talk-data.com
Activities & events
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The AI Data Paradox: High Trust in Models, Low Trust in Data
2025-11-09 · 23:53
Ariel Pohoryles
– guest
@ Rivery
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Tobias Macey
– host
Summary In this episode of the Data Engineering Podcast Ariel Pohoryles, head of product marketing for Boomi's data management offerings, talks about a recent survey of 300 data leaders on how organizations are investing in data to scale AI. He shares a paradox uncovered in the research: while 77% of leaders trust the data feeding their AI systems, only 50% trust their organization's data overall. Ariel explains why truly productionizing AI demands broader, continuously refreshed data with stronger automation and governance, and highlights the challenges posed by unstructured data and vector stores. The conversation covers the need to shift from manual reviews to automated pipelines, the resurgence of metadata and master data management, and the importance of guardrails, traceability, and agent governance. Ariel also predicts a growing convergence between data teams and application integration teams and advises leaders to focus on high-value use cases, aggressive pipeline automation, and cataloging and governing the coming sprawl of AI agents, all while using AI to accelerate data engineering itself. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Composable data infrastructure is great, until you spend all of your time gluing it together. Bruin is an open source framework, driven from the command line, that makes integration a breeze. Write Python and SQL to handle the business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. Bruin allows you to build end-to-end data workflows using AI, has connectors for hundreds of platforms, and helps data teams deliver faster. Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform. Go to dataengineeringpodcast.com/bruin today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud.Your host is Tobias Macey and today I'm interviewing Ariel Pohoryles about data management investments that organizations are making to enable them to scale AI implementationsInterview IntroductionHow did you get involved in the area of data management?Can you start by describing the motivation and scope of your recent survey on data management investments for AI across your respondents?What are the key takeaways that were most significant to you?The survey reveals a fascinating paradox: 77% of leaders trust the data used by their AI systems, yet only half trust their organization's overall data quality. For our data engineering audience, what does this suggest about how companies are currently sourcing data for AI? Does it imply they are using narrow, manually-curated "golden datasets," and what are the technical challenges and risks of that approach as they try to scale?The report highlights a heavy reliance on manual data quality processes, with one expert noting companies feel it's "not reliable to fully automate validation" for external or customer data. At the same time, maturity in "Automated tools for data integration and cleansing" is low, at only 42%. What specific technical hurdles or organizational inertia are preventing teams from adopting more automation in their data quality and integration pipelines?There was a significant point made that with generative AI, "biases can scale much faster," making automated governance essential. From a data engineering perspective, how does the data management strategy need to evolve to support generative AI versus traditional ML models? What new types of data quality checks, lineage tracking, or monitoring for feedback loops are required when the model itself is generating new content based on its own outputs?The report champions a "centralized data management platform" as the "connective tissue" for reliable AI. How do you see the scale and data maturity impacting the realities of that effort?How do architectural patterns in the shape of cloud warehouses, lakehouses, data mesh, data products, etc. factor into that need for centralized/unified platforms?A surprising finding was that a third of respondents have not fully grasped the risk of significant inaccuracies in their AI models if they fail to prioritize data management. In your experience, what are the biggest blind spots for data and analytics leaders?Looking at the maturity charts, companies rate themselves highly on "Developing a data management strategy" (65%) but lag significantly in areas like "Automated tools for data integration and cleansing" (42%) and "Conducting bias-detection audits" (24%). If you were advising a data engineering team lead based on these findings, what would you tell them to prioritize in the next 6-12 months to bridge the gap between strategy and a truly scalable, trustworthy data foundation for AI?The report states that 83% of companies expect to integrate more data sources for their AI in the next year. For a data engineer on the ground, what is the most important capability they need to build into their platform to handle this influx?What are the most interesting, innovative, or unexpected ways that you have seen teams addressing the new and accelerated data needs for AI applications?What are some of the noteworthy trends or predictions that you have for the near-term future of the impact that AI is having or will have on data teams and systems?Contact Info LinkedInParting 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 shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.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.Links BoomiData ManagementIntegration & Automation DemoAgentstudioData Connector Agent WebinarSurvey ResultsData GovernanceShadow ITPodcast EpisodeThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA |
Data Engineering Podcast |
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How Create Music Group tops the charts with embedded analytics
2024-04-10 · 22:30
Eddie Jauregui
– Director of Data Strategy
@ Create Music Group
,
Mary Grace Glascott
– Outbound Product Manager
@ Google Cloud
,
Sharon Zhang
– Product Manager
@ Looker
