Abstract This IBM® Redbooks® publication introduces the latest member of the IBM Z platform, the IBM z14 Model ZR1 (Machine Type 3907). It includes information about the Z environment and how it helps integrate data and transactions more securely, and provides insight for faster and more accurate business decisions. The z14 ZR1 is a state-of-the-art data and transaction system that delivers advanced capabilities, which are vital to any digital transformation. The z14 ZR1 is designed for enhanced modularity, which is in an industry standard footprint. This system excels at the following tasks: Securing data with pervasive encryption Transforming a transactional platform into a data powerhouse Getting more out of the platform with IT Operational Analytics Providing resilience towards zero downtime Accelerating digital transformation with agile service delivery Revolutionizing business processes Mixing open source and Z technologies This book explains how this system uses new innovations and traditional Z strengths to satisfy growing demand for cloud, analytics, and open source technologies. With the z14 ZR1 as the base, applications can run in a trusted, reliable, and secure environment that improves operations and lessens business risk.

Summary

Managing an analytics project can be difficult due to the number of systems involved and the need to ensure that new information can be delivered quickly and reliably. That challenge can be met by adopting practices and principles from lean manufacturing and agile software development, and the cross-functional collaboration, feedback loops, and focus on automation in the DevOps movement. In this episode Christopher Bergh discusses ways that you can start adding reliability and speed to your workflow to deliver results with confidence and consistency.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Christopher Bergh about DataKitchen and the rise of DataOps

Interview

Introduction How did you get involved in the area of data management? How do you define DataOps?

How does it compare to the practices encouraged by the DevOps movement? How does it relate to or influence the role of a data engineer?

How does a DataOps oriented workflow differ from other existing approaches for building data platforms? One of the aspects of DataOps that you call out is the practice of providing multiple environments to provide a platform for testing the various aspects of the analytics workflow in a non-production context. What are some of the techniques that are available for managing data in appropriate volumes across those deployments? The practice of testing logic as code is fairly well understood and has a large set of existing tools. What have you found to be some of the most effective methods for testing data as it flows through a system? One of the practices of DevOps is to create feedback loops that can be used to ensure that business needs are being met. What are the metrics that you track in your platform to define the value that is being created and how the various steps in the workflow are proceeding toward that goal?

In order to keep feedback loops fast it is necessary for tests to run quickly. How do you balance the need for larger quantities of data to be used for verifying scalability/performance against optimizing for cost and speed in non-production environments?

How does the DataKitchen platform simplify the process of operationalizing a data analytics workflow? As the need for rapid iteration and deployment of systems to capture, store, process, and analyze data becomes more prevalent how do you foresee that feeding back into the ways that the landscape of data tools are designed and developed?

Contact Info

LinkedIn @ChrisBergh on Twitter Email

Parting Question

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

Links

DataOps Manifesto DataKitchen 2017: The Year Of DataOps Air Traffic Control Chief Data Officer (CDO) Gartner W. Edwards Deming DevOps Total Quality Management (TQM) Informatica Talend Agile Development Cattle Not Pets IDE (Integrated Devel

Delve into the world of big data with 'Modern Big Data Processing with Hadoop.' This comprehensive guide introduces you to the powerful capabilities of Apache Hadoop and its ecosystem to solve data processing and analytics challenges. By the end, you will have mastered the techniques necessary to architect innovative, scalable, and efficient big data solutions. What this Book will help me do Master the principles of building an enterprise-level big data strategy with Apache Hadoop. Learn to integrate Hadoop with tools such as Apache Spark, Elasticsearch, and more for comprehensive solutions. Set up and manage your big data architecture, including deployment on cloud platforms with Apache Ambari. Develop real-time data pipelines and enterprise search solutions. Leverage advanced visualization tools like Apache Superset to make sense of data insights. Author(s) None R. Patil, None Kumar, and None Shindgikar are experienced big data professionals and accomplished authors. With years of hands-on experience in implementing and managing Apache Hadoop systems, they bring a depth of expertise to their writing. Their dedication lies in making complex technical concepts accessible while demonstrating real-world best practices. Who is it for? This book is designed for data professionals aiming to advance their expertise in big data solutions using Apache Hadoop. Ideal readers include engineers and project managers involved in data architecture and those aspiring to become big data architects. Some prior exposure to big data systems is beneficial to fully benefit from this book's insights and tutorials.

