Discover the capabilities and features of SAP Analytics Cloud to draw actionable insights from a variety of data, as well as the functionality that enables you to meet typical business challenges. With this book, you will work with SAC and enable key decision makers within your enterprise to deliver crucial business decisions driven by data and key performance indicators. Along the way you’ll see how SAP has built a strong repertoire of analytics products and how SAC helps you analyze data to derive better business solutions. This book begins by covering the current trends in analytics and how SAP is re-shaping its solutions. Next, you will learn to analyze a typical business scenario and map expectations to the analytics solution including delivery via a single platform. Further, you will see how SAC as a solution meets each of the user expectations, starting with creation of a platform for sourcing data from multiple sources, enabling self-service for a spectrum of business roles, across time zones and devices. There’s a chapter on advanced capabilities of predictive analytics and custom analytical applications. Later there are chapters explaining the security aspects and their technical features before concluding with a chapter on SAP’s roadmap for SAC. Empower Decision Makers with SAP Analytics Cloud takes a unique approach of facilitating learning SAP Analytics Cloud by resolving the typical business challenges of an enterprise. These business expectations are mapped to specific features and capabilities of SAC, while covering its technical architecture block by block. What You Will Learn Work with the features and capabilities of SAP Analytics Cloud Analyze the requirements of a modern decision-support system Use the features of SAC that make it a single platform for decision support in a modern enterprise. See how SAC provides a secure and scalable platform hosted on the cloud Who This Book Is For Enterprise architects, SAP BI analytic solution architects, and developers.

Before the onset of COVID-19, the healthcare community was already moving to meet the challenges of a growing global population. By collecting record amounts of clinical data electronically and making significant progress on neural network-based AI approaches, the industry now has the potential to build powerful predictive analytics systems. The focus will accelerate the shift from a one-size-fits-all approach to individualized medicine. But several questions remain. What are the plausible outcomes for the world of predictive analytics in both the short and long term? What does the care pathway look like if everything is predicted? And with patient populations and healthcare needs increasing exponentially, how can the industry deliver care in a sustainable and cost-effective way? This comprehensive report, written by Jaquie Finn and Dr. Gavin Troughton with Cambridge Consultants, explores the possibilities. You’ll learn: How predictive analytics plays a part across all stages of the care pathway The foundational enablers for predictive analytics How healthcare economics figure into the equation Predictive analytics and today’s healthcare system The future of predictive analytics in healthcare

This blog compares Predictive vs Prognostic analytics and gives a quick view into systems dynamics and causal modeling. If it sparks your interest, watch for an upcoming series of articles connecting the practices of systems thinking, causal analysis, and analytics.

Originally published at: https://www.eckerson.com/articles/looking-at-the-future-through-analytics-predictive-vs-prognostic

Did curiosity kill the cat? Perhaps. A claim could be made that a LACK of curiosity can (and should!) kill an analyst's career! On this episode, Dr. Debbie Berebichez, who, as Tim noted, sorta' pegs out on the extreme end of the curiosity spectrum, joined the show to explore the subject: the societal norms that (still!) often discourage young women from exploring and developing their curiosity; exploratory data analysis as one way to spark curiosity about a data set; the (often) misguided expectations of "the business" when it comes to analytics and data science (and the imperative to continue to promote data literacy to combat them), and more! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Summary Data engineering is a constantly growing and evolving discipline. There are always new tools, systems, and design patterns to learn, which leads to a great deal of confusion for newcomers. Daniel Molnar has dedicated his time to helping data professionals get back to basics through presentations at conferences and meetups, and with his most recent endeavor of building the Pipeline Data Engineering Academy. In this episode he shares advice on how to cut through the noise, which principles are foundational to building a successful career as a data engineer, and his approach to educating the next generation of data practitioners. This was a useful conversation for anyone working with data who has found themselves spending too much time chasing the latest trends and wishes to develop a more focused approach to their work.

