Have data quality issues? What about reliability problems? You may be hearing a lot of these terms, along with many others, that describe issues you face with your data. What’s the difference, which are you suffering from, and how do you tackle both? Knowing that your Airflow DAGs are green is not enough. It’s time to focus on data reliability and quality measurements to build trust in your data platform. Join this session to learn how Databand’s proactive data observability platform makes it easy to achieve trusted data in your pipelines. In this session we’ll cover: The core differences between data reliability vs data quality What role the data platform team, analysts, and scientists play in guaranteeing data quality Hands-on demo setting up Airflow reliability tracking

We’ve all heard the phrase “data is the new oil.” But really imagine a world where this analogy is more real, where problems in the flow of data - delays, low quality, high volatility - could bring down whole economies? When data is the new oil with people and businesses similarly reliant on it, how do you avoid the fires, spills, and crises? As data products become central to companies’ bottom line, data engineering teams need to create higher standards for the availability, completeness, and fidelity of their data. In this session we’ll demonstrate how Databand helps organizations guarantee the health of their Airflow pipelines. Databand is a data pipeline observability system that monitors SLAs and data quality issues, and proactively alerts users on problems to avoid data downtime. The session will be led by Josh Benamram, CEO and Cofounder of Databand.ai. Josh will be joined by Vinoo Ganesh, an experienced software engineer, system architect, and current CTO of Veraset, a data-as-a-service startup focused on understanding the world from a geospatial perspective. Join to see how Databand.ai can help you create stable, reliable pipelines that your business can depend on!

While Airflow is a central product for data engineering teams, it’s usually one piece of a bigger puzzle. The vast majority of teams use Airflow in combination with other tools like Spark, Snowflake, and BigQuery. Making sure pipelines are reliable, detecting issues that lead to SLA misses, and identifying data quality problems requires deep visibility into DAGs and data flows. Join this session to learn how Databand’s observability system makes it easy to monitor your end-to-end pipeline health and quickly remediate issues. This is a sponsored talk, presented by Databand .