Send us a text 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.

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 Lisa Palmer. Lisa is currently pursuing her doctorate in Artificial Intelligence, Chief Technical Advisor with Splunk Technologies. Lisa also was a professor at Southern Nazarene University, Tara Data Global Accounts Strategist, and then VP and America’s Regional Manager for Gartner.     

Show Notes 1:30 – Lisa’s background 6:16 – What is your Brand 10: 06 - Pros and cons of AI Intelligence 12:14 – Policing AI 15:40 – Does bias worry you 20:00 - What is flipping to data leadership 23:55 – What are you working on 27:07 – Why is pursuing your doctorate important to you 28:38 – What’s your advice for other leaders 30:37 – Trusted information 32:50 – Predication for next year Invisible Women: Data Bias in a World Designed for Men Lisa Palmer - LinkedIn

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.

Send us a text 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.

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 Dale Davis Jones, who is an IBM Vice President and Distinguished Engineer in Global Technology Services, where she leads the GTS IT Architect community and Client Innovation. We also have Hai-Nhu Tran, who is the Senior Manager of Content of Design in Data and AI at IBM. Hai-Nhu and her team are responsible for the technical content experience for a large portfolio of products and platforms.

Show Notes 11:43 - What is the context and how did you get involved? 17:10 - How do you define success? 19:40 - Are you focused on IT language? 25:03 - How do you know you’re doing it right? 32:30 - What decision have already been made? 37:13 - What other challenges have you faced? 40:00 - How do you know when you’re done? 41:29 - How can people contribute? Dale Davis Jones - LinkedIn Hai-Nhu Tran - LinkedIn Blog - Words Matter: Driving Thoughtful Change Toward Inclusive Language in Technology https://www.ibm.com/blogs/think/2020/08/words-matter-driving-thoughtful-change-toward-inclusive-language-in-technology/ Call for Code for Racial Justice: https://developer.ibm.com/callforcode/racial-justice/ Linux Foundation

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.

This IBM® Redpaper™ publication is a comprehensive guide covering the IBM Power System S822 (8284-22A) server that supports the IBM AIX® and Linux operating systems (OSes) running on bare metal, and the IBM i OS running under the VIOS. The objective of this paper is to introduce the major innovative Power S822 offerings and their relevant functions: The new IBM POWER8™ processor, which is available at frequencies of 3.42 GHz, and 3.89 GHz Significantly strengthened cores and larger caches Two integrated memory controllers with improved latency and bandwidth Integrated I/O subsystem and hot-pluggable PCIe Gen3 I/O slots Improved reliability, serviceability, and availability (RAS) functions IBM EnergyScale™ technology that provides features such as power trending, power-saving, capping of power, and thermal measurement This publication is for professionals who want to acquire a better understanding of IBM Power Systems™ products. 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 S822 system. This paper does not replace the latest marketing materials and configuration tools. It is intended as an additional source of information that, together with existing sources, can be used to enhance your knowledge of IBM server solutions.

Send us a text 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.

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 Kathy Parks. Kathy worked at IBM and is now an owner and independent Angel Investor. Kathy started her career in publishing and then transitioned over to Kurzweil Computer Products working on reading machine for the blind. Kathy then moved to Interleaf working on publishing products, then to QA and Project Management. Kathy then moved to Rational Software and in 2003 Rational was bought by IBM.

Show Notes 2:10 - How do you go from publishing to project management? 3:15 - How did you transition to Angel Investing? 5:58 - What’s at the core 7:06 - What is Angel Investing 12:40 - Is there a formula that you use? 17:24 - How are pitches made? 18:37 - Other forms of capital 22:43 - How many investments have you made? 29:21 - What data to you use? 30:42 - Why are we not seeing the investments? 31:58 - What are the KPIs in a pitch? 33:20 - How long does the process take? Angel Capital Association  KJParks - Linkedin Angel Investing - Venture Deals Age of Surveillance Capitalism 

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.

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. IBM® FlashSystem 5010 and FlashSystem 5030 systems provide a smarter solution that is affordable, easy to use, and self-optimizing, which enables organizations to overcome these storage challenges. The IBM FlashSystem® 5010 and FlashSystem 5030 deliver 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 system 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 IBM FlashSystem 5010 and FlashSystem 5030 and IBM Spectrum® Virtualize V8.3.1. This edition applies to IBM Spectrum Virtualize V8.3.1 and the associated hardware and software detailed within. Screen captures that are included within this book might differ from the generally available (GA) version because parts of this book were written with pre-GA code. On February 11, 2020, IBM announced that it was simplifying its portfolio. This book was written by using previous models of the product line before the simplification; however, most of the general principles apply. If you are in any doubt as to their applicability, work with your local IBM representative.

