Data teams across companies have continuous challenges of consolidating data, processing it and making it useful. They deal with challenges such as a mixture of multiple ETL jobs, long ETL windows capacity-bound on-premise data warehouses and ever-increasing demands from users. They also need to make sure that the downstream requirements of ML, reporting and analytics are met with the data processing.
The Cloudera Enterprise Data Maturity Report is a global survey of 3,150 business and IT decision makers assessing organizations’ maturity when it comes to their current capabilities and handling of data and analytics.
Stitch is pleased to announce the availability of Microsoft SQL Server as a destination. MS SQL Server joins nine other data destinations (including Microsoft Azure Synapse) that Stitch supports to help execute all your data modeling and analysis projects. Stitch customers can immediately benefit from the new destination, which supports both Azure SQL Server and standard SQL Server editions reaching as far back as SQL Server 2012.
About the report The Cloudera Enterprise Data Maturity Report is a global survey of 3,150 business and IT decision makers assessing organizations’ maturity when it comes to their current capabilities and handling of data and analytics.
The following is Part III of a three-part series. Welcome to the final installment of a three-part series discussing the areas to take seriously when you want to drive business with analytics. In Part I of this series, I discussed how to prioritize data accessibility and how to address the challenges that come with it. Those challenges include: Part II discussed where the disconnect is and addressed how organizations can bridge the gap.
Over the course of my next two blog posts, I would like to share my thoughts around a debate raging in data architecture circles. The bone of contention? That the 21st century needs a new data management paradigm for modern analytics. First up, I’ll frame the argument and explain the two prominent approaches of data hub and data fabric. Then, I’ll cover data mesh and compare all three architectures. As always, I’d love to get your input, feedback, queries and comments!
