Summary Data professionals are working in a domain that is rapidly evolving. In order to stay current we need access to deeply technical presentations that aren’t burdened by extraneous marketing. To fulfill that need Pete Soderling and his team have been running the Data Council series of conferences and meetups around the world. In this episode Pete discusses his motivation for starting these events, how they serve to bring the data community together, and the observations that he has made about the direction that we are moving. He also shares his experiences as an investor in developer oriented startups and his views on the importance of empowering engineers to launch their own companies.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Listen, I’m sure you work for a ‘data driven’ company – who doesn’t these days? Does your company use Amazon Redshift? Have you ever groaned over slow queries or are just afraid that Amazon Redshift is gonna fall over at some point? Well, you’ve got to talk to the folks over at intermix.io. They have built the “missing” Amazon Redshift console – it’s an amazing analytics product for data engineers to find and re-write slow queries and gives actionable recommendations to optimize data pipelines. WeWork, Postmates, and Medium are just a few of their customers. Go to dataengineeringpodcast.com/intermix today and use promo code DEP at sign up to get a $50 discount! 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 management.For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Pete Soderling about his work to build and grow a community for data professionals with the Data Council conferences and meetups, as well as his experiences as an investor in data oriented companies

Interview

Introduction How did you get involved in the area of data management? What was your original reason for focusing your efforts on fostering a community of data engineers?

What was the state of recognition in the industry for that role at the time that you began your efforts?

The current manifestation of your community efforts is in the form of the Data Council conferences and meetups. Previously they were known as Data Eng Conf and before that was Hakka Labs. Can you discuss the evolution of your efforts to grow this community?

How has the community itself changed and grown over the past few years?

Communities form around a huge variety of focal points. What are some of the complexities or challenges in building one based on something as nebulous as data? Where do you draw inspiration and direction for how to manage such a large and distributed community?

What are some of the most interesting/challenging/unexpected aspects of community management that you have encountered?

What are some ways that you have been surprised or delighted in your interactions with the data community? How do you approach sustainability of the Data Council community and the organization itself? The tagline that you have focused on for Data Council events is that they are no fluff, juxtaposing them against larger business oriented events. What are your guidelines for fulfilling that promise and why do you think that is an important distinction? In addition to your community building you are also an investor. How did you get involved in that side of your business and how does it fit into your overall mission? You also have a stated mission to help engineers build their own companies. In your opinion, how does an engineer led business differ from one that may be founded or run by a business oriented individual and why do you think that we need more of them?

What are the ways that you typically work to empower engineering founders or encourage them to create their own businesses?

What are some of the challenges that engineering founders face and what are some common difficulties or misunderstandings related to business?

What are your opinions on venture-backed vs. "lifestyle" or bootstrapped businesses?

What are the characteristics of a data business that you look at when evaluating a potential investment? What are some of the current industry trends that you are most excited by?

What are some that you find concerning?

What are your goals and plans for the future of Data Council?

Contact Info

@petesoder on Twitter LinkedIn @petesoder on Medium

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

Data Council Database Design For Mere Mortals Bloomberg Garmin 500 Startups Geeks On A Plane Data Council NYC 2019 Track Summary Pete’s Angel List Syndicate DataOps

Data Kitchen Episode DataOps Vs DevOps Episode

Great Expectations

Podcast.init Interview

Elementl Dagster

Data Council Presentation

Data Council Call For Proposals

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

Support Data Engineering Podcast

Real-Time Data Analytics for Large-Scale Sensor Data covers the theory and applications of hardware platforms and architectures, the development of software methods, techniques and tools, applications, governance and adoption strategies for the use of massive sensor data in real-time data analytics. It presents the leading-edge research in the field and identifies future challenges in this fledging research area. The book captures the essence of real-time IoT based solutions that require a multidisciplinary approach for catering to on-the-fly processing, including methods for high performance stream processing, adaptively streaming adjustment, uncertainty handling, latency handling, and more. Examines IoT applications, the design of real-time intelligent systems, and how to manage the rapid growth of the large volume of sensor data Discusses intelligent management systems for applications such as healthcare, robotics and environment modeling Provides a focused approach towards the design and implementation of real-time intelligent systems for the management of sensor data in large-scale environments