As an API-first and cloud-born platform, Looker has always been focused on providing developers with the tools to build scalable and unique data applications and embedded analytics experiences. Learn about the latest Looker Embedded product enhancements and about Create Music Group's journey to create amazing data products by partnering with Google Cloud. |
Google Cloud Next '24
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Unlocking The Value Of Data Across The Organization Through User Friendly Data Tools With Prophecy
2022-05-23 · 01:00
Raj Bains
– Founder
@ Prophecy
,
Tobias Macey
– host
Summary The interfaces and design cues that a tool offers can have a massive impact on who is able to use it and the tasks that they are able to perform. With an eye to making data workflows more accessible to everyone in an organization Raj Bains and his team at Prophecy designed a powerful and extensible low-code platform that lets technical and non-technical users scale data flows without forcing everyone into the same layers of abstraction. In this episode he explores the tension between code-first and no-code utilities and how he is working to balance the strengths without falling prey to their shortcomings. 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 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 $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at dataengineeringpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan. Your host is Tobias Macey and today I’m interviewing Raj Bains about how improving the user experience for data tools can make your work as a data engineer better and easier Interview Introduction How did you get involved in the area of data management? What are the broad categories of data tool designs that are available currently and how does that impact what is possible with them? What are the points of friction that are introduced by the tools? Can you share some of the types of workarounds or wasted effort that are made necessary by those design elements? What are the core design principles that you have built into Prophecy to address these shortcomings? How do those user experience changes improve the quality and speed of work for data engineers? How has the Prophecy platform changed since we last spoke almost a year ago? What are the tradeoffs of low code systems for productivity vs. flexibility and creativity? What are the most interesting, innovative, or unexpected approaches to developer experience that you have seen for data tools? What are the most interesting, unexpected, or challenging lessons that you have learned while working on user experience optimization for data tooling at Prophecy? When is it more important to optimize for computational efficiency over developer productivity? What do you have planned for the future of Prophecy? Contact Info LinkedIn @_raj_bains on Twitter 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 Links Prophecy Podcast Episode CUDA Clustrix Hortonworks Apache Hive Compilerworks Podcast Episode Airflow Databricks Fivetran Podcast Episode Airbyte Podcast Episode Streamsets Change Data Capture Apache Pig Spark Scala Ab Initio Type 2 Slowly Changing Dimensions AWS Deequ Matillion Podcast Episode Prophecy SaaS The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast |
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Summary Machine learning has become a meaningful target for data applications, bringing with it an increase in the complexity of orchestrating the entire data flow. Flyte is a project that was started at Lyft to address their internal needs for machine learning and integrated closely with Kubernetes as the execution manager. In this episode Ketan Umare and Haytham Abuelfutuh share the story of the Flyte project and how their work at Union is focused on supporting and scaling the code and community that has made Flyte successful. 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 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 $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! This episode is brought to you by Acryl Data, the company behind DataHub, the leading developer-friendly data catalog for the modern data stack. Open Source DataHub is running in production at several companies like Peloton, Optum, Udemy, Zynga and others. Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product at dataengineeringpodcast.com/acryl RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. Data lake architectures provide the best combination of massive scalability and cost reduction, but they aren’t always the most performant option. That’s why Kyligence has built on top of the leading open source OLAP engine for data lakes, Apache Kylin. With their AI augmented engine they detect patterns from your critical queries, automatically build data marts with optimized table structures, and provide a unified SQL interface across your lake, cubes, and indexes. Their cost-based query router will give you interactive speeds across petabyte scale data sets for BI dashboards and ad-hoc data exploration. Stop struggling to speed up your data lake. Get started with Kyligence today at dataengineeringpodcast.com/kyligence Your host is Tobias Macey and today I’m interviewing Ketan Umare and Haytham Abuelfutuh about Flyte, the open source and kubernetes-native orchestration engine for your data systems Interview Introduction How did you get involved in the area of data management? Can you describe what Flyte is and the story behind it? What was missing in the ecosystem of available tools that made it necessary/worthwhile to create Flyte? Workflow orchestrators have been around for several years and have gone through a number of generational shifts. How would you characterize Flyte’s position in the ecosystem? What do you see as the closest alternatives? What are the core differentiators that might lead someone to choose Flyte over e.g. Airflow/Prefect/Dagster? What are the core primitives that Flyte exposes for building up complex workflows? Machine learning use cases have been a core focus since the project’s inception. What are some of the ways that that manifests in the design and feature set? Can you describe the architecture of Flyte? How have the design and goals of the platform changed/evolved since you first started working on it? What are the changes in the