How HyreCar Maximizes the Power of Self-Serve Analytics

In the era of cloud computing, it can be a challenge to consolidate data to successfully serve self-service analytics. It is impractical for organizations to rely on the traditional ETL approach.

At HyreCar, we successfully deployed self-service analytics by leveraging open data architecture. The implementation not only streamlined the self-serve BI experience, kept business stakeholders engaged, and powered timely decision making, but also played an important role in building a data culture. In this talk, I want to share our experience building self-serve analytics with tools such as Dremio, Databricks, and Tableau.

Speakers

Linda Liu

Linda Liu received her master’s degrees in statistics and economics from the University of California, Santa Barbara. She has worked extensively in data analytics and data science for a number of businesses over the past ten years. In her current position at HyreCar, Inc. leading the data analytics team, she works closely with data engineering and development teams to continuously drive insights by leveraging cloud data lake infrastructure to guide business decisions and improve user experience.

Ready to Get Started? Here Are Some Resources to Help

Webinars

Smart Data – Smart Factory with Octotronic and Dremio

Guides

What Is a Data Lakehouse?

The data lakehouse is a new architecture that combines the best parts of data lakes and data warehouses. Learn more about the data lakehouse and its key advantages.

Whitepaper

Simplifying Data Mesh for Self-Service Analytics on an Open Data Lakehouse

The adoption of data mesh as a decentralized data management approach has become popular in recent years, helping teams overcome challenges associated with centralized data architecture.