Effective Data Pipelines: Data Mngmt from Chaos

Creating automated, efficient and accurate data pipelines out of the (often) noisy, disparate and busy data flows used by today's enterprises is a difficult task. Data science teams and engineering teams may be asked to work together to create a management platform (or install one) that helps...

Data Cleansing and Understanding Best Practices

Any data scientist who works with real data will tell you that the hardest part of any data science task is the data preparation. Everything from cleaning dirty data to understanding where your data is missing and how your data is shaped, the care and feeding of your data is a prime task for the...

Reliable & Scalable Data Infra Eco-System At Uber

Uber's vision is to make transportation as reliable as running water everywhere, for everyone. Data is key for Uber's 24x7 global business operations and making data available for different use cases across the company in a reliable, scalable and performant way is often challenging. In this...

Building a Data Science Capability From Scratch

This talk will cover the challenges, both technical and cultural, of building a data science team and capability in a large, global company. It will discuss best practices, lessons learned, and rewards of leveraging data effectively in the next frontier of data science: commercial insurance.

Data Engineering Open Space

Building Data Pipelines in Python

This talk discusses the process of building data pipelines, e.g. extraction, cleaning, integration, pre-processing of data, in general all the steps that are necessary to prepare your data for your data-driven product. In particular, the focus is on data plumbing and on the practice of going from...