In this podcast, Amy Gershkoff(@amygershkoff) talks about the ingredients of a successful data science team. Amy sheds light on the challenges of building a successful team and how businesses could align themselves to get maximum out of their data science practice. Amy discussed some tricks, tips, and easy to execute strategies to keep the data science team and practice at the top of its efficiency. This is a great session for anyone who wants to be part of a winning and thriving data science practice within the organization.

Timeline:

0:29 Amy's journey. 8:39 Working on Obama's campaign. 15:35 Getting started with a data project. 20:39 First steps for creating a data science team. 27:53 Hiring a data scientist recruiter. 33:00 Building an internal data science workforce. 40:00 Hiring the right data scientist. 42:36 Tips for a data scientist to become a good hire. 44:42 Leadership getting educated in data science. 48:05 How to build diversity in the data science field. 52:52 Being bias free. 54:20 Amy's reading list. 56:06 Key takeaways. 

Youtube: https://youtu.be/0PBK5dfQaUk iTunes: http://apple.co/2zMLByT

Podcast Link: https://futureofdata.org/amygershkoff-on-building-winning-datascience-team/

Amy's BIO: Dr. Amy Gershkoff consults and advises technology companies across the globe. She is the former Chief Data Officer for Ancestry, the world's leading genealogy and consumer genomics company. Prior to joining Ancestry, she was Chief Data Officer at Zynga. Previously, Amy built and led the Customer Analytics & Insights team and led the Global Data Science team at eBay. She has also served as the Chief Data Scientist for WPP, Data Alliance, where she worked across WPP’s more than 350 operating companies worldwide to create integrated data and technology solutions. She was also the Head of Media Planning at Obama for America 2012, where she was the architect of Obama’s advertising strategy and designed the campaign's analytics systems.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Wanna Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Dive right into the powerful world of Microsoft Power BI with this comprehensive guide. This book takes you through every step of mastering Power BI, from data modeling to creating actionable visualizations. You'll find clear explanations and practical steps to improve your data analytics and enhance business decision-making. What this Book will help me do Learn to connect and transform data using Power Query M Language to create clean, structured datasets. Understand how to design scalable and performance-optimized Power BI Data Models for effective analytics. Develop professional, visually appealing and interactive reports and dashboards to convey insights confidently. Implement best practices for managing Power BI solutions, including deployment, version control, and monitoring. Gain practical knowledge to administer Power BI across organizational structures, ensuring security and efficiency. Author(s) None Powell is a seasoned expert in business intelligence and a passionate educator in the field of data analytics. With extensive hands-on experience in Microsoft Power BI, None has supported many organizations in unlocking the potential of their data. The approachable writing style reflects a real-world yet proficient understanding of Power BI's capabilities. Who is it for? This book is ideal for business intelligence professionals looking to deepen their expertise in Microsoft Power BI. Readers already familiar with basic BI concepts and Power BI will gain significant technical depth. It suits professionals keen to enhance their data modeling, visualization, and analytics skills. If you're aiming to create impactful dashboards and benefit from advanced insights, this book is for you.