It's 1:00 AM, and you can't sleep. The paid search manager needs to know whether brand keywords can be turned off without impacting revenue. The product team needs the latest A/B test results analyzed before they can start on their next sprint. The display media intern urgently needs your help figuring out why the campaign tracking parameters he added for the campaign that launches in two days are breaking the site (you're pretty sure he's confusing "&" and "?" again). And the team running the site redesign needs to know YESTERDAY what fields they need to include in the new headless CMS to support analytics. You're pulled in a million directions, and every request is valid. How do you manage your world without losing your sanity? On this episode, analytics philosopher Astrid Illum from DFDS joins the gang to discuss those challenges. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Summary Data engineers are responsible for building tools and platforms to power the workflows of other members of the business. Each group of users has their own set of requirements for the way that they access and interact with those platforms depending on the insights they are trying to gather. Benn Stancil is the chief analyst at Mode Analytics and in this episode he explains the set of considerations and requirements that data analysts need in their tools and. He also explains useful patterns for collaboration between data engineers and data analysts, and what they can learn from each other.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! 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 management.For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Counsil. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Benn Stancil, chief analyst at Mode Analytics, about what data engineers need to know when building tools for analysts

Interview

Introduction How did you get involved in the area of data management? Can you start by describing some of the main features that you are looking for in the tools that you use? What are some of the common shortcomings that you have found in out-of-the-box tools that organizations use to build their data stack? What should data engineers be considering as they design and implement the foundational data platforms that higher order systems are built on, which are ultimately used by analysts and data scientists?

In terms of mindset, what are the ways that data engineers and analysts can align and where are the points of conflict?

In terms of team and organizational structure, what have you found to be useful patterns for reducing friction in the product lifecycle for data tools (internal or external)? What are some anti-patterns that data engineers can guard against as they are designing their pipelines? In your experience as an analyst, what have been the characteristics of the most seamless projects that you have been involved with? How much understanding of analytics are necessary for data engineers to be successful in their projects and careers?

Conversely, how much understanding of data management should analysts have?

What are the industry trends that you are most excited by as an analyst?

Contact Info

LinkedIn @bennstancil on Twitter Website

Parting Question

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

Closing Announcements

Thank you for

Dive deep into the advanced capabilities of Elasticsearch 7.0 with this expert-level guide. In this book, you will explore the most effective techniques and tools for building, indexing, and querying advanced distributed search engines. Whether optimizing performance, scaling applications, or integrating with big data analytics, this guide empowers you with practical skills and insights. What this Book will help me do Master ingestion pipelines and preprocess documents for faster and more efficient indexing. Model search data optimally for complex and varied real-world applications. Perform exploratory data analyses using Elasticsearch's robust features. Integrate Elasticsearch with modern analytics platforms like Kibana and Logstash. Leverage Elasticsearch with Apache Spark and machine learning libraries for real-time advanced analytics. Author(s) None Wong is a seasoned Elasticsearch expert with years of real-world experience developing enterprise-grade search and analytics systems. With a passion for innovation and teaching, Wong enjoys breaking down complex technical concepts into digestible learning experiences. His work reflects a pragmatic and results-driven approach to teaching Elasticsearch. Who is it for? This book is ideal for Elasticsearch developers and data engineers with some prior experience who are looking to elevate their skills to an advanced level. It suits professionals seeking to enhance their expertise in building scalable search and analytics solutions. If you aim to master sophisticated Elasticsearch operations and real-time integrations, this book is tailored for you.

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: •Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling •Understand how use ML tools in real world business problems, where causation matters more that correlation •Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science.

SQL for Data Analytics provides readers with the tools and knowledge to use SQL effectively for extracting, analyzing, and interpreting complex datasets. Whether you're working with time-series data, geospatial data, or textual data, this book combines insightful explanations with practical guidance to enhance your data analysis capabilities. What this Book will help me do Perform advanced statistical calculations using SQL functions like WINDOW. Develop and optimize queries for better performance and faster results. Analyze and work with geospatial, time-series, and text datasets effectively. Debug problematic SQL queries and ensure their correctness. Create robust SQL pipelines and integrate them with other analytics tools. Author(s) The authors of SQL for Data Analytics, Upom Malik, Matt Goldwasser, and Benjamin Johnston, are seasoned professionals experienced in both the practical and theoretical aspects of SQL and data analysis. They bring their collective expertise to guide readers through the essentials and advanced usage of SQL in analytics. Who is it for? This book is aimed at database engineers aspiring to delve into analytics, backend developers wanting to improve their data handling skills, and data professionals aiming to enhance their SQL proficiency. A basic understanding of SQL and databases will help readers follow along and maximize their learning.

