop Distributed File System (HDFS) for Hadoop analytics services. IBM Spectrum Scale includes NFS, SMB, and Object services and meets the performance that is required by many industry workloads, such as technical computing, big data, analytics, and content management. IBM Spectrum Scale provides world-class, web-based storage management with extreme scalability, flash accelerated performance, and automatic policy-based storage tiering from flash through disk to the cloud, which reduces storage costs up to 90% while improving security and management efficiency in cloud, big data, and analytics environments. This Redguide publication is intended for technical professionals (analytics consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing Hadoop analytics services and are interested in learning about the benefits of the use of IBM Spectrum Scale as an alternative to HDFS.

This IBM® Redbooks® publication updates Implementing High Availability and Disaster Recovery Solutions with SAP HANA on IBM Power Systems, REDP-5443 with the latest technical content that describes how to implement an SAP HANA on IBM Power Systems™ high availability (HA) and disaster recovery (DR) solution by using theoretical knowledge and sample scenarios. This book describes how all the pieces of the reference architecture work together (IBM Power Systems servers, IBM Storage servers, IBM Spectrum™ Scale, IBM PowerHA® SystemMirror® for Linux, IBM VM Recovery Manager DR for Power Systems, and Linux distributions) and demonstrates the resilience of SAP HANA with IBM Power Systems servers. This publication is for architects, brand specialists, distributors, resellers, and anyone developing and implementing SAP HANA on IBM Power Systems integration, automation, HA, and DR solutions. This publication provides documentation to transfer the how-to-skills to the technical teams, and documentation to the sales team.

The purpose of this document is to complete the task of introducing Transport Layer Security to z/OS® so IBM Personal Communications (PCOMM) uses TLS security. This document walks you through enabling Tunneled Transport Layer Security (TTLS) on your IBM z/OS for use with a PCOMM TN3270 connection. When you complete this task, you require a certificate to access your TN3270 PCOMM session. You work with the following products and components: TN3270 TCPIP PAGENT INET (maybe) IBM RACF® This document assumes that the reader has extensive knowledge of z/OS security administration and these products and components. This document is part of the Technical Enablement Series that was created at the IBM Client Experience Centers.

Today's global organizations depend on the ability to unlock business insights from massive volumes of data. Now, with IBM® FlashSystem 900 Model AE3, they can make faster decisions based on real-time insights. Thus, they unleash the power of demanding applications, including these: Online transaction processing (OLTP) and analytical databases Virtual desktop infrastructures (VDIs) Technical computing applications Cloud environments Easy to deploy and manage, IBM FlashSystem® 900 Model AE3 is designed to accelerate the applications that drive your business. Powered by IBM FlashCore® Technology, IBM FlashSystem Model AE3 provides the following characteristics: Accelerate business-critical workloads, real-time analytics, and cognitive applications with the consistent microsecond latency and extreme reliability of IBM FlashCore technology Improve performance and help lower cost with new inline data compression Help reduce capital and operational expenses with IBM enhanced 3D triple-level cell (3D TLC) flash Protect critical data assets with patented IBM Variable Stripe RAID™ Power faster insights with IBM FlashCore including hardware-accelerated nonvolatile memory (NVM) architecture, purpose-engineered IBM MicroLatency® modules and advanced flash management FlashSystem 900 Model AE3 can be configured in capacity points as low as 14.4 TB to 180 TB usable and up to 360 TB effective capacity after RAID 5 protection and compression. You can couple this product with either 16 Gbps, 8 Gbps Fibre Channel, 16 Gbps NVMe over Fibre Channel, or 40 Gbps InfiniBand connectivity. Thus, the IBM FlashSystem 900 Model AE3 provides extreme performance to existing and next generation infrastructure.

This IBM® Redbooks® publication is a detailed technical guide to the IBM System Storage® SAN Volume Controller (SVC), which is powered by IBM Spectrum™ Virtualize V8.2.1. IBM SAN Volume Controller is a virtualization appliance solution that maps virtualized volumes that are visible to hosts and applications to physical volumes on storage devices. Each server within the storage area network (SAN) has its own set of virtual storage addresses that are mapped to physical addresses. If the physical addresses change, the server continues running by using the same virtual addresses that it had before. Therefore, volumes or storage can be added or moved while the server is still running. The IBM virtualization technology improves the management of information at the block level in a network, which enables applications and servers to share storage devices on a network.

Tackles the growing complexity of contracting logistics in a technologically accelerating world.

This document is intended to facilitate the deployment of the scalable hybrid cloud solution for data agility and collaboration using IBM® Spectrum Scale across multiple public clouds. To complete the tasks it describes, you must understand IBM Spectrum Scale and IBM Spectrum Scale Active File Management (AFM). The information in this document is distributed on an basis without any warranty that is either expressed or implied. Support assistance for the use of this material is limited to situations where IBM Spectrum Scale or IBM Spectrum Scale Active File Management are supported and entitled, and where the issues are specific to a blueprint implementation.