data ecosystem that have had the most substantial impact on the Flyte project? (e.g. roadmap, integrations, pushing people toward adoption, etc.) What is the process for setting up a Flyte deployment? What are the user personas that you prioritize in the design and feature development for Flyte? What is the workflow for someone building a new pipeline in Flyte? What are the patterns that you and the community have established to encourage discovery and reuse of granular task definitions? Beyond code reuse, how can teams scale usage of Flyte at the company/organization level? What are the affordances that you have created to facilitate local development and testing of workflows while ensuring a smooth transition to production? What are the patterns that are available for CI/CD of workflows using Flyte? How have you approached the design of data contracts/type definitions to provide a consistent/portable API for defining inter-task dependencies across languages? What are the available interfaces for extending Flyte and building integrations with other components across the data ecosystem? Data orchestration engines are a natural point for generating and taking advantage of rich metadata. How do you manage creation and propagation of metadata within and across the framework boundaries? Last year you founded Union to offer a managed version of Flyte. What are the features that you are offering beyond what is available in the open source? What are the opportunities that you see for the Flyte ecosystem with a corporate entity to invest in expanding adoption? What are the most interesting, innovative, or unexpected ways that you have seen Flyte used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Flyte? When is Flyte the wrong choice? What do you have planned for the future of Flyte? Contact Info Ketan Umare Haytham Abuelfutuh 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 Links Flyte Slack Channel Union.ai Kubeflow Airflow AWS Step Functions Protocol Buffers XGBoost MLFlow Dagster Podcast Episode Prefect Podcast Episode Arrow Parquet Metaflow Pytorch Podcast.init Episode dbt FastAPI Podcast.init Interview Python Type Annotations Modin Podcast.init Interview Monad Datahub Podcast Episode OpenMetadata Podcast Episode Hudi Podcast Episode Iceberg Podcast Episode Great Expectations Podcast Episode Pandera Union ML Weights and Biases Whylogs Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By: a… |
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Evolving And Scaling The Data Platform at Yotpo
2022-05-02 · 00:15
Summary Building a data platform is an iterative and evolutionary process that requires collaboration with internal stakeholders to ensure that their needs are being met. Yotpo has been on a journey to evolve and scale their data platform to continue serving the needs of their organization as it increases the scale and sophistication of data usage. In this episode Doron Porat and Liran Yogev explain how they arrived at their current architecture, the capabilities that they are optimizing for, and the complex process of identifying and evaluating new components to integrate into their systems. This is an excellent exploration of the decisions and tradeoffs that need to be made while building such a complex system. 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 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 $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! This episode is brought to you by Acryl Data, the company behind DataHub, the leading developer-friendly data catalog for the modern data stack. Open Source DataHub is running in production at several companies like Peloton, Optum, Udemy, Zynga and others. Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product at dataengineeringpodcast.com/acryl RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. The most important piece of any data project is the data itself, which is why it is critical that your data source is high quality. PostHog is your all-in-one product analytics suite including product analysis, user funnels, feature flags, experimentation, and it’s open source so you can host it yourself or let them do it for you! You have full control over your data and their plugin system lets you integrate with all of your other data tools, including data warehouses and SaaS platforms. Give it a try today with their generous free tier at dataengineeringpodcast.com/posthog Your host is Tobias Macey and today I’m interviewing Doron Porat and Liran Yogev about their experiences designing and implementing a self-serve data platform at Yotpo Interview Introduction How did you get involved in the area of data management? Can you describe what Yotpo is and the role that data plays in the organization? What are the core data types and sources that you are working with? What kinds of data assets are being produced and how do those get consumed and re-integrated into the business? What are the user personas that you are supporting and what are the interfaces that they are comfortable interacting with? What is the size of your team and how is it structured? You recently posted about the current architecture of your data platform. What was the starting point on your platform journey? What did the early stages of feature and platform evolution look like? What was the catalyst for making a concerted effort to integrate your systems into a cohesive platform? What was the scope and directive of the project for building a platform? What are the metrics and capabilities that you are optimizing for in the structure of your data platform? What are the organizational or regulatory constraints that you needed to account for? What are some of the early decisions that affected your available choices in later stages of the project? What does the current state of your architecture look like? How long did it take to get to where you are today? What were the factors that you considered in the various build vs. buy decisions? How did you manage cost modeling to understand the true savings on either side of that decision? If you were to start from scratch on a new data platform today what might you do differently? What are the decisions that proved helpful in the later stages of your platform development? What