Reduce Friction In Your Business Analytics Through Entity Centric Data Modeling
This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the. . .
This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here. Support the show!09 July 2023Reduce Friction In Your Business Analytics Through Entity Centric Data Modeling - E382Rewind 10 seconds1XSkip 30 seconds ahead0:00/0:00Share on social media:DescriptionTranscriptChaptersSummaryFor business analytics the way that you model the data in your warehouse has a lasting impact on what types of questions can be answered quickly and easily. The major strategies in use today were created decades ago when the software and hardware for warehouse databases were far more constrained. In this episode Maxime Beauchemin of Airflow and Superset fame shares his vision for the entity-centric data model and how you can incorporate it into your own warehouse design. AnnouncementsHello and welcome to the Data Engineering Podcast, the show about modern data managementIntroducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today atdataengineeringpodcast. com/rudderstackYour host is Tobias Macey and today I'm interviewing Max Beauchemin about the concept of entity-centric data modeling for analytical use casesInterviewIntroductionHow did you get involved in the area of data management?Can you describe what entity-centric modeling (ECM) is and the story behind it?How does it compare to dimensional modeling strategies?What are some of the other competing methodsComparison to activity schemaWhat impact does this have on ML teams? (e. g. feature engineering)What role does the tooling of a team have in the ways that they end up thinking about modeling? (e. g. dbt vs. informatica vs. ETL scripts, etc. )What is the impact on the underlying compute engine on the modeling strategies used?What are some examples of data sources or problem domains for which this approach is well suited?What are some cases where entity centric modeling techniques might be counterproductive?What are the ways that the benefits of ECM manifest in use cases that are down-stream from the warehouse?What are some concrete tactical steps that teams should be thinking about to implement a workable domain model using entity-centric principles?How does this work across business domains within a given organization (especially at "enterprise" scale)?What are the most interesting, innovative, or unexpected ways that you have seen ECM used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on ECM?When is ECM the wrong choice?What are your predictions for the future direction/adoption of ECM or other modeling techniques?Contact Infomistercrunchon GitHubLinkedInParting QuestionFrom your perspective, what is the biggest gap in the tooling or technology for data management today?Closing AnnouncementsThank 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. TheMachine Learning Podcasthelps you go from idea to production with machine learning. Visit thesiteto 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! Emailhosts@dataengineeringpodcast. com) with your story. To help other people find the show please leave a review onApple Podcastsand tell your friends and co-workersLinksEntity Centric Modeling Blog PostMax's Previous ApperancesDefining Data Engineering with Maxime BeaucheminSelf Service Data Exploration And Dashboarding With SupersetExploring The Evolving Role Of Data EngineersAlumni Of AirBnB's Early Years Reflect On What They Learned About Building Data Driven OrganizationsApache AirflowApache SupersetPresetUbisoftRalph KimballThe Rise Of The Data EngineerThe Downfall Of The Data EngineerThe Rise Of The Data ScientistDimensional Data ModelingStar SchemaDatabase NormalizationFeature EngineeringDRY == Don't Repeat YourselfActivity SchemaPodcast EpisodeCorporate Information Factory(affiliate link)The intro and outro music is fromThe HugbyThe Freak Fandango Orchestra/CC BY-SASponsored By:Rudderstack: Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast. com/rudderstack](https://www. dataengineeringpodcast. com/rudderstack)Support Data Engineering PodcastShare on social media:Listen in your favorite app:PodversePodcast GuruApple PodcastsPocket CastsOvercastSpotifyPodlinkMore optionsHere are shows you might likeSee show recommendationsAI Engineering PodcastTobias MaceyThe Python Podcast. __init__Tobias Macey