Send us a text 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.

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 Oliver Claude Portfolio Offering Manager for Data and AI and Oliver is a Data Governance expert. Oliver also worked as a Chief Marketing Officer, VP and Chief Solution Owner, Solution Management, and Consulting, Al and Oliver discuss Data Governance and Data Ops and how it all fits into your business. 

Show Notes 2:50 - What is the definition of Data Governance? 4:06 - What is Data Ops? 4:40 - What is IBM doing with Data Ops? 5:16 - How have we automated our tools? 6:58 - What is better red or white wine? 7:33 - What is the future of Data Governance? 9:37 - How is Data Governance and Data Ops related to AI? 11:06 - What are the pitfalls for customers implementing Data Governance? 12:10 - How do companies get started? Oliver Claude - LinkedIn IBM DataOps    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.

IBM® Db2® Analytics Accelerator is a workload optimized appliance add-on to IBM DB2® for IBM z/OS® that enables the integration of analytic insights into operational processes to drive business critical analytics and exceptional business value. Together, the Db2 Analytics Accelerator and DB2 for z/OS form an integrated hybrid environment that can run transaction processing, complex analytical, and reporting workloads concurrently and efficiently. With IBM DB2 Analytics Accelerator for z/OS V7, the following flexible deployment options are introduced: Accelerator on IBM Integrated Analytics System (IIAS): Deployment on pre-configured hardware and software Accelerator on IBM Z®: Deployment within an IBM Secure Service Container LPAR For using the accelerator for business-critical environments, the need arose to integrate the accelerator into High Availability (HA) architectures and Disaster Recovery (DR) processes. This IBM Redpaper™ publication focuses on different integration aspects of both deployment options of the IBM Db2 Analytics Accelerator into HA and DR environments. It also shares best practices to provide wanted Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO). HA systems often are a requirement in business-critical environments and can be implemented by redundant, independent components. A failure of one of these components is detected automatically and their tasks are taken over by another component. Depending on business requirements, a system can be implemented in a way that users do not notice outages (continuous availability), or in a major disaster, users notice an outage and systems resume services after a defined period, potentially with loss of data from previous work. IBM Z was strong for decades regarding HA and DR. By design, storage and operating systems are implemented in a way to support enhanced availability requirements. IBM Parallel Sysplex® and IBM Globally Dispersed Parallel Sysplex (IBM GDPS®) offer a unique architecture to support various degrees of automated failover and availability concepts. This IBM Redpaper publication shows how IBM Db2 Analytics Accelerator V7 can easily integrate into or complement existing IBM Z topologies for HA and DR. If you are using IBM Db2 Analytics Accelerator V5.1 or lower, see IBM Db2 Analytics Accelerator: High Availability and Disaster Recovery, REDP-5104.

This publication is intended to facilitate the deployment of the hybrid cloud business continuity solution with Red Hat OpenShift Container Platform and IBM® block CSI (Container Storage Interface) driver plug-in for IBM Spectrum® Virtualize on Public Cloud AWS (Amazon Web Services). This solution is designed to protect the data by using IBM Storage-based Global Mirror replication. For demonstration purposes, MySQL containerized database is installed on the on-premises IBM FlashSystem® that is connected to the Red Hat OpenShift Container Platform (OCP) cluster in the vSphere environment through the IBM block CSI driver. The volume (LUN) on IBM FlashSystem storage system is replicated by using global mirror on IBM Spectrum Virtualize for Public Cloud on AWS. Red Hat OpenShift cluster (OCP cluster) and the IBM block CSI driver plug-in are installed on AWS by using Installer-Provisioned Infrastructure (IPI) methodology. 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 Spectrum Virtualize for Public Cloud is supported and entitled, and where the issues are specific to this Blueprint implementation.

More than 80% of all data that is collected by organizations is not in a standard relational database. Instead, it is trapped in unstructured documents, social media posts, machine logs, and so on. Many organizations face significant challenges to manage this deluge of unstructured data, such as the following examples: Pinpointing and activating relevant data for large-scale analytics Lacking the fine-grained visibility that is needed to map data to business priorities Removing redundant, obsolete, and trivial (ROT) data Identifying and classifying sensitive data IBM® Spectrum Discover is a modern metadata management software that provides data insight for petabyte-scale file and Object Storage, storage on-premises, and in the cloud. This software enables organizations to make better business decisions and gain and maintain a competitive advantage. IBM Spectrum® Discover provides a rich metadata layer that enables storage administrators, data stewards, and data scientists to efficiently manage, classify, and gain insights from massive amounts of unstructured data. It improves storage economics, helps mitigate risk, and accelerates large-scale analytics to create competitive advantage and speed critical research. This IBM Redbooks® publication presents several use cases that are focused on artificial intelligence (AI) solutions with IBM Spectrum Discover. This book helps storage administrators and technical specialists plan and implement AI solutions by using IBM Spectrum Discover and several other IBM Storage products.