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! Are you bogged down by having to manually manage data access controls, repeatedly move and copy data, and create audit reports to prove compliance? How much time could you save if those tasks were automated across your cloud platforms? Immuta is an automated data governance solution that enables safe and easy data analytics in the cloud. Our comprehensive data-level security, auditing and de-identification features eliminate the need for time-consuming manual processes and our focus on data and compliance team collaboration empowers you to deliver quick and valuable data analytics on the most sensitive data to unlock the full potential of your cloud data platforms. Learn how we streamline and accelerate manual processes to help you derive real results from your data at dataengineeringpodcast.com/immuta. 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 Daniel Molnar about being a data janitor and how to cut through the hype to understand what to learn for the long run

Interview

Introduction How did you get involved in the area of data management? Can you start by describing your thoughts on the current state of the data management industry? What is your strategy for being effective in the face of so much complexity and conflicting needs for data? What are some of the common difficulties that you see data engineers contend with, whether technical or social/organizational? What are the core fundamentals that you thin

This blog is about Continuous Intelligence (CI) and how it integrates historical and real-time analytics to operate, monitor and tune systems of all types. Our next blogs will explore architectural approaches to CI, and how to navigate the trade offs it introduces to your organization.

Originally published at: https://www.eckerson.com/articles/continuous-intelligence-the-nexus-of-data-integration-analytics-and-operations

Data literacy is one of the most sought after cultural transformations of 2020. In episode #47 we discussed 'Why we should stop saying 'data literacy'. Today's guest, Jordan Morrow, shared the episode on Linkedin sparking off some very interesting and even defensive responses from the data literacy social media mafia. Today, Jordan joins the podcast to share his unique point of view on the topic. Known as the pioneer of data literacy, tune in to learn how to start a data literacy practice, the best audience for data literacy skills, and what the most important data literacy skills are. Knowledge bombs galore!

 [16:43]  - Key Quote: "Not everyone needs to be a data scientist, but everyone should develop skills in data analytics in today's day and age." – Jordan Morrow​ [22:53]  - The order that data literacy skills should be taught in​ [29:25]  - Other phrases that could be used instead of data literacy​ For full show notes, and the links mentioned visit: https://bibrainz.com/podcast/66

Enjoyed the Show?  Please leave us a review on iTunes. Check out our sponsor! Are you a BI/Analytics leader who is tired of creating useless reports or dashboards? Are you struggling to get users over to your BI portal? Ever thought about embedding your analytics? If so, then you have to check out Logi Composer, the first ever out of the box development experience for teams who want to get up and running fast! Logi Analytics is offering AoF Listeners a special 14-day trial to get up and running fast! Just visit - logianalytics.com/aof

Inform your own analyses by seeing how one of the best data analysts in the world approaches analytics problems Analytics Stories: How to Make Good Things Happen is a thoughtful, incisive, and entertaining exploration of the application of analytics to real-world problems and situations. Covering fields as diverse as sports, finance, politics, healthcare, and business, Analytics Stories bridges the gap between the oft inscrutable world of data analytics and the concrete problems it solves. Distinguished professor and author Wayne L. Winston answers questions like: Was Liverpool over Barcelona the greatest upset in sports history? Was Derek Jeter a great infielder What's wrong with the NFL QB rating? How did Madoff keep his fund going? Does a mutual fund’s past performance predict future performance? What caused the Crash of 2008? Can we predict where crimes are likely to occur? Is the lot of the American worker improving? How can analytics save the US Republic? The birth of evidence-based medicine: How did James Lind know citrus fruits cured scurvy? How can I objectively compare hospitals? How can we predict heart attacks in real time? How does a retail store know if you're pregnant? How can I use A/B testing to improve sales from my website? How can analytics help me write a hit song? Perfect for anyone with the word “analyst” in their job title, Analytics Stories illuminates the process of applying analytic principles to practical problems and highlights the potential pitfalls that await careless analysts.

