Moving and building in the cloud to accelerate analytics development requires enterprises to rethink their data infrastructure. Whether you are moving from an on-prem legacy system or you were born in the cloud, businesses are turning to Confluent and Databricks to help them unlock new real-time customer experiences and intelligence for their backend operations.

Join us to see how Confluent and Databricks enable companies to set data in motion across any system, at any scale, in near real-time. Connecting Confluent with Databricks allows companies to migrate and connect data from on-prem databases and data warehouses like Netezza, Oracle, and Cloudera to Databricks in the cloud to power real-time analytics.

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The Problems of Viewing Performance challenges long-held assumptions by considering the ways in which knowledge is received by more than a single audience member, and breaks new ground by, counterintuitively, claiming that viewing performance is not a shared experience.

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Abstract Hosted by Al Martin, VP, Data and AI Expert Services and Learning at IBM, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts. This week on Making Data Simple, we have Ayal Steinberg, Global Sales VP, Data and AI at IBM. Ayal Steinberg is the Vice President of Global Sales for IBM's Data and AI business unit.  In his capacity Ayal oversees IBM's largest and one of the most strategic business units with over 1,500 people and several billion dollars of annual revenue.  Ayal has proven success in managing complex and global sales organizations. Throughout his career, Ayal has created and led high-performing sales teams focused on selling complex software solutions to some of the world’s most well-known brands in more then 50 countries. Prior to IBM, Ayal successfully led sales teams through transformation and hyper growth at IBM Netezza, Oracle, Datastax (the open source provider of Apache Cassandra), and other enterprise software companies.  Earlier in his career, Ayal was a pioneer in selling software for several start-ups in price optimization and advanced analytics. Ayal majored in Economics from Binghamton University, State University of New York.

Show Notes 4:00 – Ayal’s back ground 15:33 – IBM strategy  18:45 – Moving to cloud 21:23 – Why IBM 23:24 - Value Selling 27:58 – Value vs. price 29:57 - Skills set 31:20 – How do you bring someone back around Solution Selling Challenger Sale Strengths Finder 2.0 Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.    Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Over the past decade, data science has come out of the back office to become a force of change across the entire organization. At the forefront of this change is the open data science movement that advocates the use of open source tools in a powerful, connected ecosystem. This report explores how open data science can help your organization break free from the shackles of proprietary tools, embrace a more open and collaborative work style, and unleash new intelligent applications quickly. Authors Michele Chambers and Christine Doig explain how open source tools have helped bring about many facets of the data science evolution, including collaboration, self-service, and deployment. But you’ll discover that open data science is about more than tools; it’s about a new way of working as an organization. Learn how data science—particularly open data science—has become part of everyday business Understand how open data science engages people from other disciplines, not just statisticians Examine tools and practices that enable data science to be open across technical, operational, and organizational aspects Learn benefits of open data science, including rich resources, agility, transparency, and collective intelligence Explore case studies that demonstrate different ways to implement open data science Discover how open data science can help you break down department barriers and make bold market moves Michele Chambers, Chief Marketing Officer and VP Products at Continuum Analytics, is an entrepreneurial executive with over 25 years of industry experience. Prior to Continuum Analytics, Michele held executive leadership roles at several database and analytic companies, including Netezza, IBM, Revolution Analytics, MemSQL, and RapidMiner. Christine Doig is a senior data scientist at Continuum Analytics, where she's worked on several projects, including MEMEX, a DARPA-funded open data science project to help stop human trafficking. She has 5+ years of experience in analytics, operations research, and machine learning in a variety of industries.

What IS customer intelligence? What is a customer? Is the customer best understood by breaking the word down into its component parts: "cuss" and "tumor?" Would that be an intelligent thing to do? Will these and related questions some day be answered by self-aware machines? Will any of these questions be answered on this episode? Give it a listen and find out! The mish-mash of companies, products, and miscellany mentioned on this show include: Adobe, Oracle/ATG, SAS Customer Intelligence, Salesforce.com, Scott Brinker (Chief Martec), Domo, Data Studio 360, Tableau, iJento, Netezza, SPSS, Unfrozen Caveman Lawyer, Eight Is Enough, Legend of the Plaid Dragon (and the Slack version), Office Vibe, p-value article on fivethirtyeight.com (and the p-hacking app), and the "AI, Deep Learning, and Machine Learning" video.

This book provides an overview of the fabrication methods for anti-abrasive nanocoatings.

