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| Title & Speakers | Event |
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R-Ladies Baltimore -- Python Rgonomics (Virtual)
2024-02-20 · 20:00
R-Ladies Baltimore (Bmore) is excited to host a meetup on Tuesday February 20, 2024 from 3-4:30pm (entirely virtual meetup on Zoom). We will have a wonderful presentation from Emily Riederer, a Senior Analytics Manager at Capital One (https://emilyriederer.netlify.app/about). She will be giving a talk on "Python Rgonomics: Modern python tools that feel like R"! For folks that cross languages from R to Python, come learn about newer tools in the Python space that mirror some of R's semantics and make users feel more at home, including:
Think about it as opinionated data science stack that will feel more familiar to R users! WHO is this for?
If any/all of this is true, this event might be for you! AGENDA
Details on the location:
FAQ: Q: Are men welcome to attend R-Ladies events? A: Yes, men are welcome at this event. Our mission is to encourage more women and non-binary individuals to get involved in the R community. However, we ask that cis men come as guests of women and gender minorities to our events to promote a sense support and encouragement for women and gender minorities in the R community. Please help us by spreading the word about R-Ladies. Please also take the time to review our code of conduct before you attend: https://github.com/rladies/starter-kit/wiki/Code-of-Conduct The term "women and gender minorities" is inclusive of individuals who identify as female (trans and cis), trans-men, non-binary, genderqueer, genderfluid, agender, intersex, and all minority genders). Important to know:
Finally, for more information: email: [email protected] twitter: @RLadiesBmore facebook: https://www.facebook.com/RLadiesBmore/ meetup: https://www.meetup.com/rladies-baltimore/ |
R-Ladies Baltimore -- Python Rgonomics (Virtual)
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Creating Shared Context For Your Data Warehouse With A Controlled Vocabulary
2022-01-02 · 04:00
Emily Riederer
– guest
,
Tobias Macey
– host
Summary Communication and shared context are the hardest part of any data system. In recent years the focus has been on data catalogs as the means for documenting data assets, but those introduce a secondary system of record in order to find the necessary information. In this episode Emily Riederer shares her work to create a controlled vocabulary for managing the semantic elements of the data managed by her team and encoding it in the schema definitions in her data warehouse. She also explains how she created the dbtplyr package to simplify the work of creating and enforcing your own controlled vocabularies. 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! 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. 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 Your host is Tobias Macey and today I’m interviewing Emily Riederer about defining and enforcing column contracts and controlled vocabularies for your data warehouse Interview Introduction How did you get involved in the area of data management? Can you start by discussing some of the anti-patterns that you have encountered in data warehouse naming conventions and how it relates to the modeling approach? (e.g. star/snowflake schema, data vault, etc.) What are some of the types of contracts that can, and should, be defined and enforced in data workflows? What are the boundaries where we should think about establishing those contracts? What is the utility of column and table names for defining and enforcing contracts in analytical work? What is the process for establishing contractual elements in a naming schema? Who should be involved in that design process? Who are the participants in the communication paths for column naming contracts? What are some examples of context and details that can’t be captured in column names? What are some options for managing that additional information and linking it to the naming cont |
Data Engineering Podcast |