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3 steps to build a data fabric to integrate all your data tools
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Considerable effort has gone into software development practices and how they relate to data, but have we gone far enough in how we think about and use data? Data fabrics can help.
One approach for better data utilization is the data fabric, a data management approach that arranges data in a single “fabric” that spans multiple systems and endpoints. The goal of the fabric is to link all data so it can easily be accessed.
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“DataOps and data fabric are two different but related things,” said Ed Thompson, CTO at Matillion, which provides a cloud data integration platform. “DataOps is about taking practices which are common in modern software development and applying them to data projects. Data fabric is about the type of data landscape that you create and how the tools that you use work together.” 
Thompson said the key to moving data from an exercise in interaction to a malleable fabric that can meet the needs of any application is the development of metadata, which is knowledge about the data itself. This knowledge must be able to be passed on from tool to tool.
“In a typical data stack, all the tools involved have additive knowledge about the core data,” Thompson said. “For example, a pipeline tool knows where the data came from. A data integration tool knows how it has been transformed. A quality tool can assess how trustworthy it is. An analytics tool knows how valuable it is to business users… and so on. If you want to get an end-to-end picture of the lifecycle of that data, you need to be able to see all that information from right across your data estate. This is what a data fabric gives you: a unified view of your consumption layer that can coordinate around the performance of data.”
SEE: Snowflake data warehouse platform: A cheat sheet (free PDF) (TechRepublic)
Unfortunately, commercial data tools don’t always work together, and the industry still has a way to go to realize the full potential of this concept. Companies don’t do much with data fabrics, either, unless their IT is at an advanced state of technical maturity that includes the development of a data fabric. That makes it easy for corporate IT departments to just forgo data fabric work for now, but it still doesn’t mean that IT shouldn’t be thinking about it as a future direction.
If your company would like to move forward with building this useful tool, here are the steps to take:
3 steps to build a data fabric to integrate all your data tools
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