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.
Have you ever wondered who's accountable if your data warehouse starts draining your budget? Can you monitor usage and identify the sources of costs?
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.
Business real-time data processing tackles the need for speed that data warehouses face when data from several sources guide business-critical decisions.
A blog post about dataops, analytics and data teams in 2022.
At the Snowflake Summit, the demo that combined Snowpark for Python, Streamlit, and Native Applications showed a smooth developer experience for app devs.
Tips on how to process the source data to speed up the loading process and model the data to reduce the number of loads and joins without losing any data.
When our customer wanted to test drive multiple cloud databases, we used agile Data Engine to duplicate the dataflows to Snowflake and BigQuery.
Solita is investing in the SaaS business with Agile Data Engine. The aim is to facilitate international success stories of value creation with data.
DataOps is an agile approach to enterprise data development and operations that helps achieve faster time to value with data products across their lifecycle.
