The team at Agile Data Engine likes to write about things we are passionate about: DataOps, data quality, solving business cases and keeping up with the evolving technology landscape.
Data quality issues are present in all data warehouses. But how can you tell if the quality of your data is good? The answer: set data quality dimensions.
In DataOps, terms get muddled by jargon - as does data when stored in a data lake, eventually devolving into to a data swamp. Here's a brief guide to help.
DataOps is an approach that has gained popularity in recent years as a way to manage and develop data solutions in an efficient and reliable manner.
Short-term and long-term data quality monitoring will allow you to trust the data flowing through your data platform and to take action to improve it.
This blog post explores modeling principles that enable compliance with regulatory requirements all while preserving the data model's structural integrity.
A blog post about dataops, analytics and data teams in 2022.
When our customer wanted to test drive multiple cloud databases, we used agile Data Engine to duplicate the dataflows to Snowflake and BigQuery.
