![]() You can learn what data is most used by your organization, partners, and customers.ĭata Cloud: Another important feature in Snowflake that is very relevant for Data Mesh architecture is that Snowflake Cloud Data Platform is cloud agnostic. You can even monitor easily how your data is used, understand who accesses your data and when they do it. With Snowflake, you can reduce substantially cost and time to market. Simplified and reduced time to market: Data sharing happens usually through methods like API, FTP and cloud bucket storage which is much more complex and costly. ![]() That’s exactly one of the most important objectives of Data Mesh. Sharing data helps improving customer satisfaction, increase transparency, and boost business performance. What is Snowflake?ĭata sharing can be done to both your internal teams, departments as well as external organizations, such as your customers and partners. So that data users can get value from aggregation and correlation of independent data products – the mesh is behaving as an ecosystem following global interoperability standards standards that are into the platform. and federated computational governance.So that the domain teams can create and consume data products autonomously using the platform abstractions, hiding the complexity of building, executing, and maintaining secure and interoperable data products. Self-serve data infrastructure as a platform:.So that data users can easily discover, understand, and securely use high quality data with a delightful experience data that is distributed across many domains So that the ecosystem creating and consuming data can scale out as the number of sources of data, number of use cases, and diversity of access models to the data increases simply increase the autonomous nodes on the mesh. Domain-oriented decentralized data ownership and architecture.According to Zhamak , Data Mesh just like an interlaced network, is based on the four following principles: When Zhamak Dehghani, director of emerging technologies at ThoughtWorks in North America, suggested a new data platform architecture, which addresses these dimensions, she called it a Data Mesh. But it need not always be so complicated or ‘meshed up’. Moreover, the speed of response to change is not as expected in many cases. Added to this burden is unclear data ownership and suboptimal communication between the development team of the data platform, the teams who build the source systems and the business users who consume the data of the data platform. And the challenge only multiplies when the number of data sources in the platform and the demand for data use cases increase quickly. So while a robust data architecture might enable better business intelligence, it is often a huge task to negotiate a central data lake, a central curated data area and a serving area with a big and central team of data engineers and data scientists. While most of the modern enterprises consider themselves as data-powered or data driven, not many know how to address the challenges in the way we build data platform. But before you start reading this piece, just picture a snowflake in your mind – one of a kind symmetrical crystal reflecting the perfect internal order of water molecules arranged in predetermined spaces. So what’s the connection between Data Mesh and Snowflake? Or maybe we begin by explaining what they even mean in the first place. ![]()
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