are the most interesting, innovative, or unexpected ways that you have seen your platform used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on designing and implementing your platform? What do you have planned for the future of your platform infrastructure? Contact Info Doron Liran 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 Links Yotpo Data Platform Architecture Blog Post Greenplum Databricks Metorikku Apache Hive CDC == Change Data Capture Debezium Podcast Episode Apache Hudi Podcast Episode Upsolver Podcast Episode Spark PrestoDB Snowflake Podcast Episode Druid Rockset Podcast Episode dbt Podcast Episode Acryl Podcast Episode Atlan Podcast Episode OpenLineage Podcast Episode Okera Shopify Data Warehouse Episode Redshift Delta Lake Podcast Episode Iceberg Podcast Episode Outbox Pattern Backstage Roadie Nomad Kubernetes Deequ Great Expectations Podcast Episode LakeFS Podcast Episode 2021 Recap Episode Monte Carlo The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA a… |
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How Bandsintown Helps Artists Get Seen With Fabrice Sergent
2022-04-12 · 07:00
Fabrice Sergent
– Co-Founder and Managing Partner
@ Bandsintown
Our guest today is Fabrice Sergent, Co-Founder and Managing Partner at Bandsintown, where they “believe that live music is one of the last ‘tribal experiences’ which creates happiness and understanding in the world. Their mission is to help artists build a sustainable future through virtual live streams, music releases, merchandise and traditional live events promotion. With a reach of 250 million Monthly Active music fans globally, over 67m registered concert goers and 550k touring artists registered to the platform, Bandsintown offers powerful, scalable and targeted digital marketing solutions to engage with music enthusiasts.” According to his LinkedIn profile, Sergent is “an entrepreneur driven by passion, having led hyper growth digital media enterprises in the U.S. and in Europe with extensive experience in the music industry, digital consumer marketing, brand building, business development, and mergers & acquisitions. He co-founded and led two start-ups to $100m+ revenues, starting his journey by founding Club-Internet, with the support of Lagardere/Hachette in 1995. Club-Internet was one of the very first Internet Service Provider (similar to AOL in the US) launched in France and later became one of the largest [Internet Service Providers] before it was purchased by Deutsche Telecom’s T-Online in 2000 for 1.2 billion Euro.” Today, we’ll look back on his road to music, what Bandsintown is up to nowadays, and what the future holds in store for live music. If you want more free insights, follow our podcast, our blog, and our socials. If you're an artist with a free Chartmetric account, sign up for the artist plan, made exclusively for you, here. If you're new to Chartmetric, follow the URL above after creating a free account here. |
How Music Charts |
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Synthetic Data As A Service For Simplifying Privacy Engineering With Gretel
2022-04-10 · 19:00
John Myers
– CTO
@ Gretel
,
Tobias Macey
– host
Summary Any time that you are storing data about people there are a number of privacy and security considerations that come with it. Privacy engineering is a growing field in data management that focuses on how to protect attributes of personal data so that the containing datasets can be shared safely. In this episode Gretel co-founder and CTO John Myers explains how they are building tools for data engineers and analysts to incorporate privacy engineering techniques into their workflows and validate the safety of their data against re-identification attacks. 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 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 $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! This episode is brought to you by Acryl Data, the company behind DataHub, the leading developer-friendly data catalog for the modern data stack. Open Source DataHub is running in production at several companies like Peloton, Optum, Udemy, Zynga and others. Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product at dataengineeringpodcast.com/acryl Are you looking for a structured and battle-tested approach for learning data engineering? Would you like to know how you can build proper data infrastructures that are built to last? Would you like to have a seasoned industry expert guide you and answer all your questions? Join Pipeline Academy, the worlds first data engineering bootcamp. Learn in small groups with likeminded professionals for 9 weeks part-time to level up in your career. The course covers the most relevant and essential data and software engineering topics that enable you to start your journey as a professional data engineer or analytics engineer. Plus we have AMAs with world-class guest speakers every week! The next cohort starts in April 2022. Visit dataengineeringpodcast.com/academy and apply now! RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. Your host is Tobias Macey and today I’m interviewing John Myers about privacy engineering and use cases for synthetic data Interview Introduction How did you get involved in the area of data management? Can you describe what Gretel is and the story behind it? How do you define "privacy engineering"? In an organization or data team, who is typically responsible for privacy engineering? How would you characterize the current state of the art and adoption for privacy engineering? Who are the target users of Gretel and how does that inform the features and design of the product? What are the stages of the data lifecycle where Gretel is used? Can you describe a typical workflow for integrating Gretel into data pipelines for business analytics or ML model training? How is the Gretel platform implemented? How have the design and goals of the system changed or evolved since you started working on it? What are some of the nuances of synthetic data generation or masking that data engineers/data analysts need to be aware of as they start using Gretel? What are the most interesting, innovative, or unexpected ways that you have seen Gretel used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Gretel? When is Gretel the wrong choice? What do you have planned for the future of Gretel? Contact Info LinkedIn @jtm_tech on Twitter 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 Links Gretel Privacy Engineering Weights and Biases Red Team/Blue Team Generative Adversarial Network Capture The Flag in application security CVE == Common Vulnerabilities and Exposures Machine Learning Cold Start Problem Faker Mockaroo Kaggle Sentry The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast |
