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Building a lakehouse is straightforward today thanks to many open source technologies and Databricks. However, it can be taxing to extract value from lakehouses as they grow without robust data operations. Join us to learn how YipitData uses the Unity Catalog to streamline data operations and discover best practices to scale your own Lakehouse. At YipitData, our 15+ petabyte Lakehouse is a self-service data platform built with Databricks and AWS, supporting analytics for a data team of over 250. We will share how leveraging Unity Catalog accelerates our mission to help financial institutions and corporations leverage alternative data by:

• Enabling clients to universally access our data through a spectrum of channels, including Sigma, Delta Sharing, and multiple clouds
• Fostering collaboration across internal teams using a data mesh paradigm that yields rich insights
• Strengthening the integrity and security of data assets through ACLs, data lineage, audit logs, and further isolation of AWS resources
• Reducing the cost of large tables without downtime through automated data expiration and ETL optimizations on managed delta tables

Through our migration to Unity Catalog, we have gained tactics and philosophies to seamlessly flow our data assets internally and externally. Data platforms need to be value-generating, secure, and cost-effective in today's world. We are excited to share how Unity Catalog delivers on this and helps you get the most out of your lakehouse.

Talk by: Anup Segu

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

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.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Are you bogged down by having to manually manage data access controls, repeatedly move and copy data, and create audit reports to prove compliance? How much time could you save if those tasks were automated across your cloud platforms? Immuta is an automated data governance solution that enables safe and easy data analytics in the cloud. Our comprehensive data-level security, auditing and de-identification features eliminate the need for time-consuming manual processes and our focus on data and compliance team collaboration empowers you to deliver quick and valuable data analytics on the most sensitive data to unlock the full potential of your cloud data platforms. Learn how we streamline and accelerate manual processes to help you derive real results from your data at dataengineeringpodcast.com/immuta. Today’s episode of the Data Engineering Podcast is sponsored by Datadog, a SaaS-based monitoring and analytics platform for cloud-scale infrastructure, applications, logs, and more. Datadog uses machine-learning based algorithms to detect errors and anomalies across your entire stack—which reduces the time it takes to detect and address outages and helps promote collaboration between Data Engineering, Operations, and the rest of the company. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial. If you start a trial and install Datadog’s agent, Datadog will send you a free T-shirt. 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 qualit