"Big Data Simplified blends technology with strategy and delves into applications of big data in specialized areas, such as recommendation engines, data science and Internet of Things (IoT) and enables a practitioner to make the right technology choice. The steps to strategize a big data implementation are also discussed in detail. This book presents a holistic approach to the topic, covering a wide landscape of big

data technologies like Hadoop 2.0 and package implementations, such as Cloudera. In-depth discussion of associated technologies, such as MapReduce, Hive, Pig, Oozie, ApacheZookeeper, Flume, Kafka, Spark, Python and NoSQL databases like Cassandra, MongoDB, GraphDB, etc., is also included.

This document is intended to facilitate the deployment of the Multicloud Solution for Business Continuity and Storage as service by using IBM Spectrum Virtualize for Public Cloud on Amazon Web Services (AWS). To complete the tasks it describes, you must understand IBM FlashSystem 9100, IBM Spectrum Virtualize for Public Cloud, IBM Spectrum Connect, VMware vRealize Orchestrator, and vRealize Automation and AWS Cloud. The information in this document is distributed on an "as is" basis without any warranty that is either expressed or implied. Support assistance for the use of this material is limited to situations where IBM Storwize or IBM FlashSystem storage devices are supported and entitled and where the issues are specific to a blueprint implementation.

Data engineers and data scientists are not interchangeable—and misperceptions of their roles can hurt teams and compromise productivity. This article clears up the differences of each role and how to best optimize these roles.

Managers and staff responsible for planning, hiring, and allocating resources need to understand how streaming data can fundamentally change their organizations. Companies everywhere are disrupting business, government, and society by using data and analytics to shape their business. Even if you don’t have deep knowledge of programming or digital technology, this high-level introduction brings data streaming into focus. You won’t find math or programming details here, or recommendations for particular tools in this rapidly evolving space. But you will explore the decision-making technologies and practices that organizations need to process streaming data and respond to fast-changing events. By describing the principles and activities behind this new phenomenon, author Andy Oram shows you how streaming data provides hidden gems of information that can transform the way your business works. Learn where streaming data comes from and how companies put it to work Follow a simple data processing project from ingesting and analyzing data to presenting results Explore how (and why) big data processing tools have evolved from MapReduce to Kubernetes Understand why streaming data is particularly useful for machine learning projects Learn how containers, microservices, and cloud computing led to continuous integration and DevOps

Deep Learning for Search teaches you how to improve the effectiveness of your search by implementing neural network-based techniques. By the time you're finished with the book, you'll be ready to build amazing search engines that deliver the results your users need and that get better as time goes on! About the Technology Deep learning handles the toughest search challenges, including imprecise search terms, badly indexed data, and retrieving images with minimal metadata. And with modern tools like DL4J and TensorFlow, you can apply powerful DL techniques without a deep background in data science or natural language processing (NLP). This book will show you how. About the Book Deep Learning for Search teaches you to improve your search results with neural networks. You’ll review how DL relates to search basics like indexing and ranking. Then, you’ll walk through in-depth examples to upgrade your search with DL techniques using Apache Lucene and Deeplearning4j. As the book progresses, you’ll explore advanced topics like searching through images, translating user queries, and designing search engines that improve as they learn! What's Inside Accurate and relevant rankings Searching across languages Content-based image search Search with recommendations About the Reader For developers comfortable with Java or a similar language and search basics. No experience with deep learning or NLP needed. About the Author Tommaso Teofili is a software engineer with a passion for open source and machine learning. As a member of the Apache Software Foundation, he contributes to a number of open source projects, ranging from topics like information retrieval (such as Lucene and Solr) to natural language processing and machine translation (including OpenNLP, Joshua, and UIMA). He currently works at Adobe, developing search and indexing infrastructure components, and researching the areas of natural language processing, information retrieval, and deep learning. He has presented search and machine learning talks at conferences including BerlinBuzzwords, International Conference on Computational Science, ApacheCon, EclipseCon, and others. You can find him on Twitter at @tteofili. Quotes A practical approach that shows you the state of the art in using neural networks, AI, and deep learning in the development of search engines. - From the Foreword by Chris Mattmann, NASA JPL A thorough and thoughtful synthesis of traditional search and the latest advancements in deep learning. - Greg Zanotti, Marquette Partners A well-laid-out deep dive into the latest technologies that will take your search engine to the next level. - Andrew Wyllie, Thynk Health Hands-on exercises teach you how to master deep learning for search-based products. - Antonio Magnaghi, System1

This Blueprint is intended to define the infrastructure that is required for a blockchain remote peer and to facilitate the deployment of IBM Blockchain Platform on IBM Cloud Private using that infrastructure. This infrastructure includes the necessary document handler components, such as IBM Blockchain Document Store, and covers the required storage for on-chain and off-chain blockchain data. To complete these tasks, you must have a basic understanding of each of the used components or have access the correct educational material to gain that knowledge.