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Building A Data Governance Bridge Between Cloud And Datacenters For The Enterprise At Privacera
2022-03-27 · 17:00
Balaji Ganesan
– guest
@ Privacera
,
Tobias Macey
– host
Summary Data governance is a practice that requires a high degree of flexibility and collaboration at the organizational and technical levels. The growing prominence of cloud and hybrid environments in data management adds additional stress to an already complex endeavor. Privacera is an enterprise grade solution for cloud and hybrid data governance built on top of the robust and battle tested Apache Ranger project. In this episode Balaji Ganesan shares how his experiences building and maintaining Ranger in previous roles helped him understand the needs of organizations and engineers as they define and evolve their data governance policies and practices. 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 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 $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! This episode is brought to you by Acryl Data, the company behind DataHub, the leading developer-friendly data catalog for the modern data stack. Open Source DataHub is running in production at several companies like Peloton, Optum, Udemy, Zynga and others. Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product at dataengineeringpodcast.com/acryl RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. The most important piece of any data project is the data itself, which is why it is critical that your data source is high quality. PostHog is your all-in-one product analytics suite including product analysis, user funnels, feature flags, experimentation, and it’s open source so you can host it yourself or let them do it for you! You have full control over your data and their plugin system lets you integrate with all of your other data tools, including data warehouses and SaaS platforms. Give it a try today with their generous free tier at dataengineeringpodcast.com/posthog Your host is Tobias Macey and today I’m interviewing Balaji Ganesan about his work at Privacera and his view on the state of data governance, access control, and security in the cloud Interview Introduction How did you get involved in the area of data management? Can you describe what Privacera is and the story behind it? What is your working definition of "data governance" and how does that influence your product focus and priorities? What are some of the lessons that you learned from your work on Apache Ranger that helped with your efforts at Privacera? How would you characterize your position in the market for data governance/data security tools? What are the unique constraints and challenges that come into play when managing data in cloud platforms? Can you explain how the Privacera platform is architected? How have the design and goals of the system changed or evolved since you started working on it? What is the workflow for an operator integrating Privacera into a data platform? How do you provide feedback to users about the level of coverage for discovered data assets? How does Privacera fit into the workflow of the different personas working with data? What are some of the security and privacy controls that Privacera introduces? How do you mitigate the potential for anyone to bypass Privacera’s controls by interacting directly with the underlying systems? What are the most interesting, innovative, or unexpected ways that you have seen Privacera used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Privacera? When is Privacera the wrong choice? What do you have planned for the future of Privacera? Contact Info LinkedIn @Balaji_Blog on Twitter 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 Links Privacera Hadoop Hortonworks Apache Ranger Oracle Teradata Presto/Trino Starburst Podcast Episode Ahana Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By:
Acryl: The modern data stack needs a reimagined metadata management platform. Acryl Data’s vision is to bring clarity to your data through its next generation multi-cloud metadata management platform. Founded by the leaders that created projects like LinkedIn DataHub and Airbnb Dataportal, Acryl Data enables delightful search and discovery, data observability, and federated governance across data ecosystems. Signup for the SaaS product today at dataengineeringpodcast.com/acrylSupport Data Engineering Podcast |
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Navigating the Music Business With The Outlaw Ocean Project's Ian Urbina
2022-02-15 · 08:00
Ian Urbina
– Director
@ The Outlaw Ocean Project
Our guest today is Ian Urbina, the director of The Outlaw Ocean Project. The project is a non-profit journalism organization based in Washington, D.C., that produces investigative stories about human rights, environment, and labor concerns on the open seas. Urbina won a Pulitzer Prize for Breaking News and a George Polk Award for Foreign Reporting. Several of his stories have been adapted into major feature films, and his reporting for a New York Times Magazine article called The Secret Life Of Passwords was nominated for an Emmy Award. He has degrees in history and cultural anthropology from Georgetown University and the University of Chicago, respectively. Before joining The New York Times for roughly 17 years as a staff reporter, he was a Fulbright Fellow in Cuba, and he also wrote about the Middle East and Africa for various outlets including the Los Angeles Times, Harper’s, and Vanity Fair. On this episode, we talk to Urbina about the Outlaw Ocean Music Project, an offshoot of The Outlaw Ocean Project that's "[a]imed at people who might not otherwise have encountered this reporting." According to the project's website, "[T]he music renders stories more viscerally, and delivers them to the public through different channels. The music project’s goal is to raise awareness and stoke a sense of urgency about the human rights, labor, and environmental abuses that occur at sea.” If you want more free insights, follow our podcast, our blog, and our socials. If you're an artist with a free Chartmetric account, sign up for the artist plan, made exclusively for you, here. If you're new to Chartmetric, follow the URL above after creating a free account here. |
