Building a modern data stack of the future
Many organizations spend way too much time and money getting data ready for analysis — before a single insight is gleaned. Learning how to innovate with data science and machine learning (ML) is challenging because that capability needs to be integrated into the IT stack. The traditional approach of standing up a data stack for AI does not work because it’s too complex to manage data replication between multiple platforms.
Business impacts due to data integration challenges
Critical capabilities of a modern data stack
How automated ELT and data lakehouses are key components of data stack modernization strategies
Common use cases for the combination of Fivetran and Databricks
Building a modern data stack of the future
Many organizations spend way too much time and money getting data ready for analysis — before a single insight is gleaned. Learning how to innovate with data science and machine learning (ML) is challenging because that capability needs to be integrated into the IT stack. The traditional approach of standing up a data stack for AI does not work because it’s too complex to manage data replication between multiple platforms.
Business impacts due to data integration challenges
Critical capabilities of a modern data stack
How automated ELT and data lakehouses are key components of data stack modernization strategies
Common use cases for the combination of Fivetran and Databricks