"Data Analysis with R, Second Edition" is your ultimate guide to mastering data analysis in R, encompassing everything from foundational concepts to advanced techniques. You will learn to manipulate, analyze, and visualize data effectively, applying cutting-edge R packages like ggplot2 and dplyr. Through rich examples, this comprehensive book thoroughly prepares you to tackle real-world analytical challenges. What this Book will help me do Understand foundational statistical reasoning and sampling methods. Perform hypothesis testing and apply Bayesian methods to data analysis. Build and evaluate regression, classification, and time series models. Handle messy and missing data using advanced R techniques and methods. Optimize performance through parallel processing, Rcpp, and efficient data manipulation. Author(s) Chris Burnett is an experienced data analyst with over 15 years of expertise harnessing R for insights. A passionate advocate for accessible computing, Chris integrates practical exercises and rich examples to demystify complex analytical techniques. Their experience and dedication shine in this approachable yet detailed guide. Who is it for? This book is ideal for budding and professional data analysts or data scientists who wish to deepen their expertise in R. It suits learners with a basic understanding of R who aim to extend their proficiency in applied data analysis. The guide provides significant value for professionals aiming to implement effective analytical models. Readers seeking to grow within the analytics sector will find this resource indispensable.

This IBM® Redpaper™ publication is a comprehensive guide that covers the IBM Power System AC922 server (8335-GTG and 8335-GTW models). The Power AC922 server is the next generation of the IBM Power processor-based systems, which are designed for deep learning and artificial intelligence (AI), high-performance analytics, and high-performance computing (HPC). This paper introduces the major innovative Power AC922 server features and their relevant functions: Powerful IBM POWER9™ processors that offer 16 cores at 2.6 GHz with 3.09 GHz turbo performance or 20 cores at 2.0 GHz with 2.87 GHz turbo for the 8335-GTG Eighteen cores at 2.98 GHz with 3.26 GHz turbo performance or 22 at 2.78 GHz cores with 3.07 GHz turbo for the 8335-GTW IBM Coherent Accelerator Processor Interface (CAPI) 2.0, IBM OpenCAPI™, and second-generation NVIDIA NVLink technology for exceptional processor-to-accelerator intercommunication Up to six dedicated NVIDIA Tesla V100 GPUs This publication is for professionals who want to acquire a better understanding of IBM Power Systems™ products and is intended for the following audiences: Clients Sales and marketing professionals Technical support professionals IBM Business Partners Independent software vendors (ISVs) This paper expands the set of IBM Power Systems documentation by providing a desktop reference that offers a detailed technical description of the Power AC922 server. This paper does not replace the current marketing materials and configuration tools. It is intended as an extra source of information that, together with existing sources, can be used to enhance your knowledge of IBM server solutions.

In this podcast Stephen Gatchell (@stephengatchell) from @Dell talks about the ingredients of a successful data scientist. He sheds light on the importance of data governance and compliance in defining a robust data science strategy. He suggested tactical steps that executives could take in starting their journey to a robust governance framework. He talked about how to take away the scare from governance. He gave insights on some of the things leaders could do today to build robust data science teams and framework. This podcast is great for leaders seeking some tactical insights into building a robust data science framework.

Timeline:

0:29 Stephen's journey. 4:45 Dell's customer experience journey. 7:39 Suggestions for a startup in regard to customer experience. 12:02 Building a center of excellence around data. 15:29 Data ownership. 19:18 Fixing data governance. 24:02 Fixing the data culture. 29:40 Distributed data ownership and data lakes. 32:50 Understanding data lakes. 35:50 Common pitfalls and opportunities in data governance. 38:50 Pleasant surprises in data governance. 41:30 Ideal data team. 44:04 Hiring the right candidates for data excellence. 46:13 How do I know the "why"? 49:05 Stephen's success mantra. 50:56 Stephen's best read. Steve's Recommended Read: Big Data MBA: Driving Business Strategies with Data Science by Bill Schmarzo http://amzn.to/2HWjOyT

Podcast Link: https://futureofdata.org/want-to-fix-datascience-fix-governance-by-stephengatchell-futureofdata/

