Women in Open Source & Presto – Getting started in the Presto open source ecosystem

Women in Open Source & Presto – Getting started in the Presto open source ecosystem

Women in Open Source & Presto – Getting Started in the Presto Open Source Ecosystem – Neha Pawar, Startree; Rebecca Schlussel, Meta; RongRong Zhong, Celonis & Moderated By Dipti Borkar, Microsoft Among GitHub users with at least ten contributions, a mere 6% were women. This is way less than the ratio of women in tech that various research shows at 26%. Given the amount of investment going into and the growth / success of companies based on open source as well as the enormous demand for developers in open source, it is a ratio we need to strive to improve for women. In this panel, we will discuss a few areas: – The journey of each panelist into open source projects – The benefits they have seen by participating in open source projects particularly Presto – The challenges women face in male-dominated open source communities – Ideas, suggestions and guidance to budding engineers on getting started with open source including Presto.

Headless BI Architecture and Trade-offs – Pavel Tiunov, Cube Dev

Headless BI Architecture and Trade-offs – Pavel Tiunov, Cube Dev

There has been a proliferation of tools in different categories of the modern data stack. This talk will focus on the Headless BI category and Cube’s implementation of Headless BI. Headless BI injects a component between data warehouses and other data sources and tools on the other side of the stack (e.g. CDP, data exploration tools, custom data apps, etc.). This new component encapsulates several critical functions like data modeling, access control, and aggregate awareness while deliberately omitting others, like data visualization and presentation. We’ll explore: – Keeping data models separate from data sources and not substituting data modeling with mere data transformation. – Managing access control centrally, aggregate awareness, and caching in a separate layer upstack from data consumers. – Removing data presentation features and embracing data accessibility via a set of APIs.