Collecting data on first occurance of the problem can id in problem determination and timely resolution of defects. At IBM®, this process of collecting data on first occurance if often referred to as First Failure Data Capture (FFDC). Gathering this data before reporting a defect helps to understand the problem more quickly and thoroughly, which saves time analyzing data and reduces the time and mission affects in fixing defects. Several diagnostic capabilities are built into the Linux operating system that enable you to determine the application level problems and system level problems. Collecting FFDC logs early, even before opening a defect report, helps to quickly determine whether: Symptoms match known problems (rediscovery) A report can be identified and resolved as a not-a-defect problem A workaround to reduce severity exists

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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 Lynne Snead. Lynne is the founder of Talent Evolution Systems, a behavioral analyst, consultant, training specialist, speaker, coach, Lynne has a back ground in Educational Psychology, and has specialized in organizational performance for over 20 years. Lynne is one of the original Franklin Covey co-authors, has a best seller, she created Franklin Covey’s signature Project Development process and programs, worked directly with Stephen Covey. Show Notes 2:25 – Can data define behavior in someone? 7:19 – How do you complete an assessment? 9:46 – Attributes of assessments 20:25 – Coaching advice

Lynne Snead - LinkedIn Talent Evolution Systems Lynne’s email: [email protected] Leadership and self deception  How will you measure your life? Lynne’s List of Recommended Leadership Books Leadership and Self Deception, Arbinger Institute7 Habits of Highly Effective People, Stephen R. Covey (Senior)5 Levels of Leadership, John Maxwell. What Got You Here Won’t Get You There, Marshall GoldsmithConversational Intelligence, Judith GlaserThe Speed of Trust, Stephen M.R. Covey (Junior) Emotional Intelligence, Daniel GolemanThe New Art of Managing People, Tony Alessandra and Phil HunsakerHow to Influence People, John MaxwellHumble Inquiry, Edgar H. Schein Humble Leadership, Edgar H. Schein and Peter ScheinLeadership is an Art, Max De PreeThe Secrets to Winning at Office Politics, Marie G. McIntyreThe Power of Focus, Jack Canfield, Mark Victor Hansen, Les HewittThe Power of Self-Confidence, Brian Tracy  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.

This easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard, if not impossible, to do using native Power BI tools. For example, you will learn to score Power BI data using custom data science models and powerful models from Microsoft Cognitive Services. The R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration but become easier by leveraging the capabilities of R and Python. If you are a business analyst, data analyst, or a data scientist who wants to push Power BI and transform it from being just a business intelligence tool into an advanced data analytics tool, then this is the book to help you do that. What You Will Learn Create advanced data visualizations via R using the ggplot2 package Ingest data using R and Python to overcome some limitations of Power Query Apply machine learning models to your data using R and Python without the need of Power BI premium capacity Incorporate advanced AI in Power BI without the need of Power BI premium capacity via Microsoft Cognitive Services, IBM Watson Natural Language Understanding, and pre-trained models in SQL Server Machine Learning Services Perform advanced string manipulations not otherwise possible in Power BI using R and Python Who This Book Is For Power users, data analysts, and data scientists who want to go beyond Power BI’s built-in functionality to create advanced visualizations, transform data in ways not otherwise supported, and automate data ingestion from sources such as SQL Server and Excel in a more concise way

Send us a text 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.

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 Lynne Snead. Lynne is the founder of Talent Evolution Systems, a behavioral analyst, consultant, training specialist, speaker, coach, Lynne has a back ground in Educational Psychology, and has specialized in organizational performance for over 20 years. Lynne is one of the original Franklin Covey co-authors, has a best seller, she created Franklin Covey’s signature Project Development process and programs, worked directly with Stephen Covey. 1:30 – Lynne talks about her background 5:40 – Lynne’s coaching specialty and mission statement 10:30 – Why don’t all leaders have coaches? 12:08 – Why do you differentiate corporate coaching from life coaching? 16:27 - Do you believe in the element of natural state?  18:32 – How many individuals have you coached? 19:49 – What constitutes a great leader? 20:55 – What are the common mistakes in a leader? 24:44 – Steer them back on track

Lynne Snead - LinkedIn Talent Evolution Systems Lynne’s email: [email protected] Leadership and self deception  How will you measure your life?   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.