Steve's BIO: Stephen is currently a Chief Data Officer Engineering & Data Lake at Dell and serves on the Dell Information Quality Governance Office and the Dell IT Technology Advisory Board, developing Dell’s corporate strategies for the Business Data Lake, Advanced Analytics, and Information Asset Management. Stephen also serves as a Customer Insight Analyst for the Chief Technology Office, analyzing customer technology challenges and requirements. Stephen has been awarded the People’s Choice Award by the Dell Total Customer Experience Team for the Data Governance and Business Data Lake project, as well as a Chief Technology Officer Innovation finalist for utilizing advanced analytics for customer configurations improving product development and product test coverage. Prior to Stephen’s current role, he managed Dell’s Global Product Development Lab Operations team developing internal cloud orchestration and automation environments, an Information Systems Executive for IBM leading acquisition conversion efforts, and was VP of Enterprise Systems and Operations managing mission-critical Information Systems for Telelogic (a Swedish public software firm). Stephen has an MBA from Southern New Hampshire University, a BSBA, and an AS in Finance from Northeastern University.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Wanna Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

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In this podcast, Henry Eckerson interviews Dave Wells on the current health and future of the data warehouse. Wells acknowledges that data warehouses are struggling, but argues they are still necessary and cannot be replaced by data lakes. He then explains what the role of the modern data warehouse should be, practical steps forward for evolving the data warehouse, and much more.

Wells is an advisory consultant, educator, and industry analyst dedicated to building meaningful connections throughout the path from data to business value. He works at the intersection of information management and business management, driving business impact through analytics, business intelligence, and active data management. More than forty years of information systems experience combined with over ten years of business management give him a unique perspective about the connections among business, information, data, and technology. Knowledge sharing and skill building are Dave’s passions, carried out through consulting, speaking, teaching, and writing.

He is now the practice director of data management at Eckerson Group, cofounder and director of education at eLearningCurve, and a faculty member at The Data Warehousing Institute.

Mastering Qlik Sense is a comprehensive guide designed to empower you to utilize Qlik Sense for advanced data analytics and dynamic visualizations. This book provides detailed insights into creating seamless Business Intelligence solutions tailored to your needs. Whether you're building dashboards, optimizing data models, or exploring Qlik Cloud functionalities, this book has you covered. What this Book will help me do Build interactive and insightful dashboards using Qlik Sense's intuitive tools. Learn to model data efficiently and apply best practices for optimized performance. Master the Qlik Sense APIs and create advanced custom extensions. Understand enterprise security measures including role-based access controls. Gain expertise in migrating from QlikView to Qlik Sense effectively Author(s) Juan Ignacio Vitantonio is an experienced expert in Business Intelligence solutions and data analytics. With a profound understanding of Qlik technologies, Juan has developed and implemented impactful BI solutions across various industries. His writing reflects his practical knowledge and passion for empowering users with actionable insights into data. Who is it for? This book is perfect for BI professionals, data analysts, and organizations aiming to leverage Qlik Sense for advanced analytics. Ideal for those with a foundational grasp of Qlik Sense, it also provides comprehensive guidance for QlikView users transitioning to Qlik Sense. If you want to improve your BI solutions and data-driven decision-making skills, this book is for you.

"SQL Server 2017 Developer's Guide" provides a comprehensive approach to learning and utilizing the new features introduced in SQL Server 2017. From advanced Transact-SQL to integrating R and Python into your database projects, this book equips you with the knowledge to design and develop efficient database applications tailored to modern requirements. What this Book will help me do Master new features in SQL Server 2017 to enhance database application development. Implement In-Memory OLTP and columnstore indexes for optimal performance. Utilize JSON support in SQL Server to integrate modern data formats. Leverage R and Python integration to apply advanced data analytics and machine learning. Learn Linux and container deployment options to expand SQL Server usage scenarios. Author(s) The authors of "SQL Server 2017 Developer's Guide" are industry veterans with extensive experience in database design, business intelligence, and advanced analytics. They bring a practical, hands-on writing style that helps developers apply theoretical concepts effectively. Their commitment to teaching is evident in the clear and detailed guidance provided throughout the book. Who is it for? This book is ideal for database developers and solution architects aiming to build robust database applications with SQL Server 2017. It's a valuable resource for business intelligence developers or analysts seeking to harness SQL Server 2017's advanced features. Some familiarity with SQL Server and T-SQL is recommended to fully leverage the insights provided by this book.