How Music Charts |
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Scalable Strategies For Protecting Data Privacy In Your Shared Data Sets
2022-02-06 · 22:00
Will Thompson
– guest
@ Privacy Dynamics
,
Tobias Macey
– host
Summary There are many dimensions to the work of protecting the privacy of users in our data. When you need to share a data set with other teams, departments, or businesses then it is of utmost importance that you eliminate or obfuscate personal information. In this episode Will Thompson explores the many ways that sensitive data can be leaked, re-identified, or otherwise be at risk, as well as the different strategies that can be employed to mitigate those attack vectors. He also explains how he and his team at Privacy Dynamics are working to make those strategies more accessible to organizations so that you can focus on all of the other tasks required of you. 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 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 $100 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 is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy. The only thing worse than having bad data is not knowing that you have it. With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders. With complete API access, a user-friendly interface, and automated yet flexible alerting, you’ve got everything you need to establish and maintain trust in your data. Go to dataengineeringpodcast.com/bigeye today to sign up and start trusting your analyses. Your host is Tobias Macey and today I’m interviewing Will Thompson about managing data privacy concerns for data sets used in analytics and machine learning Interview Introduction How did you get involved in the area of data management? Data privacy is a multi-faceted problem domain. Can you start by enumerating the different categories of privacy concern that are involved in analytical use cases? Can you describe what Privacy Dynamics is and the story behind it? Which categor(y|ies) are you focused on addressing? What are some of the best practices in the definition, protection, and enforcement of data privacy policies? Is there a data security/privacy equivalent to the OWASP top 10? What are some of the techniques that are available for anonymizing data while maintaining statistical utility/significance? What are some of the engineering/systems capabilities that are required for data (platform) engineers to incorporate these practices in their platforms? What are the tradeoffs of encryption vs. obfuscation when anonymizing data? What are some of the types of PII that are non-obvious? What are the risks associated with data re-identification, and what are some of the vectors that might be exploited to achieve that? How can privacy risks mitigation be maintained as new data sources are introduced that might contribute to these re-identification vectors? Can you describe how Privacy Dynamics is implemented? What are the most challenging engineering problems that you are dealing with? How do you approach validation of a data set’s privacy? What have you found to be useful heuristics for identifying private data? What are the risks of false positives vs. false negatives? Can you describe what is involved in integrating the Privacy Dynamics system into an existing data platform/warehouse? What would be required to integrate with systems such as Presto, Clickhouse, Druid, etc.? What are the most interesting, innovative, or unexpected ways that you have seen Privacy Dynamics used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Privacy Dynamics? When is Privacy Dynamics the wrong choice? What do you have planned for the future of Privacy Dynamics? Contact Info LinkedIn @willseth on Twitter 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 Links Privacy Dynamics Pandas Podcast Episode – Pandas For Data Engineering Homomorphic Encryption Differential Privacy Immuta Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast |
Data Engineering Podcast |
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The Power of Music Metadata With Byta Founder Marc Brown
2022-01-28 · 08:00
Marc Brown
– Founder
@ Byta
Our guest today is Marc Brown, Founder of Byta, a music sharing app that lets artists, studios, and managers share, collaborate on, and promote secure music files before uploading them to streaming services. Marc is currently based in Stockholm, Sweden, though he is originally from Canada and spent many years in London working in A&R and artist promotion. On this episode, we discuss how he came to found Byta, what sets the app apart from DropBox and Soundcloud, and what this might mean for the future of Hi-Fi audio and NFTs. Check out Byta here. To attend a free Byta symposium that explores "how the music ecosystem will evolve and adapt to the developing digital landscape," register here. Byta recently published a white paper on the state of music sharing, which you can check out here. If you want more free insights, follow our podcast, our blog, and our socials. If you're an artist with a free Chartmetric account, sign up for the artist plan, made exclusively for you, here. If you're new to Chartmetric, follow the URL above after creating a free account here. |
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A Fireside Chat With Chartmetric Founder Sung Cho
2022-01-04 · 08:00
Sung Cho
– Founder
@ Chartmetric