This IBM® Redpaper® publication is a blueprint for configuration, testing results, and tuning guidelines for running SAS workloads on Red Hat Enterprise Linux that use IBM Spectrum® Scale and IBM Elastic Storage® System (ESS) 3000. IBM lab validation was conducted with the Red Hat Linux nodes running with the SAS simulator scripts that are connected to the IBM Spectrum Scale and IBM ESS 3000. Simultaneous workloads are simulated across multiple x-86 nodes running with Red Hat Linux to determine scalability against the IBM Spectrum Scale clustered file system and ESS 3000 array. This paper outlines the architecture, configuration details, and performance tuning to maximize SAS application performance with the IBM Spectrum Scale 5.0.4.3 and IBM ESS 3000. This document is intended to facilitate the deployment and configuration of the SAS applications that use IBM Spectrum Scale and IBM Elastic Storage System (ESS) 3000. 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 Spectrum Scale or IBM ESS 3000 are supported and entitled and where the issues are specific to a blueprint implementation.

Send us a text 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.

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 Amy McDermott. Amy is a science journalist at Front Matter, the magazine section of PNAS (Proceedings of the National Academy of Sciences), where she covers new and emerging research. Her background spans ecology and journalism: she has an MA in conservation biology from Columbia University and a graduate certificate from the UC Santa Cruz Science Communication Program.

Show Notes 4:20 - Amy’s background 7:25 – Amy summaries the stories on fires 11:21 – What does the data suggest about fires? 12:04 – Are there computer models on fires?  17:35 – Real world example of how models are used 19:44 – How accurate is the data? 24:25 - Examining the data 28:35 - How do you define success?  29:55 – Amy’s passion  Amy McDermott - LinkenIn Front Matter     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.

Send us a text 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.

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 Dr. Kayla Lee Growth Product Manager, Community Partnerships at IBM Quantum & Qiskit. Dr. Kayla Lee works with innovation teams across industries to understand how they can using new and emerging technologies to solve their business challenges. In her role, she serves as a bridge between business and science to help drive value for enterprise clients. Her primary focus is the new model of computation, quantum computing, working with clients to understand potential applications, prioritize use cases, and build a business strategy to prepare for the future of computing.

Al and Dr. Lee try and help us understand Quantum Computing as a new technology.   

Show Notes 3:03 - Dr. Lee talks about her day to day job 4:40 – The challenge  6:12 - Dr. Lee describes quantum  8:03 – What is quantum computing going to do for us that we can’t do today? 9:30 – How does this work? 17:50 – What kind of problem is quantum computing suited to answer? 19:40 – Will quantum computing replace traditional computing? 23:36 – Who can use quantum computing? 32:30 – Security and quantum 33:50 – Dr. Lee’s team Dr. Kayla Lee - LinkedIn IBM Quantum Computing  Qiskit HBCU Center Driving Diversity and Inclusion in Quantum Computing IBM’s Roadmap For Scaling Quantum Technology

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.

Continuing its commitment to developing and delivering industry-leading storage technologies, IBM® introduces the IBM FlashSystem® solution that is powered by IBM Spectrum® Virtualize V8.3.1. This innovative storage offering delivers essential storage efficiency technologies and exceptional ease of use and performance, all integrated into a compact, modular design that is offered at a competitive, midrange price. The solution incorporates some of the top IBM technologies that are typically found only in enterprise-class storage systems, which raises the standard for storage efficiency in midrange disk systems. This cutting-edge storage system extends the comprehensive storage portfolio from IBM and can help change the way organizations address the ongoing information explosion. This IBM Redbooks® publication introduces the features and functions of an IBM Spectrum Virtualize V8.3.1 system through several examples. This book is aimed at pre-sales and post-sales technical support and marketing and storage administrators. It helps you understand the architecture, how to implement it, and how to take advantage of its industry-leading functions and features. Applicability: This edition applies to IBM Spectrum Virtualize V8.3.1 and the associated hardware and software that is detailed within. The screen captures included within this book might differ from the generally available (GA) version because parts of this book were written with pre-GA code. On 11 February 2020, IBM announced that it was simplifying its portfolio. This book was written by using previous models of the product line before the simplification; however, most of the general principles apply. If you are in any doubt as to their applicability, contact your local IBM representative. IBM Knowledge Center: In this book we provide links to Knowledge Center and a description of the relevant section that provides more information. Our starting point is the IBM FlashSystem 9200 family page, and the reader may have to select the product that applies to their environment.