Just a few years can be a lifetime in the data world, but there's reason to expect that PowerBI, which has already taken the business intelligence world by storm, is set up for at least a ten-year reign? Why is that? What do you need to know about PowerBI? That's exactly what today's guest Patrick Leblanc is going to cover! As the co-host of the famous Guy in a Cube YouTube channel (with over 100,000 followers), and Principal Program Manager at Microsoft, Patrick share's his unbiased view of whether or not he feels that PowerBI is here to stay. He also shares practical ways to embrace PowerBI in your organization and gives some of his thoughts about the future about PowerBI! Whether you have Power BI or your considering it this episode is a must listen and share! Enjoy!

  [32:41] - Patrick on getting started in Power BI: "I say you take a really slow step toward that solution, and the first thing you need to do is really look at your data." [33:50] - Patrick on Power BI Adoption: "That's the big tip, you have to be gradual with these people." [43:58] - Patrick on trying Power BI: "You can honestly just get into PowerBI desktop, which is free, do everything you need globally, kick the tires on it if it doesn't work, it cost you some time, because of your human resource cost, but for the most part, you're going to pay them anyway. So it costs you nothing." For full show notes, and the links mentioned visit: https://bibrainz.com/podcast/65

Enjoyed the Show?  Please leave us a review on iTunes. Free Data Storytelling Training Register before it sells out again! Our BI Data Storytelling Mastery Accelerator 3-Day Live Workshop new dates are finally available. Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of the workshop, you'll leave with a clear BI delivery action plan. Register today!

Summary Data warehouse technology has been around for decades and has gone through several generational shifts in that time. The current trends in data warehousing are oriented around cloud native architectures that take advantage of dynamic scaling and the separation of compute and storage. Firebolt is taking that a step further with a core focus on speed and interactivity. In this episode CEO and founder Eldad Farkash explains how the Firebolt platform is architected for high throughput, their simple and transparent pricing model to encourage widespread use, and the use cases that it unlocks through interactive query speeds.

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! Today’s episode of the Data Engineering Podcast is sponsored by Datadog, a SaaS-based monitoring and analytics platform for cloud-scale infrastructure, applications, logs, and more. Datadog uses machine-learning based algorithms to detect errors and anomalies across your entire stack—which reduces the time it takes to detect and address outages and helps promote collaboration between Data Engineering, Operations, and the rest of the company. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial. If you start a trial and install Datadog’s agent, Datadog will send you a free T-shirt. 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 Eldad Farkash about Firebolt, a cloud data warehouse optimized for speed and elasticity on structured and semi-structured data

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Firebolt is and your motivation for building it? How does Firebolt compare to other data warehouse technologies what unique features does it provide? The lines between a data warehouse and a data lake have been blurring in recent years. Where on that continuum does Firebolt lie? What are the unique use cases that Firebolt allows for? How do the performance characteristics of Firebolt change the ways that an engineer should think about data modeling? What technologies might someone replace with Firebolt? How is Firebolt architected and how has the design evolved since you first began working on it? What are some of the most challenging aspects of building a data warehouse platform that is optimized for speed? How do you ha

"Learning Tableau 2020" is a comprehensive resource designed to strengthen your understanding of Tableau. It takes you from mastering the fundamentals to achieving proficiency in advanced visualization and data handling techniques. Through this book, you will gain the ability to create impactful data visualizations and interactive dashboards, effectively leveraging the capabilities of Tableau 2020. What this Book will help me do Effectively utilize Tableau 2020 features to develop data visualizations and dashboards. Apply advanced Tableau techniques, such as LOD and table calculations, to solve complex data analysis problems. Clean and structure data using Tableau Prep, enhancing data quality and reliability. Incorporate mapping and geospatial visualization for geographic data insights. Master storytelling with data by constructing engaging and interactive dashboards. Author(s) Joshua N. Milligan, the author of "Learning Tableau 2020," is an experienced Tableau training consultant and professional. With extensive years in the data visualization and analytics field, Joshua brings a practical perspective to the book. He excels at breaking down complex topics into accessible learning paths, making advanced Tableau concepts approachable for learners of all levels. Who is it for? This book is perfect for aspiring data analysts, IT professionals, and data enthusiasts who aim to understand and create compelling business intelligence reports. Beginners in Tableau will find the learning process straightforward due to its structured and incremental lessons. Advanced users can refine their skills with the wide range of complex examples covered. A basic familiarity with working with data is beneficial, though not required.

