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

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Ready to Get Started? Here Are Some Resources to Help


Smart Data – Smart Factory with Octotronic and Dremio

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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.

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