In this podcast, Ashok Srivastava(@aerotrekker) talks about how the code of creating a great data science practice goes through #PeopleDataTech, and he suggested how to handle unreasonable expectations from reasonable technologies. He shared his journey through culturally diverse organizations and how he successfully build data science practice. He shared his role in Intuit and some of the AI/Machine learning focus in his current role. This podcast is a must for all data-driven leaders, strategists, and wannabe technologists tasked to grow their organization and build a robust data science practice.

Timeline:

0:29 Ashok's journey. 9:58 The role of a CDO at Intuit. 12:45 Ashok's secret to success working with diverse workforces. 15:42 Building a culture of data science. 19:03 Tactical strategies to convince the leadership about data. 22:03 Comparing a data officer and analytics officer. 24:09 Ownership of data. 27:33 Best practices for putting together a data team. 30:16 Best practices for a company to build a good data science practice. 32:40 Who's the ideal data science candidate? 35:17 Data citizens as data leaders. 37:47 Use cases of AI at Intuit. 39:55 Deciding which product deserves AI. 42:35 Disruptive nature of AI. 45:05 Ashok's success mantra. 46:56 Ashok's favorite reads. 49:15 Key takeaways.

Ashok's Recommended Read: Guns, Germs, and Steel: The Fates of Human Societies - Jared Diamond Ph.D. http://amzn.to/2C4bLMT Collapse: How Societies Choose to Fail or Succeed: Revised Edition - by Jared Diamond http://amzn.to/2C3Bu8f

Podcast Link: https://futureofdata.org/ashok-srivastavaaerotrekker-on-winning-the-art-of-datascience/

Ashok's BIO: Ashok N. Srivastava, Ph.D., is the Senior Vice President and Chief Data Officer at Intuit. He is responsible for setting the vision and direction for large-scale machine learning and AI across the enterprise to help power prosperity across the world. He is hiring hundreds of people in machine learning, AI, and related areas at all levels.

Previously, he was Vice President of Big Data and Artificial Intelligence Systems and the Chief Data Scientist at Verizon. He is an Adjunct Professor at Stanford in the Electrical Engineering Department and is the Editor-in-Chief of the AIAA Journal of Aerospace Information Systems. Ashok is a Fellow of the IEEE, the American Association for the Advancement of Science (AAAS), and the American Institute of Aeronautics and Astronautics (AIAA).

Ashok has a range of business experience, including serving as Senior Director at Blue Martini Software and Senior Consultant at IBM.

He has won numerous awards, including the Distinguished Engineering Alumni Award, the NASA Exceptional Achievement Medal, the IBM Golden Circle Award, the Department of Education Merit Fellowship, and several fellowships from the University of Colorado. Ashok holds a Ph.D. in Electrical Engineering from the University of Colorado at Boulder.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Wanna Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. How do you get started? Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others. You'll learn how to: Identify the metrics you need to measure Run experiments and A/B tests Ask the right questions of your data experts Understand statistical terms and concepts Create effective charts and visualizations Avoid common mistakes

In this podcast, Bill Schmarzo talks about the ingredients of successful data science practice, team, and executives. Bill shared his insights on what some leaders in the industries are doing and some challenges seen in the successful deployment. Bill shared his key take on ingredients for some of the successful hires. This podcast is great for growth mindset executives willing to learn about creating a successful data science practice.

Timeline: 0:29 Bill's journey. 5:05:00 Bill's current role. 7:04 Data science adoption challenges for businesses. 9:33 The good side of data science adoption. 11:22 How is data science changing business. 14:34 Strategies behind distributed IT. 18:35 Analysing the current amount of data. 21:50 Who should own the idea of data science? 24:34 The right background for a CDO. 25:52 Bias in IT. 29:35 Hacks to keep yourself bias-free. 31:58 Team vs. tool for putting together a good data-driven practice. 34:54 Value cycle in data science. 37:10 Maturity model. 39:17 Convincing culture heavy businesses to adopt data. 42:47 Keeping oneself sane during the technological disruption. 46:20 Hiring the right talent. 51:46 Ingredients of a good data science hire. 56:00 Bill's success mantra. 59:07 Bill's favorite reads. 1:00:36 Closing remarks.