Many companies want to use data to gain a competitive edge in their industries--this can pose a challenge for organizations whose core business is not inherently technical. To make things worst lots of providers and consulting firms push unproven strategies and technical solutions that don't work. My guest today, Heather Smith is a data leader who has over 15 years of experience in the data science field, including recognition as one of Seattle's Top 5% of Women Engineers by Women Impact Tech.

As co-founder of Rove Technologies, Heather teaches how to set realistic expectations, when it comes to senior management and why it's so important. She shares real-world examples of where expectations were set, and unfortunately when they were not. In a more personal tone, she discusses how her female appearance has impacted her professionally. Listen in to hear Heather talk about the role of executive expectation management in data science and how you can start managing expectations correctly!

 [26:51]  - The role of executives in data management​ [29:54] - Heather on Data Literacy: "Often very smart people including executives, become dumb in the face of data."​ [45:48]  - Whether the team that built the successful model should own the model​ For full show notes, and the links mentioned visit: https://bibrainz.com/podcast/64

Enjoyed the Show?  Please leave us a review on iTunes. Check out our sponsor! Are you a BI/Analytics leader who is tired of creating useless reports or dashboards? Are you struggling to get users over to your BI portal? Ever thought about embedding your analytics? If so, then you have to check out Logi Composer, the first ever out of the box development experience for teams who want to get up and running fast! Logi Analytics is offering AoF Listeners a special 14-day trial to get up and running fast! Just visit - logianalytics.com/aof

Summary In order to scale the use of data across an organization there are a number of challenges related to discovery, governance, and integration that need to be solved. The key to those solutions is a robust and flexible metadata management system. LinkedIn has gone through several iterations on the most maintainable and scalable approach to metadata, leading them to their current work on DataHub. In this episode Mars Lan and Pardhu Gunnam explain how they designed the platform, how it integrates into their data platforms, and how it is being used to power data discovery and analytics at LinkedIn.

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! If you’ve been exploring scalable, cost-effective and secure ways to collect and route data across your organization, RudderStack is the only solution that helps you turn your own warehouse into a state of the art customer data platform. Their mission is to empower data engineers to fully own their customer data infrastructure and easily push value to other parts of the organization, like marketing and product management. With their open-source foundation, fixed pricing, and unlimited volume, they are enterprise ready, but accessible to everyone. Go to dataengineeringpodcast.com/rudder to request a demo and get one free month of access to the hosted platform along with a free t-shirt. 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 Pardhu Gunnam and Mars Lan about DataHub, LinkedIn’s metadata management and data catalog platform

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of what DataHub is and some of its back story?

What were you using at LinkedIn for metadata management prior to the introduction of DataHub? What was lacking in the previous solutions that motivated you to create a new platform?

There are a large number of other systems available for building data catalogs and tracking metadata, both open source and proprietary. What are the features of DataHub that would lead someone to use it in place of the other options? Who is the target audience for DataHub?

How do the needs of those end users influence or constrain your approach to the design and interfaces provided by DataHub?

Can you describe how DataHub is architected?

How has it evolved since yo

This book is your gateway into the world of graph analytics with Neo4j, empowering you to reveal insights hidden in connected data. By diving into real-world examples, you'll learn how to implement algorithms to uncover relationships and patterns critical for applications such as fraud detection, recommendation systems, and more. What this Book will help me do Understand fundamental concepts of the Neo4j graph database, including nodes, relationships, and Cypher querying. Effectively explore and visualize data relationships, enhancing applications like search engines and recommendations. Gain proficiency in graph algorithms such as pathfinding and spatial search to solve key business challenges. Leverage Neo4j's Graph Data Science library for machine learning and predictive analysis tasks. Implement web applications that utilize Neo4j for scalable, production-ready graph data management. Author(s) None Scifo is an experienced author in graph technologies, extensively working with Neo4j. He brings practical knowledge and a hands-on approach to the forefront, making complex topics accessible to learners of all levels. Through his work, he continues to inspire readers to harness the power of connected data effectively. Who is it for? This book is perfect for professionals like data analysts, business analysts, graph analysts, and database developers aiming to delve into graph data. It caters to those seeking to solve problems through graph analytics, whether in fraud detection, recommendation systems, or other fields. Some prior experience with Neo4j is recommended for maximal benefit.