How can one of my students go from a BI startup to a Big five acquisition? By using our BI Dashboard Formula (BIDF) methodology. This week, I'm talking to Andrew Mackay, co-founder of former Dubai based BI startup Team base, now director of digital services at PwC Middle East. I met Andrew six years ago when he registered for our online BI Dashboard Formula methodology online course, as he was seeking to differentiate his BI offerings in the middle east. This inspiring conversation outlines both his reasons for using BIDF, how he operationalized it and how it led to his recent acquisition by PwC. There is so much to be learned from his focus and use of the BIDF methodology! Enjoy!

Sponsor

This exciting season of AOF is sponsored by our BI Data Storytelling Mastery Accelerator 3-Day Live workshop. Our first workshop coming up on Sept 17-19 almost full and VIP is sold out! Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of three days, you'll leave with the tools and techniques to truly engage your users. Register today

Enjoyed the Show?  Please leave us a review on iTunes.   For all links and resources mentioned visit: https://bibrainz.com/podcast/33

The goal of self-service analytics is to empower business people to build their own reports, dashboards, and predictive models. If that happens, does your company still need a corporate business intelligence team?

Originally published at https://www.eckerson.com/articles/organizing-success-part-1-organize-bi-team

Summary Managing big data projects at scale is a perennial problem, with a wide variety of solutions that have evolved over the past 20 years. One of the early entrants that predates Hadoop and has since been open sourced is the HPCC (High Performance Computing Cluster) system. Designed as a fully integrated platform to meet the needs of enterprise grade analytics it provides a solution for the full lifecycle of data at massive scale. In this episode Flavio Villanustre, VP of infrastructure and products at HPCC Systems, shares the history of the platform, how it is architected for scale and speed, and the unique solutions that it provides for enterprise grade data analytics. He also discusses the motivations for open sourcing the platform, the detailed workflow that it enables, and how you can try it for your own projects. This was an interesting view of how a well engineered product can survive massive evolutionary shifts in the industry while remaining relevant and useful.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! To connect with the startups that are shaping the future and take advantage of the opportunities that they provide, check out Angel List where you can invest in innovative business, find a job, or post a position of your own. Sign up today at dataengineeringpodcast.com/angel and help support this show. 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 management.For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Counsil. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. 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 Your host is Tobias Macey and today I’m interviewing Flavio Villanustre about the HPCC Systems project and his work at LexisNexis Risk Solutions

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what the HPCC system is and the problems that you were facing at LexisNexis Risk Solutions which led to its creation?

What was the overall state of the data landscape at the time and what was the motivation for releasing it as open source?

Can you describe the high level architecture of the HPCC Systems platform and some of the ways that the design has changed over the years that it has been maintained? Given how long the project has been in use, c

Send us a text The authors of Machine Learning for Dummies – Judith Hurwitz, and Daniel Kirsch — are here to help you. In this episode, Judith, Daniel and Al discuss the state of machine learning today, how to use it to advance your business as well as discoveries they made while writing their book. Learn how small and large businesses alike can find insights from data to enhance relationships with customers. We’ll also share where you can get a copy of Machine Learning for Dummies at no cost. Show notes 01.00 Connect with Al Martin on Twitter and LinkedIn. 01.10 Connect with Kate Nichols on Twitter and LinkedIn. 01.15 Connect with Fatima Sirhindi on Twitter and LinkedIn. 02.00 Learn more about Hurwitz & Associates. 02.10 Connect with Judith Hurwitz on Twitter, LinkedIn and find her blog here. 03.20 Connect with Daniel Kirsch on Twitter and  Hurwitz & Associates 04.00 Read Machine Learning for Dummiesby Judith Hurwitz and Daniel Kirsch. 04.40 Learn what neural nets are here. 04.50 Learn more about Arthur Samuel here. 05.00 Learn more about how Deep Blue beat the world chess champion. 15.39 Learn more about Apache Hadoop.  17.30 Learn more about IBM Watson. 26.50 Find Cognitive Computing and Big Data Analytics by Judith Hurwitz, Marcia Kaufman and Adrian Bowles. 27.45 FindEverybody Lies: Big Data, New Data and What the Internet Can Tell Us About Who We Really Are by Seth Stephens-Davidowitz. 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.

What did you think of my very first solo podcast? If you haven't listened to it yet, go to Analytics on Fire - 31: My Top BI User Adoption Hacks (Part 1). Today is Part 2, where I wrap up my list of top BI User Adoption hacks to help you be successful. Use adoption is the only KPI that BI teams should be focused on IMO. Enjoy and I'd love your feedback!

