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Welcome to the new edition of Data Engineering London on Data Storage!

Join us for the fourth edition of the Data Engineering meetup with a range of talks looking at data storage. You'll have the chance to network and meet fellow data engineers (and other data enthusiasts)!

👉 The venue requires us to collect names/emails, if you have RSVP'ed yes, please make sure to fill out this google form: https://forms.gle/ZR3prm5HgtXQTv7X8 👈

When? 18:00 - 18:30 Networking with food and drinks from Dremio 18:30 - 19:45 Talks 19:45 - 20:30 More networking

Where? Dremio offices (see address)

Speakers and Talks:

1.Building an Open Data Lakehouse using Apache Spark, Apache Iceberg and Dremio - by Mike Flower (Solution Architect @ Dremio)

2. Micro-Partitions\, Clustering and Pruning - Improving Query Performance with Storage Optimization - by Niall Woodward (Co-founder & CTO @ SELECT)

3. Geospatial Analysis in Snowflake: How native Snowflake capabilities make light work of Lidar data - by Mike Taylor (Principal Architect @ Snowflake)

If you have a topic you're passionate about and wish to see discussed, let us know! We're always looking for more talks for our future events.

Places are limited, make sure you register!

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your program's AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture—and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and code

The UVA School of Data Science was formed in September 2019 and has since grown in its collaborations, partnerships, program offerings, and teaching and research personnel. We are now constructing a new facility that will house the School of Data Science at the University of Virginia.

The new building is in the first phase of development and, once complete, will link the University's Central Grounds with the athletic fields and North Grounds. The 60,000-square-foot building is the future home of the UVA School of Data Science and will serve as the gateway to the new Emmet-Ivy Corridor and the Discovery Nexus.

This bonus episode is a conversation between UVA architect Alice Raucher and Mike Taylor, a principal with Hopkins Architects. Both Alice and Mike have been instrumental in the building’s design. Alice has also played a key role in the development of the Ivy Corridor. Mike and Alice take a deep dive into the thought process behind the building’s design, its relationship to the University and its history, the land's unique topography, and its significance to future projects along the Ivy Corridor. 

Links:

Hopkins Architects

School of Data Science New Building Website

This week Peter will present ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation by Sungduk Yu · Walter Hannah · Liran Peng · Jerry Lin · Mohamed Aziz Bhouri · Ritwik Gupta · Björn Lütjens · Justus C. Will · Gunnar Behrens · Julius Busecke · Nora Loose · Charles Stern · Tom Beucler · Bryce Harrop · Benjamin Hillman · Andrea Jenney · Savannah L. Ferretti · Nana Liu · Animashree Anandkumar · Noah Brenowitz · Veronika Eyring · Nicholas Geneva · Pierre Gentine · Stephan Mandt · Jaideep Pathak · Akshay Subramaniam · Carl Vondrick · Rose Yu · Laure Zanna · Tian Zheng · Ryan Abernathey · Fiaz Ahmed · David Bader · Pierre Baldi · Elizabeth Barnes · Christopher Bretherton · Peter Caldwell · Wayne Chuang · Yilun Han · YU HUANG · Fernando Iglesias-Suarez · Sanket Jantre · Karthik Kashinath · Marat Khairoutdinov · Thorsten Kurth · Nicholas Lutsko · Po-Lun Ma · Griffin Mooers · J. David Neelin · David Randall · Sara Shamekh · Mark Taylor · Nathan Urban · Janni Yuval · Guang Zhang · Mike Pritchard.

We look forward to seeing you there!

Want to know more Paper Club?

• We discuss a different research paper every week. We post each week's paper in our GitHub repo - please read it before the meetup.
• All events will be hosted on a Google Meets video call. Once a month we also host an in-person event in our London office - watch this space for updates.
• All recorded presentations can be found in our YouTube channel (don't forget to subscribe!).