Before starting Chartmetric, Sung worked on Sales Cloud as the Principal Product Manager at Oracle Corporation. Prior to that, he was the first employee/engineer at the publicly-traded gaming company Gamevil (which has a Market Cap of $400MM), where he initiated mobile game development and eventually positioned the company as a leading mobile game developer. Sung graduated with a bachelor’s degree of electrical engineering and computer science from Seoul National University, and an MBA from UCLA Anderson School of Management. He enjoys posting ideas on his blog (http://sungmooncho.com), which has attracted more than 5 million views and is considered as one of the leading tech blogs in Korea. Sung has invested in 10 startups in New York, Silicon Valley, and Seoul (http://angel.co/sung-cho). If you want more free insights, follow our podcast, our blog, and our socials. If you're an artist with a free Chartmetric account, sign up for the artist plan, made exclusively for you, here. If you're new to Chartmetric, follow the URL above after creating a free account here. |
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Zhamak Dehghani
– guest
,
Tobias Macey
– host
Summary The data mesh is a thesis that was presented to address the technical and organizational challenges that businesses face in managing their analytical workflows at scale. Zhamak Dehghani introduced the concepts behind this architectural patterns in 2019, and since then it has been gaining popularity with many companies adopting some version of it in their systems. In this episode Zhamak re-joins the show to discuss the real world benefits that have been seen, the lessons that she has learned while working with her clients and the community, and her vision for the future of the data mesh. 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 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 $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Your host is Tobias Macey and today I’m welcoming back Zhamak Dehghani to talk about her work on the data mesh book and the lessons learned over the past 2 years Interview Introduction How did you get involved in the area of data management? Can you start by giving a brief recap of the principles of the data mesh and the story behind it? How has your view of the principles of the data mesh changed since our conversation in July of 2019? What are some of the ways that your work on the data mesh book influenced your thinking on the practical elements of implementing a data mesh? What do you view as the as-yet-unknown elements of the technical and social design constructs that are needed for a sustainable data mesh implementation? In the opening of your book you state that "Data Mesh is a new approach in sourcing, managing, and accessing data for analytical use cases at scale". As with everything, scale is subjective, but what are some of the heuristics that you rely on for determining when a data mesh is an appropriate solution? What are some of the ways that data mesh concepts manifest at the boundaries of organizations? While the idea of federated access to data product quanta reduces the amount of coordination necessary at the organizational level, it raises the spectre of more complex logic required for consumers of multiple quanta. How can data mesh implementations mitigate the impact of this problem? What are some of the technical components that you have found to be best suited to the implementation of data elements within a mesh? What are the technological components that are still missing for a mesh-native data platform? How should an organization that wishes to implement a mesh style architecture think about the roles and skills that they will need on staff? How can vendors factor into the solution? What is the role of application developers in a data mesh ecosystem and how do they need to change their thinking around the interfaces that they provide in their products? What are the most interesting, innovative, or unexpected ways that you have seen data mesh principles used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data mesh implementations? When is a data mesh the wrong approach? What do you think the future of the data mesh will look like? Contact Info LinkedIn @zhamakd on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Data Engineering Podcast Data Mesh Interview Data Mesh Book Thoughtworks Expert Systems OpenLineage Podcast Episode Data Mesh Learning The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast |
Data Engineering Podcast |
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Disrupting Live Music With DICE President Russ Tannen
2021-12-14 · 08:00
Russ Tannen
– President
@ DICE
Russ Tannen is President of DICE, a UK- and now NYC-based mobile ticketing, live streaming, and live music recommendation platform that partners directly with venues, labels, and promoters to bring upfront pricing to live music fans (no fees are added at checkout, so the price you see at first is the price you get). Before rising through the ranks at DICE, Russ spent many years as an artist manager at Deadly Management during — and after — his time as an Events Manager at Vice Media. Before that, Russ studied Photography at the University for the Creative Arts in the UK. Read 20 things we've learned about Music Live Streaming by DICE Founder & CEO Phil Hutcheon. Check out DICE here, and download the app on your phone. If you want more free insights, follow our podcast, our blog, and our socials. If you're an artist with a free Chartmetric account, sign up for the artist plan, made exclusively for you, here. If you're new to Chartmetric, follow the URL above after creating a free account here. |
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Music Trends in Arab Countries With Firas Abou Fakher
2021-12-13 · 22:00
Firas Abou Fakher
– guest
The Arab nations have rarely been viewed as important markets by the global music industry. And artists from the region have only ever achieved modest international success. Until 20 years ago, exactly the same could be said of countless other countries. Today, markets like Latin America and South Korea produce global superstars with astonishing regularity. If you want more free insights, follow our podcast, our blog, and our socials. If you're an artist with a free Chartmetric account, sign up for the artist plan, made exclusively for you, here. If you're new to Chartmetric, follow the URL above after creating a free account here. |
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Lessons for Artists and Leaders With Stem CEO Milana Lewis