Sponsor

This exciting season of AOF is sponsored by our BI Data Storytelling Mastery Accelerator 3-Day Live workshop. Our first workshop coming up on Sept 17-19 almost full and VIP is sold out! Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of three days, you'll leave with the tools and techniques to truly engage your users. Register today

Enjoyed the Show?  Please leave us a review on iTunes.   For all links and resources mentioned visit: https://bibrainz.com/podcast/32

Before a company hires data science talent, they should understand the role and types of data scientists. Failing to differentiate between research, applied, and citizen data scientist can result in appointing the wrong people on crucial projects. To continue our previous episode's discussion, we invited Alex Vayner for a second time to get an answer to the question: What is a data scientist?

Alex Vayner is a Partner and Americas Data & AI Practice Leader for PA Consulting Group, an innovation and transformation consultancy. Alex has spent his entire career in data & analytics, with his last five roles focused on building and running high-performance data science teams and capabilities in consulting and corporate environments. Before joining PA Consulting, Alex ran the NA Data Science & AI practice at Capgemini. He joined Capgemini from Equifax, where he served as VP, Global Data Innovation Leader, building a team responsible for pioneering disruptive data & analytics solutions for clients across all industries.

Explore how data analytics leaders can balance the use of specialists and generalists.

Originally published at https://www.eckerson.com/articles/specialists-or-generalists

In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them. In this book, you’ll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more. After using this handy quick reference guide, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis. What You Will Learn Import data with readr Work with categories using forcats, time and dates with lubridate, and strings with stringr Format data using tidyr and then transform that data using magrittr and dplyrWrite functions with R for data science, data mining, and analytics-based applications Visualize data with ggplot2 and fit data to models using modelr Who This Book Is For Programmers new to R's data science, data mining, and analytics packages. Some prior coding experience with R in general is recommended.

Summary Data is only valuable if you use it for something, and the first step is knowing that it is available. As organizations grow and data sources proliferate it becomes difficult to keep track of everything, particularly for analysts and data scientists who are not involved with the collection and management of that information. Lyft has build the Amundsen platform to address the problem of data discovery and in this episode Tao Feng and Mark Grover explain how it works, why they built it, and how it has impacted the workflow of data professionals in their organization. If you are struggling to realize the value of your information because you don’t know what you have or where it is then give this a listen and then try out Amundsen for yourself.

Announcements

Welcome to the Data Engineering Podcast, the show about modern data management 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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Finding the data that you need is tricky, and Amundsen will help you solve that problem. And as your data grows in volume and complexity, there are foundational principles that you can follow to keep data workflows streamlined. Mode – the advanced analytics platform that Lyft trusts – has compiled 3 reasons to rethink data discovery. Read them at dataengineeringpodcast.com/mode-lyft. 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 management.For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, the Open Data Science Conference, and Corinium Intelligence. Upcoming events include the O’Reilly AI Conference, the Strata Data Conference, and the combined events of the Data Architecture Summit and Graphorum. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. 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 Your host is Tobias Macey and today I’m interviewing Mark Grover and Tao Feng about Amundsen, the data discovery platform and metadata engine that powers self service data access at Lyft

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Amundsen is and the problems that it was designed to address?

What was lacking in the existing projects at the time that led you to building a new platform from the ground up?

How does Amundsen fit in the larger ecosystem of data tools?

How does it compare to what WeWork is building with Marquez?

Can you describe the overall architecture of Amundsen and how it has evolved since you began working on it?

What were the main assumptions that you had going into this project and how have they been challenged or updated in the process of building and using it?