Bill's Recommended Read: Moneyball: The Art of Winning an Unfair Game by Michael Lewis http://amzn.to/2FqBFg8 Big Data MBA: Driving Business Strategies with Data Science by Bill Schmarzo http://amzn.to/2tlZAvP

Podcast Link: https://futureofdata.org/schmarzo-dellemc-on-ingredients-of-healthy-datascience-practice-futureofdata-podcast/

Bill's BIO: Bill Schmarzo is the CTO for the Big Data Practice, where he is responsible for working with organizations to help them identify where and how to start their big data journeys. He's written several white papers, is an avid blogger, and is a frequent speaker on the use of Big Data and data science to power the organization's key business initiatives. He is a University of San Francisco School of Management Fellow, where he teaches the "Big Data MBA" course.

Bill has over three decades of experience in data warehousing, BI, and analytics. Bill authored EMC's Vision Workshop methodology that links an organization's strategic business initiatives with their supporting data and analytic requirements and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute's faculty as the head of the analytic applications curriculum.

Bill holds a master's degree in Business Administration from the University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science, and Business Administration from Coe College.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Wanna Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Learn how to leverage SQL Server 2017 Machine Learning Services and the R programming language to create robust, efficient data analysis and machine learning solutions. This book provides actionable insights and practical examples to help you implement and manage database-oriented analytics and predictive modeling. What this Book will help me do Understand and use SQL Server 2017 Machine Learning Services integrated with R. Gain experience in installing, configuring, and maintaining R services in SQL Server. Create and operationalize predictive models using RevoScaleR and other R packages. Improve database solutions by incorporating advanced analytics techniques. Monitor and manage R-based services effectively for reliable production solutions. Author(s) Tomaž Kaštrun and None Koesmarno bring a wealth of expertise as practitioners and educators in data science and SQL Server technologies. They share their experience innovatively, making intricate subjects approachable. Their unified teaching method ensures readers can directly benefit from practical examples and real-world applications. Who is it for? This book is tailored for database administrators, data analysts, and data scientists eager to integrate R with SQL Server. It caters to professionals with varying levels of R experience who are looking to enhance their proficiency in database-oriented analytics. Readers will benefit most if they are motivated to design effective, data-driven solutions in SQL Server environments.

In this episode, Wayne Eckerson and Mike Masciandaro discuss keys to creating successful reports that users will love and use. Masciandaro provides in-depth explanations of each key (easy, drill, monitor, accurate, relevant, timely, responsive, and secure).

Masciandaro is a veteran business intelligence practitioner who recently retired from an illustrious career at Dow Chemical as director of BI. During that time, Mike saw and did just about everything there is to do in the world of BI, data, and analytics. He is now intent on sharing his hard-won knowledge with others.

Are you ready to master Teradata, one of the leading relational database management systems for data warehousing? In the "Teradata Cookbook," you will find over 85 recipes covering vital tasks like querying, performance tuning, and administrative operations. With clear and practical instructions, this book will equip you with the skills necessary to optimize data storage and analytics in your organization. What this Book will help me do Master Teradata's advanced features for efficient data warehousing applications. Understand and employ Teradata SQL for effective data manipulation and analytics. Explore practical solutions for Teradata administration tasks, including user and security management. Learn performance tuning techniques to enhance the efficiency of your queries and processes. Acquire detailed knowledge about Teradata's architecture and its unique capabilities. Author(s) The authors of "Teradata Cookbook" are experienced professionals in database management and data warehousing. With a deep understanding of Teradata's architecture and use in real-world applications, they bring a wealth of knowledge to each of the book's recipes. Their focus is to provide practical, actionable insights to help you tackle challenges you may face. Who is it for? This book is ideal for database administrators, data analysts, and professionals working with data warehousing who want to leverage the power of Teradata. Whether you are new to this database management system or looking to enhance your expertise, this cookbook provides practical solutions and in-depth insights, making it an essential resource.

