In today’s data-driven world, businesses must deal with complex challenges related to managing, integrating, and properly using massive amounts of data housed in multiple locations. Organizations that unlock the right data architectural approach empower themselves with much better decision-making and strategic insights. Two popular approaches — data mesh and data fabric — have surfaced as prominent and innovative solutions for handling data at scale.

This blog looks at some of the risks associated with data mesh and why organizations need to look at more than just the concepts of distributed data management to ensure successful data mesh. Companies need to evaluate the needs for managing their data products, data governance, the use of data platforms, and how business domains will be managed across the data ecosystem.

What’s the right way to manage growing volumes of enterprise data, while providing the consistency, data quality and governance required for analytics at scale? Is centralizing data management in a data lake the right approach? Or is a distributed data mesh architecture right for your organization? When it comes down to it, most organizations seeking these solutions are looking for a way to analyze data without having to move or transform it via complex extract, transform and load (ETL) pipelines.

Scania is at the forefront of a more autonomous, connected, electric future for the transportation industry. Find out why its Head of Data and Information Management uses data mesh—and Snowflake—to make it a reality. Scania is a global truck, bus, and industrial engine manufacturer and offers an extensive range of related services so its customers can focus on their core business.

Current data architecture is going through a revolution. Enterprises are starting to shift away from the monolithic data lake towards something less centralized: data mesh. It’s a relatively new concept, first coined in 2019, that addresses potential issues with data warehouses and data lakes that can cause businesses to be slow, unresponsive, or even suffer from data silos. What is a data mesh, and how could it benefit your business?

The world is full of different types of goals. Consider football. The goal in football is at the end of the field. A runner either crosses the goal or they don't, when trying to make a touchdown. Or, consider basketball. In basketball, when a player shoots the ball, the player’s shot either goes through the net or it doesn’t. Alternatively, consider ice hockey. When a hockey player shoots the puck, it either goes into the net — or it doesn’t.