Send us a text Adam Weinstein is currently CEO and Co-Founder of Cursor, having worked at LinkedIn as a Senior Manager of Business Development and having founded enGreet, a print-on-demand greeting card company that merged crowd-sourcing with social expressions. In this episode, he describes his data analytics company and provides insight into creating a successful startup.

Shownotes

00:00 - Check us out on YouTube and SoundCloud!   

00:10 - Connect with Producer Steve Moore on LinkedIn & Twitter   

00:15 - Connect with Producer Liam Seston on LinkedIn & Twitter.   

00:20 - Connect with Producer Rachit Sharma on LinkedIn.

00:25 - Connect with Host Al Martin on LinkedIn & Twitter.   

00:55 - Connect with Adam Weinstein on LinkedIn.

03:55 - Find out more about Cursor.

06:45 - Learn more about Cursor's Co-Founder and CEO Adam Weinstein.

13:10 - Learn more about Big Data Analytics.

19:20 - What is Python/Jupyter Notebooks?

26:35 - Learn more about Data Fluency.

35:30 - What is a startup?  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.

In Ep 50 with Jurgen Faiist, I discussed why we need a new data visualization language. In today's episode, we return to debate that topic with my good Suisse buddy Raphael Branger who is a Principal Data & Analytics Consultant at IT-Logix in Switzerland. Raphael is an IBCS Certified Consultant (he introduced me to it) as well as a Certified Disciplined Agile Practitioner with more than 18 years of experience in business intelligence and data warehousing. I met Raphael almost a decade when he invited to keynote their epic BI event in Zurich. As one of the most passionate people I've met around requirements gathering 'engineering' is he calls it, his feedback was instrumental to the ongoing enhancement of our BI Dashboard Formula methodology!

In today's episode, Raphael gives examples of why a new data viz language is needed and explains the International Business Communication Standards (IBCS) SUCCESS poster and how those standards can help. So much knowledge bombs in this one! Be sure to tune in!

 [03:01] The pros and cons of whether a new data visualization language is needed [03:15]  - User Expectations: Real-world experiences using the IBCS standards [24:45]  - How to ease communication between consumer and creator For full show notes, and the links mentioned visit: https://bibrainz.com/podcast/63

Enjoyed the Show?  Please leave us a review on iTunes. Free Data Storytelling Training Register before it sells out again! Our BI Data Storytelling Mastery Accelerator 3-Day Live Workshop new dates are finally available. Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of the workshop, you'll leave with a clear BI delivery action plan. Register today!

Summary Most databases are designed to work with textual data, with some special purpose engines that support domain specific formats. TileDB is a data engine that was built to support every type of data by using multi-dimensional arrays as the foundational primitive. In this episode the creator and founder of TileDB shares how he first started working on the underlying technology and the benefits of using a single engine for efficiently storing and querying any form of data. He also discusses the shifts in database architectures from vertically integrated monoliths to separately deployed layers, and the approach he is taking with TileDB cloud to embed the authorization into the storage engine, while providing a flexible interface for compute. This was a great conversation about a different approach to database architecture and how that enables a more flexible way to store and interact with data to power better data sharing and new opportunities for blending specialized domains.

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! Today’s episode of the Data Engineering Podcast is sponsored by Datadog, a SaaS-based monitoring and analytics platform for cloud-scale infrastructure, applications, logs, and more. Datadog uses machine-learning based algorithms to detect errors and anomalies across your entire stack—which reduces the time it takes to detect and address outages and helps promote collaboration between Data Engineering, Operations, and the rest of the company. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial. If you start a trial and install Datadog’s agent, Datadog will send you a free T-shirt. 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 Stavros Papadopoulos about TileDB, the universal storage engine

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what TileDB is and the problem that you are trying to solve with it?

