Data Modeling Meetup Munich (DM3)

 
 
 

We’re happy to welcome cognitive scientist and staff data engineer Dr. Marielle Dado who will tell us about data modeling as knowledge construction.

Abstract:

In this talk, Marielle will explain how principles of knowledge construction (KC) can be applied to data modelling. According to this theory, people (or groups of people) learn best by connecting new information with information that they already know, thereby "constructing" new knowledge or understanding. Similarly, data modellers build sets of foundational data models that can be shared and referenced by new models, thereby generating new business insights on top of existing data. When applied properly, a KC-based approach to data modeling could potentially foster collaboration and data literacy between cross-functional teams.

This talk is an extended version of Marielle’s Coalesce talk entitled "I don't build models, I construct knowledge" and is mainly based on her own work experiences navigating data modeling with the modern data stack (cloud data warehouses and dbt). Therefore, this session will have an interactive element where I open the floor up to feedback from the audience about the pros and cons of a KC-based approach.

Speaker:

Marielle Dado is a Staff Data Engineer at Paddle, a UK-based unicorn that offers payments infrastructure for SaaS companies. She has a PhD in Applied Cognitive Sciences from the University of Duisbuirg-Essen with a research focus on collaborative knowledge construction in educational settings.

Agenda:

19:00 Welcome & Introduction
19:10 Data Modeling as Knowledge Construction (Marielle Dado)
20:00 Discussion & Conclusion

 
 
 

We’re happy to welcome Nikolai Golov, Anton Polyakov and Yury Gavrilov from ManyChat who will share their latest experiences with automating anchor modeling on analytical cloud databases.

Abstract:

Applying data modelling to real world problems requires solving many problems: delivering raw data, creating a data model, describing mappings, automating ETL.

With a proper modeling technique and proper automation, all these steps can be automatically derived from just a semantic description of incoming data. This approach was implemented in Manychat, in a semi-automatic fashion.

During the meetup, we plan to make a live demonstration of a fully automated tool, called Youta, with live data from Stripe.

Speakers:

Nikolai Golov, Anton Polyakov and Yury Gavrilov are data engineers at ManyChat and researchers of automation of data modeling for modern cloud environments like Snowflake.

Agenda:

19:00 Welcome & Introduction
19:10 An Approach to Automating Your (Anchor) Modeling (Nikolai Golov et al.)
20:00 Discussion & Conclusion

 
 
 

We’re happy to welcome German data modeling and data warehousing expert Dirk Lerner who will discuss expectations and reality when starting with data warehouse automation.

Abstract:

Today, quite a few data warehouse (data solution) project teams are thinking about automating their data logistics processes. How can you simplify the same tasks, leave these processes to an automatism and at the same time put as little repetitive effort as possible into the development? The demand for an end-to-end automation product for the data solution is then usually very quick to arise.

In the meantime, there are many such products on the market: long-established, newcomers and proprietary developments. Project teams are spoiled for choice. And quite often (after the product selection) experience a disappointment.

In this session, Dirk Lerner will give an insight into the expectations of project teams, the processes of product selection and the reality.

This presentation does not contain a recommendation for a specific product, nor is it mentioned or evaluated by the speaker!

Speaker:

Dirk Lerner is the founder and CEO of TEDAMOH GmbH. He has led BI projects for two decades and is recognized as an expert for BI architecture and data modeling. Dirk advocates for flexible, lean and easily extensible data warehouse architectures.

Agenda:

19:00 Welcome & Introduction
19:10 Data Warehouse Automation – Expectations and Reality (Dirk Lerner)
20:00 Discussion & Conclusion

 
 
 

Nachdem wir 2018, 2019 und 2022 so eine nette Runde zusammengebracht haben (2020 und 2021 war irgendwas ...), werden wir uns auch zur TDWI 2023 im Biergarten (bzw. bei schlechtem Wetter drin) zusammensetzen und über Datenmodellierungsthemen unterhalten.

Agenda:
Was immer euch interessiert. :-) Die Vorträge auf der Konferenz sollten genug Anregung bieten ...

Anfahrt mit öffentlichen Verkehrsmitteln vom MOC aus:
Mit der U6 Richtung Innenstadt zum Nordfriedhof, von dort noch ein kurzer Fußweg.

Bildquelle:
User: Bbb at wikivoyage shared
https://creativecommons.org/licenses/by-sa/3.0/deed.en