December 15, 2020

Building A Self Service Data Platform For Alternative Data Analytics At YipitData

1 hour 4 minutes

Summary

As a data engineer you’re familiar with the process of collecting data from databases, customer data platforms, APIs, etc. At YipitData they rely on a variety of alternative data sources to inform investment decisions by hedge funds and businesses. In this episode Andrew Gross, Bobby Muldoon, and Anup Segu describe the self service data platform that they have built to allow data analysts to own the end-to-end delivery of data projects and how that has allowed them to scale their output. They share the journey that they went through to build a scalable and maintainable system for web scraping, how to make it reliable and resilient to errors, and the lessons that they learned in the process. This was a great conversation about real world experiences in building a successful data-oriented business.

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Your host is Tobias Macey and today I’m interviewing Andrew Gross, Bobby Muldoon, and Anup Segu about they are building pipelines at Yipit Data

Interview

Introduction

How did you get involved in the area of data management?

Can you start by giving an overview of what YipitData does?

What kinds of data sources and data assets are you working with?

What is the composition of your data teams and how are they structured?

Given the use of your data products in the financial sector how do you handle monitoring and alerting around data quality?

For web scraping in particular, given how fragile it can be, what have you done to make it a reliable and repeatable part of the data pipeline?

Can you describe how your data platform is implemented?

How has the design of your platform and its goals evolved or changed?

What is your guiding principle for providing an approachable interface to analysts?

How much knowledge do your analysts require about the guarantees offered, and edge cases to be aware of in the underlying data and its processing?

What are some examples of specific tools that you have built to empower your analysts to own the full lifecycle of the data that they are working with?

Can you characterize or quantify the benefits that you have seen from training the analysts to work with the engineering tool chain?

What have been some of the most interesting, unexpected, or surprising outcomes of how you are approaching the different responsibilities and levels of ownership in your data organization?

What are some of the most interesting, unexpected, or challenging lessons that you have learned from building out the platform, tooling, and organizational structure for creating data products at Yipit?

What advice or recommendations do you have for other leaders of data teams about how to think about the organizational and technical aspects of managing the lifecycle of data projects?

Contact Info

Andrew

@awgross on Twitter

Bobby

@TheDooner64

Anup

anup-segu on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Yipit Data

Redshift

MySQL

Airflow

Databricks

Groupon

Living Social

Web Scraping

Podcast.__init__ Episode

Readypipe

Graphite

Podcast.init Episode

AWS Kinesis Firehose

Parquet

Papermill

Podcast Episode About Notebooks At Netflix

Fivetran

Podcast Episode

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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By Tobias Macey

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