What was your motivation for building it?

What are the main use cases or problem domains that you are trying to solve for?

What are the shortcomings of existing approaches to database design that prevent them from being useful for these applications?

What are the benefits of using matrices for data processing and domain modeling?

What are the challenges that you

Mike Bugembe is a speaker, consultant, and Amazon best selling author of the book Cracking the Data Code. He joins today's podcast to talk about the things that you can do that will help create successful analytics projects. After being the head of business intelligence for one of the largest diamond companies in the world, and seeing project struggle or fail, Mike went on a journey to find out why some unicorn companies like Netflix were succeeding in analytics, which traditional companies were not. In today's interview, he discusses the five key traits of a successful project and why 90% of enterprises data are missing most of them. Listen in to hear what Mike has to say about finding use cases for AI, learning how to ask data questions, and the biggest obstacles companies face when trying to unlock the value of their data.

  [03:42]  - Cracking the Data Code: 5 Things that successful unicorn companies are doing that you are not for your data projects​ [24:43]  - Key Quote: "Around 75% of investments in data fail to see any return on investment." –Mike Bugembe​ [31:18]  - How to offer a skillshare to teammates​ For full show notes, and the links mentioned visit: https://bibrainz.com/podcast/62

Enjoyed the Show?  Please leave us a review on iTunes.   Check out our sponsor! Are you a BI/Analytics leader who is tired of creating useless reports or dashboards? Are you struggling to get users over to your BI portal? Ever thought about embedding your analytics? If so, then you have to check out Logi Composer, the first ever out of the box development experience for teams who want to get up and running fast! Logi Analytics is offering AoF Listeners a special 14-day trial to get up and running fast! Just visit - logianalytics.com/aof

Whether you are an executive, departmental decision maker, or analyst, the need to leverage data and analytical techniques in order make critical business decisions is now crucial to every part of an organization. Gain Powerful Insights with SAS Viya! Smart Data Discovery with SAS Viya: Powerful Techniques for Deeper Insights provides you with the necessary knowledge and skills to conduct a smart discovery process and empower you to ask more complex questions using your data. The book highlights key components of a smart data discovery process utilizing advanced machine learning techniques, powerful capabilities from SAS Viya, and finally brings it all together using real examples and applications. With its step-by-step approach and integrated examples, the book provides a relevant and practical guide to insight discovery that goes beyond traditional charts and graphs. By showcasing the powerful visual modeling capabilities of SAS Viya, it also opens up the world of advanced analytics and machine learning techniques to a much broader set of audiences.

Summary Event based data is a rich source of information for analytics, unless none of the event structures are consistent. The team at Iteratively are building a platform to manage the end to end flow of collaboration around what events are needed, how to structure the attributes, and how they are captured. In this episode founders Patrick Thompson and Ondrej Hrebicek discuss the problems that they have experienced as a result of inconsistent event schemas, how the Iteratively platform integrates the definition, development, and delivery of event data, and the benefits of elevating the visibility of event data for improving the effectiveness of the resulting analytics. If you are struggling with inconsistent implementations of event data collection, lack of clarity on what attributes are needed, and how it is being used then this is definitely a conversation worth following.

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! If you’ve been exploring scalable, cost-effective and secure ways to collect and route data across your organization, RudderStack is the only solution that helps you turn your own warehouse into a state of the art customer data platform. Their mission is to empower data engineers to fully own their customer data infrastructure and easily push value to other parts of the organization, like marketing and product management. With their open-source foundation, fixed pricing, and unlimited volume, they are enterprise ready, but accessible to everyone. Go to dataengineeringpodcast.com/rudder to request a demo and get one free month of access to the hosted platform along with a free t-shirt. 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 Patrick Thompson and Ondrej Hrebicek about Iteratively, a platform for enforcing consistent schemas for your event data

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Iteratively and your motivation for creating it? What are some of the ways that you have seen inconsistent message structures cause problems? What are some of the common anti-patterns that you have seen for managing the structure of event messages? What are the benefits that Iteratively provides for the different roles in an organization? Can you describe the workflow for a team using