The IBM® DB2® Analytics Accelerator for IBM z/OS® is a high-performance appliance that integrates the IBM zEnterprise® infrastructure with IBM PureData™ for Analytics, powered by IBM Netezza® technology. With this integration, you can accelerate data-intensive and complex queries in a DB2 for z/OS highly secure and available environment. DB2 and the Analytics Accelerator appliance form a self-managing hybrid environment running online transaction processing and online transactional analytical processing concurrently and efficiently. These online transactions run together with business intelligence and online analytic processing workloads. DB2 Analytics Accelerator V4.1 expands the value of high-performance analytics. DB2 Analytics Accelerator V4.1 opens to static Structured Query Language (SQL) applications and row set processing, minimizes data movement, reduces latency, and improves availability. This IBM Redbooks® publication provides technical decision-makers with an understanding of the benefits of version 4.1 of the Analytics Accelerator with DB2 11 for z/OS. It describes the installation of the new functions, and the advantages to existing analytical processes as measured in our test environment. This book also introduces the DB2 Analytics Accelerator Loader V1.1, a tool that facilitates the data population of the DB2 Analytics Accelerator.

The IBM® DB2® Analytics Accelerator Version 3.1 for IBM z/OS® (simply called Accelerator in this book) is a union of the IBM System z® quality of service and IBM Netezza® technology to accelerate complex queries in a DB2 for z/OS highly secure and available environment. Superior performance and scalability with rapid appliance deployment provide an ideal solution for complex analysis. In this IBM Redbooks® publication, we provide technical decision-makers with a broad understanding of the benefits of Version 3.1 of the Accelerator's major new functions. We describe their installation and the advantages to existing analytical processes as measured in our test environment. We also describe the IBM zEnterprise® Analytics System 9700, a hybrid System z solution offering that is surrounded by a complete set of optional packs to enable customers to custom tailor the system to their unique needs..

Integrating data from multiple sources is essential in the age of big data, but it can be a challenging and time-consuming task. This handy cookbook provides dozens of ready-to-use recipes for using Apache Sqoop, the command-line interface application that optimizes data transfers between relational databases and Hadoop. Sqoop is both powerful and bewildering, but with this cookbook’s problem-solution-discussion format, you’ll quickly learn how to deploy and then apply Sqoop in your environment. The authors provide MySQL, Oracle, and PostgreSQL database examples on GitHub that you can easily adapt for SQL Server, Netezza, Teradata, or other relational systems. Transfer data from a single database table into your Hadoop ecosystem Keep table data and Hadoop in sync by importing data incrementally Import data from more than one database table Customize transferred data by calling various database functions Export generated, processed, or backed-up data from Hadoop to your database Run Sqoop within Oozie, Hadoop’s specialized workflow scheduler Load data into Hadoop’s data warehouse (Hive) or database (HBase) Handle installation, connection, and syntax issues common to specific database vendors

How do you experience a photograph? What do you want a viewer to feel when they look at your image? Perception and Imaging explains how we see and what we don't see. Relevant psychological principles will help you predict your viewer's emotional reaction to your photographic images, giving you more power, control, and tools for communicating your desired message. Knowing how our minds work helps photographers, graphic designers, videographers, animators, and visual communicators both create and critique sophisticated works of visual art. Benefit from this insight in your work. Topics covered in this book: gestalt grouping, memory and association, space, time, color, contours, illusion and ambiguity, morphics, personality, subliminals, critiquing photographs, and rhetoric.

The IBM® DB2® Analytics Accelerator Version 2.1 for IBM z/OS® (also called DB2 Analytics Accelerator or Query Accelerator in this book and in DB2 for z/OS documentation) is a marriage of the IBM System z® Quality of Service and Netezza® technology to accelerate complex queries in a DB2 for z/OS highly secure and available environment. Superior performance and scalability with rapid appliance deployment provide an ideal solution for complex analysis. This IBM Redbooks® publication provides technical decision-makers with a broad understanding of the IBM DB2 Analytics Accelerator architecture and its exploitation by documenting the steps for the installation of this solution in an existing DB2 10 for z/OS environment. In this book we define a business analytics scenario, evaluate the potential benefits of the DB2 Analytics Accelerator appliance, describe the installation and integration steps with the DB2 environment, evaluate performance, and show the advantages to existing business intelligence processes. Please note that the additional material referenced in the text is not available from IBM.