This IBM® Redpaper™ publication addresses IBM Patterns for Cognitive Systems topics to anyone developing, implementing, and using Cognitive Solutions on IBM Power Systems™ servers. Moreover, this publication provides documentation to transfer the knowledge to the sales and technical teams. This publication describes IBM Patterns for Cognitive Systems. Think of a pattern as a use case for a specific scenario, such as event-based real-time marketing for real-time analytics, anti-money laundering, and addressing data oceans by reducing the cost of Hadoop. These examples are just a few of the cognitive patterns that are now available. Patterns identify and address challenges for cognitive infrastructures. These entry points then help you understand where you are on the cognitive journey and enables IBM to demonstrate the set of solutions capabilities for each lifecycle stage. This book targets technical readers, including IT specialist, systems architects, data scientists, developers, and anyone looking for a guide about how to unleash the cognitive capabilities of IBM Power Systems by using patterns.

For the second year in a row for the podcast -- but the first appearance since Moe joined the crew -- we headed to the Hunguest Grandhotel Galya outside Budapest for Superweek, one of the most unique conference experiences in the digital analytics industry: comfortably isolated over an hour outside of Budapest in a beautiful setting, it's a temporary community of, for, and by the analyst. With sessions ranging from GDPR to machine learning to attribution to media analytics, the spaces before, between, and after the presentations were extended discussions with great people on a wide range of topics. The "fireside chat" on Wednesday evening was a recording of the podcast with a live audience, where we had attendees to share tips and ideas that we found particularly intriguing. And had quite a bit of fun along the way. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

In this podcast, Chuck Rehberg from Trigent Software sat with Vishal to discuss how, as a technologist, leaders should think about connecting technology to help solve real business pains. Chuck also shared some of the best practices technologists could adopt to built successful integrity-filled bias-free teams and solutions.

Timeline 0:29 Chuck's journey. 8:45 Chuck's role in Trigent. 14:18 Trigent's niche clients. 16:26 Semantics and Trigent model. 18:42 What is semantics? 22:00 The state of semantics today. 28:00 Best practices for businesses to use technology optimally. 33:13 Tips for businesses to remain stable in the time of disruptive technology. 36:18 App technology vis a vis enterprise stack. 39:43 Perspectives on the bias. 43:40 Measuring KPIs for success. 48:16 Ingredients of a good technology team. 50:56 Creating a technology team from scratch. 54:42 Things to be done in semantics. 58:52 Chuck's success mantra. 1:02:24 Chuck's favorite reads. 1:07:05 Closing remarks.

Chuck's Recommended Read: World Hypotheses: A Study in Evidence - by Stephen C. Pepper http://amzn.to/2GXGYVV Women, Fire and Dangerous Things: What Categories Reveal About the Mind - by George Lakoff http://amzn.to/2GWIQOA How to Solve It: A New Aspect of Mathematical Method (Princeton Science Library) - by G. Polya (Author),‎ John H. Conway (Foreword, Contributor) http://amzn.to/2BLECtw The Better Angels of Our Nature: Why Violence Has Declined - by Steven Pinker http://amzn.to/2EaLQZI Finite and Infinite Games – by James Carse (Author) http://amzn.to/2BLfIdx Being Mortal: Medicine and What Matters in the End - by Atul Gawande http://amzn.to/2BhgBtp

Podcast Link: https://futureofdata.org/chuckrehberg-trigentsoftware-translating-technology-solve-business-problems-futureofdata/

Here is Chuck's Bio: As CTO at Trigent Software and Chief Scientist at Semantic Insights, Chuck Rehberg has developed patented high-performance rules engine technology and advanced natural language understanding technologies that empower a new generation of semantic research solutions.

Chuck has more than thirty years in the high-tech industry, developing leading-edge solutions in the areas of Artificial Intelligence, Semantic Technologies, analytics, and product configuration software.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Wanna Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy