Achieve Proactive Data Observability for Your Lakehouse
Making sense of all your input data isn’t fun, especially when you’re consuming inputs from 10s to 1000s of data sources daily. If your data engineering teams are consuming massive amounts of data, across multiple data pipelines, it’s nearly impossible to be confident in the quality of your data.
Instead of retroactive data monitoring, it’s time for more of a proactive approach to ensure better data quality.
Join this session to learn:
• Challenges most code-driven engineers face with input data
• The five steps to proactive data observability
• How Databand can helps teams observe, monitor, and alert on data in transit
Josh comes from a varied background with a common thread of data obsession. He started in the finance world, working first as an analyst at a quant investment firm, then at Bessemer Venture Partners where he focused on investing in data and ML companies. Before founding Databand.ai, he worked as a product manager at Sisense, a high growth analytics company, where he built product capabilities geared toward data engineering teams. He started Databand.ai with his two co-founders to help data engineers deliver more reliable data products. He holds a B.Sc from Cornell University.
Ryan is the IBM Product Strategy Leader. He’s passionate about solving data quality and reliability problems through data observability. Before IBM, Ryan led Go-To-Market programs at high-growth startups in software test automation, DevOps, and IAM cybersecurity.