Send us a text 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.   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.  Abstract   This week on Making Data Simple, we have a returning guest Dr. Kyu Rhee VP & Chief Health Officer IBM and IBM Watson Health, discussing the Covid-19 pandemic and how we prepare and react individually and as a country. What can we do for ourselves and how this pandemic affects the economy. And when do we see a light at the end of the tunnel. Show Notes 1. https://www.ibm.com/blogs/watson-health/author/kyurhee/ 2. https://www.ibm.com/impact/covid-19/ Connect with the Team Producer Kate Brown - LinkedIn. Producer Michael Sestak - LinkedIn. Producer Meighann Helene - LinkedIn.

Host Al Martin - LinkedIn and Twitter. Additional resources:   IBM Watson Health COVID-19 Resources: https://www.ibm.com/watson-health/covid-19 IBM Watson Health: Micromedex with Watson: https://www.ibm.com/products/dynamed-and-micromedex-with-watson How governments are rising to the challenge of COVID-19: https://www.ibm.com/blogs/watson-health/governments-agencies-rising-challenge-of-covid-19/ 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.

Summary In memory computing provides significant performance benefits, but brings along challenges for managing failures and scaling up. Hazelcast is a platform for managing stateful in-memory storage and computation across a distributed cluster of commodity hardware. On top of this foundation, the Hazelcast team has also built a streaming platform for reliable high throughput data transmission. In this episode Dale Kim shares how Hazelcast is implemented, the use cases that it enables, and how it complements on-disk data management systems.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Tree Schema is a data catalog that is making metadata management accessible to everyone. With Tree Schema you can create your data catalog and have it fully populated in under five minutes when using one of the many automated adapters that can connect directly to your data stores. Tree Schema includes essential cataloging features such as first class support for both tabular and unstructured data, data lineage, rich text documentation, asset tagging and more. Built from the ground up with a focus on the intersection of people and data, your entire team will find it easier to foster collaboration around your data. With the most transparent pricing in the industry – $99/mo for your entire company – and a money-back guarantee for excellent service, you’ll love Tree Schema as much as you love your data. Go to dataengineeringpodcast.com/treeschema today to get your first month free, and mention this podcast to get %50 off your first three months after the trial. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today! Your host is Tobias Macey and today I’m interviewing Dale Kim about Hazelcast, a distributed in-memory computing platform for data intensive applications

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Hazelcast is and its origins? What are the benefits and tradeoffs of in-memory computation for data-intensive workloads? What are some of the common use cases for the Hazelcast in memory grid? How is Hazelcast implemented?

How has the architecture evolved since it was first created?

How is the Jet streaming framework architected?

What was the motivation for building it? How do the capabilities of Jet compare to systems such as Flink or Spark Streaming?

How has the introduction of hardware capabilities such as NVMe drives influenced the market for in-memory systems? How is the governance of the open source grid and Jet projects handled?

What is the guiding heuristic for which capabilities or features to include in the open source projects vs. the commercial offerings?

What is involved in building an application or workflow on top of Hazelcast? What are the common patterns for engineers who are building on top of Hazelcast? What is involved in deploying and maintaining an installation of the Hazelcast grid or Jet streaming? What are the scaling factors for Hazelcast?

What are the edge cases that users should be aware of?

What are some of the most interesting, innovative, or unexpected ways that you have seen Hazelcast used? When is Hazelcast Grid or Jet the wrong choice? What is in store for the future of Hazelcast?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

HazelCast Istanbul Apache Spark OrientDB CAP Theorem NVMe Memristors Intel Optane Persistent Memory Hazelcast Jet Kappa Architecture IBM Cloud Paks Digital Integration Hub (Gartner)

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

Send us a text 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. 

Abstract

This week, Paul Zikopolous, IBM VP Big Data Cognitive Systems, makes a highly anticipated return to Making Data Simple. Paul gives an update to what he's been working on, including his A.I. tracking app which saw an interesting use case at a recent Luke Bryan concert. We are also given some insight to the state of data and the rest of the industry. Host Al Martin then finishes things off by discussing what it means to lead a team, and tips for growing your career. 

Connect with Paul

LinkedIn 

Twitter

IBM Blogs

Show Notes

07:02 - Read more about Watson Anywhere here. 

20:20 - Check out Auto AI here.

Connect with the Team

Producer Liam Seston - LinkedIn.

Producer Lana Cosic - LinkedIn.

Producer Meighann Helene - 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.