2021-12-07 · 08:00
Milana Lewis
– Co-Founder and CEO
@ Stem
Our guest today is Milana Lewis, Co-Founder and CEO of Stem, a platform making it easier for artists, managers, labels and brands to distribute music, manage contracts, share data, split royalties, and stay independent. After six years working in talent agencies, spending the majority of that time as a Digital Media Agent with United Talent Agency, Milana started Stem in 2015 with the goal of simplifying how musicians and their teams pay collaborators. At the start of 2020, Stem announced Scale, a $100 million cash advance program aimed at giving artists a way to access alternative funding with fewer restrictions than a label typically imposes. This year, Inc. featured Milana on their Female Founders 100 list. Check out Stem here. If you want more free insights, follow our podcast, our blog, and our socials. If you're an artist with a free Chartmetric account, sign up for the artist plan, made exclusively for you, here. If you're new to Chartmetric, follow the URL above after creating a free account here. |
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How to Distribute and Monetize Your Music With UnitedMasters' Dave Melhado
2021-11-30 · 08:00
David Melhado
– Head of Marketing
@ UnitedMasters
UnitedMasters’ Head of Marketing David Melhado (mel-HEY-doh) is a New York City-based industry veteran who first cut his teeth in music marketing and management in the South, holding roles at Atlantic Records, iHeartRadio, StreamCut Media & RocNation. Connect With Dave on LinkedIn and check out UnitedMasters here. If you want more free insights, follow our podcast, our blog, and our socials. If you're an artist with a free Chartmetric account, sign up for the artist plan, made exclusively for you, here. If you're new to Chartmetric, follow the URL above after creating a free account here. |
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From Playlists to Mailing Lists With Aileen Crowley
2021-11-23 · 08:00
Aileen Crowley
– Former Vice President of Global Streaming Marketing
@ Universal Music Group
On this episode, we chat with Aileen Crowley, former Vice President of Global Streaming Marketing at Universal Music Group. Before leaving the major label world in November 2020, Aileen devised data-driven streaming strategy for developing artists, working directly with artist management to translate streaming analytics, develop artist release strategies, and implement plans for audience growth. Prior to that, Aileen was the General Manager of DigSin, a subscription-based independent music label focused on singles, playlisting, and data, as well as being an artist manager—and that was after spending almost seven years at world-renowned consulting firm McKinsey & Co. Today, Aileen runs The Streaming Story, a website dedicated to contextualizing streaming success with the narrative surrounding that success. Since recording this interview, Aileen has teamed up with Lark42, a digital consultancy that solves hard problems in the music, data, blockchain, streaming and startup space. You can connect with Aileen on LinkedIn here. If you want more free insights, follow our podcast, our blog, and our socials. If you're an artist with a free Chartmetric account, sign up for the artist plan, made exclusively for you, here. If you're new to Chartmetric, follow the URL above after creating a free account here. |
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How the TikTok Charts Influence the German Charts With Popakademie
2021-11-16 · 08:00
Aqui Kumar
– music business student
@ Popakademie
,
Valerian Dilger
– music business student
@ Popakademie
,
Jannick Steinke
– music business student
@ Popakademie
,
Sebastian Gorki
– music business student
@ Popakademie
Aqui Kumar, Jannick Steinke, Sebastian Gorki, and Valerian Dilger are music business students at the Popakademie in Germany. “Established in 2003, the Popakademie is a higher education institution for the music and creative industries and their pop cultural scenes. By focusing its study programmes on popular music, it offers an academic education that is unique in Germany's public university landscape.” In this context, the students are part of Popakademie’s SMIX.LAB. Founded in 2008 as an interface between the online world and the traditional music industry, SMIX.LAB sees itself as a center for the digital music business. It integrates digital knowledge and future-oriented research, investigating the modern possibilities of music marketing and other forward-thinking initiatives. The students themselves have industry experience at companies such as Live From Earth, Electric Feel, Amazon Music, and Ease Agency. Over the past few months, the Chartmetric team supported the students in their project to study how TikTok is influencing the German charts. Check out their exciting research on our blog: https://blog.chartmetric.com If you want more free insights, follow our podcast, our blog, and our socials. If you're an artist with a free Chartmetric account, sign up for the artist plan, made exclusively for you, here. If you're new to Chartmetric, follow the URL above after creating a free account here. |
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How to Think Like an Artist Manager With RAC Manager Zael Ellenhorn
2021-11-09 · 08:00
Zael Ellenhorn
– Manager
@ YMU Group
Zael is a manager at YMU Group, a global talent management company with a roster that includes RAC, Kina, Ryan Caraveo, Ben Zaidi, and Oshi. Originally from Amherst, Massachusetts, Zael grew up around a lot of jazz, cows, and psychologists. After moving out to LA and building his own management company, Zael joined forces with YMU in late 2019 where he kicked things off by developing the marketing plan for RAC’s third studio album “BOY” before signing Kina and Ryan Caraveo in early 2020. Connect With Zael on LinkedIn and check out YMU Group here. If you want more free insights, follow our podcast, our blog, and our socials. If you're an artist with a free Chartmetric account, sign up for the artist plan, made exclusively for you, here. If you're new to Chartmetric, follow the URL above after creating a free account here. |
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