What has been the impact of Amundsen on the workflows

Highlights  Spotify, Apple Music, and Deezer’s biggest playlists are growing — both in terms of follower count and also track count — but what does that mean for artists looking to land a big add?Mission   Good morning, it’s Rutger here at Chartmetric with your 3-minute Data Dump where we upload charts, artists and playlists into your brain so you can stay up on the latest in the music data world.We’re on the socials at “chartmetric”, that’s Chartmetric, no “S ”- follow us on LinkedIn, Instagram, Twitter, or Facebook, and talk to us! We’d love to hear from you.Feature: Labels PageHey Rutger, it’s Jason — sorry to interrupt, but can I just do a quick product update? Of course, what’s up?Thanks, man. Hi Chartmetric fans, you may or may not have gotten a chance to check out the new Labels Page feature that we discussed in the last podcast episode this week.We’ve temporarily pulled the feature back from its soft release because we just don’t think it’s up to the music analytics standard we strive for.If you’ve been with us for some time, you’ve seen how dedicated we are to innovating and as we say in the tech world, sometimes “breaking things”.Well, we’ve gotten a lot of your feedback and realize that we jumped the gun a bit early and we need to better clean, organize and visualize the label metadata that we have, which is what we do best.So we recognize the issue, and we are working swiftly to bring the Labels Page back with verve and more importantly, accuracy!Back to our regularly scheduled program, take it away, Rutger!Thanks, Jason!DateThis is your Data Dump for Friday, August 2nd, 2019.How 2019’s Playlist Growth Might Affect Emerging ArtistsThese days, getting onto streaming’s top playlists is sort of the name of the game.It really determines the visibility of emerging artists and cements the longevity of established ones.So, it got us wondering…. What’s been going on on the top playlists in 2019?Hitting the Playlists tab on the Chartmetric homepage brings up tons of playlist information for Spotify, Apple Music, Deezer, and Amazon.From there, we can compare everything going on when it comes to the playlists claiming the top spots across a number of different measurements.On Spotify, Today’s Top Hits maintains the highest follow number, starting the year off with 22.3M and hitting 23.6M by the end of June.That’s 5.8 percent increase for that six month period.On Deezer, Les Titres Du Moment claims the top follower spot, and over the same period, experienced only about 1 percent growth from 9.8M followers to 9.9M followers.Digging in a bit deeper, we can also compare playlist length, aka number of tracks.For that six month period, for example, Spotify’s Hot Country playlist grew 31.4 percent in length, while Apple Music’s The A-List: Pop playlist grew the same amount.But those aren’t the highest numbers. Spotify’s EDM-focused Mint playlist grew 35.8 percent, and Apple’s Hip-Hop-oriented Gymflow playlist grew 66.7 percent.Overall, Apple added more tracks to its top playlists than Spotify did — about 11 percent vs. 23 percent, to be exact. The growth of these playlists, both in terms of follower count and also track count, means a higher chance of an emerging artist landing on one of them and a significant increase in visibility if they do.However, it also makes it more likely that they get lost in the noise, making it hard to capitalize on an otherwise super exciting add.Knowing the genre breakdown of tracks and also the country distribution of artists can help, but we’ll have to save that for another episode. You can also tell us what you find by doing your own digging at chartmetric.com!Outro That’s it for your Daily Data Dump for Friday, August 2nd, 2019. This is Rutger from Chartmetric.Article links and show notes are at: podcast.chartmetric.comAnd if you like what we’re doing, don’t forget to leave us a rating or review!Happy Friday, have a great weekend, and we’ll see you next week!

I'm so nervous that my armpits are sweating! But I'm trying something new and different. Solo podcasts focused on topics that you voted you need help with. 

Today's topic is, My Top 10 BI User Adoption Hacks (Part 1). User adoption is a problem that most BI teams experience regardless of how great our dashboards perform or look, so I created a checklist of items that should evoke action.

Sponsor

This exciting season of AOF is sponsored by our BI Data Storytelling Mastery Accelerator 3-Day Live workshop. Our first workshop coming up on Sept 17-19 almost full and VIP is sold out! Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of three days, you'll leave with the tools and techniques to truly engage your users. Register today!

Enjoyed the Show?  Please leave us a review on iTunes.   For all links and resources mentioned visit: https://bibrainz.com/podcast/31

Now expanded and updated with modern best practices, this is the most complete guide to Microsoft's DAX language for business intelligence, data modeling, and analytics. Expert Microsoft BI consultants Marco Russo and Alberto Ferrari help you master everything from table functions through advanced code and model optimization. You'll learn exactly what happens under the hood when you run a DAX expression, and use this knowledge to write fast, robust code. This edition focuses on examples you can build and run with the free Power BI Desktop, and helps you make the most of the powerful syntax of variables (VAR) in Power BI, Excel, or Analysis Services. Want to leverage all of DAX's remarkable capabilities? This no-compromise "deep dive" is exactly what you need. Related Content Video: Introduction to Microsoft Power BI (Video), Data Analysis Fundamentals with Excel (Video) Perform powerful data analysis with DAX for Power BI, SQL Server, and Excel · Master core DAX concepts, including calculated columns, measures, and calculation groups · Work efficiently with basic and advanced table functions · Understand evaluation contexts and the CALCULATE and CALCULATETABLE functions · Perform time-based calculations · Use calculation groups and calculation items · Use syntax of variables (VAR) to write more readable, maintainable code · Express diverse and unusual relationships with DAX, including many-to-many relationships and bidirectional filters · Master advanced optimization techniques, and improve performance in aggregations · Optimize data models to achieve better compression · Measure DAX query performance with DAX Studio and learn how to optimize your DAX

New challenges to analytics platforms have prompted SAS to create new responses. The software giant responds with automation and decision support tools.