Making sense of all your input data isn’t fun, especially when consuming inputs from 10s to 1000s of data sources daily.
If your data teams are orchestrating massive amounts of data across multiple data pipelines, it’s nearly impossible to feel confident in the data quality within your data warehouse.
Instead of retroactive data monitoring, it’s time for more of a proactive approach to ensure better data quality for your warehouse.
Join this session to learn:
• How data observability can provide data quality monitoring for cloud warehouses like Snowflake, BigQuery, Redshift
• How to use data quality checks to detect duplicates, NULLs, and anomalies in your tables
• Live demo of Databand providing end-to-end incident management for data in transit (data pipeline quality) and data at rest (data warehouse quality)
Eric is a Data Solution Architect at IBM. He’s passionate about helping customers detect data issues earlier and resolve them faster. Before IBM, Eric consulted companies on becoming more data-driven as a data architect and principal software engineer. When not talking to customers, you can find Eric smoking North Carolina BBQ and hanging with his family.