Speaking and
Workshops🎤

Starburst's Senior Developer Monica Miller champions female representation in the data science world while gearing up for Datanova, Starburst's premiere data conference on October 23-24!

A talk about the things I wish I knew when I started my career in tech.

As organizations grow, Data Producers and Data Consumers lose touch and critical disconnections within the organization start to arise. Data Producers should not be held responsible for support they never agreed to, yet Data Consumers cannot be expected to own data from source systems they didn’t build. Consumers should have the power to define the schema they need instead of being forced to adapt to low-quality data. The answer? Data Contracts. Learn from Chad Sanderson, Chief Operator of Data Quality Camp, how data contracts can drive a cultural change toward data-centric collaboration resulting in well-modeled, high-quality, and trusted data.

Running and scaling Trino is difficult. Starburst showcases Starburst Galaxy, a SaaS data platform built around the Trino query engine. This demoes running federated queries over Pokémon data scattered across MongoDB and Iceberg tables.

The world’s most valuable resource is data. Starburst provides a fast data lake query engine that utilizes low-cost data lake storage to provide consumers with easy and stable access to their data in open file formats, resulting in reduced data management costs and shorter time to insights for critical business decisions. Join this talk to learn how to implement a basic reporting structure that can help you operationalize your current data lake and perform various analytics. As an example, see how to group, filter, and aggregate the COVID-19 public data lake to answer proposed business questions. This presentation is brought to you by Starburst Data, an AWS Partner.

Data is arguably the most valuable asset that organizations have, but it's not easy to get it into the hands of end-users

Chatted with Tom about Data Lakehouse 101

In this hands-on lab, we guide you through the formation of data lake analytics using Amazon Simple Storage Service (Amazon S3) and Starburst Galaxy, with Covid-19 data as our sample set.

Dive into the world of data lakehouses with our latest video! Join us for a short breakdown of how data lakehouses revolutionize data management by combining the best features of data lakes and data warehouses. Discover how this innovative approach offers a modern solution to your data needs, providing the flexibility of data lakes with the structured querying capabilities of data warehouses.

Take a tour of Starburst Galaxy. See how Starburst Galaxy simplifies the catalog configuration process and cluster creation. Get an overview of admin functionality such as account creation, permission levels, usage and billing, audit logs, and query history.

Unlock the power of data products and bridge the gap between data producers and data consumers with our latest video! Join us as we explore how data products revolutionize the way data is accessed and utilized, empowering data consumers to make informed decisions and drive business outcomes.

Spending cycles building single-use ETL pipelines just to migrate a copy of data from one cloud to another? That all stops today with the introduction of cross-cloud analytics in Starburst Galaxy.

Introducing Gravity in Starburst Galaxy! Gravity is a unified access and governance layer that lets you manage all your data.

So, we have an early surprise for you all... Ahead of Launch Week, we are announcing row filters and column masks! Subscribe to our channel for all the exciting announcements next week.

The data lakehouse architecture has taken the analytics world by storm, applying critical data warehouse-like capabilities to the data lake. To achieve this desired result, you need to select two critical components of your lakehouse - a query engine and a table format. In this workshop, Jack Klamer and Monica Miller will lead you through how you can easily build and manage an open data lakehouse architecture using open-source technologies such as Trino and Apache Iceberg to support your growing analytics. Trino is an open source highly parallel and distributed query engine built from the ground up at Facebook for efficient, low-latency analytics. Iceberg is an open source, highly performant table storage format that enables an engine like Trino to perform data warehousing SQL functionality such as UPDATE, DELETE, and MERGE commands on the data lakehouse. Jack and Monica will help you configure and build a sample data lakehouse, transform your data, highlight key Iceberg functionality, and produce a final output ready to be utilized by downstream consumers.

In this episode we have the pleasure to chat with our colleagues, who now make the Trino community better every day