Data meshes can require significant investment in terms of time, resources, and expertise. They can be complex to operate and maintain, especially at scale. It's a trade-off between modularity and simplicity. Data meshes can be difficult to change or adapt once they are in place.Data Mesh encourages businesses to create cross-functional teams, each responsible for a specific domain. Small businesses can start by identifying key domains : sales, marketing, customer service.The Benefits of Using a Data Mesh Architecture
The data mesh enables data from disparate systems to be collected, integrated, and analyzed all at once, thus eliminating the need to extract data from disparate systems in one central location for further processing.
Is data mesh not a technology : Data mesh is often misunderstood as a specific technology or solution – when instead, it's more of a conceptual framework. To be exact, it's a “sociotechnical approach,” as described by Zhamak Dehghani, the creator of the data mesh concept.
Why is data mesh obsolete
In its 2022 data management hype cycle, Gartner regarded data mesh as obsolete before plateau, arguing that the original data mesh concept will be obsolete before it reaches what Gartner calls the “plateau of productivity.” That doesn't mean it is dead, but it might die out, or be absorbed or recast via another …
What problems does Datamesh solve : Data mesh, using data-as-a-product, enables organizations to expose data to all domains. This empowers sharing and collaboration across teams. It also avoids duplication of effort and helps ensure agility. The image below shows how different domains can share data-as-a-product, increasing agility.
A data mesh can help large organizations manage data successfully—if it's understood that implementing one involves more than technology considerations. Gartner's stance on data mesh
Interestingly, Gartner also predicts that the data mesh concept will become “obsolete before plateau.” The Gartner Hype Cycle for Data Management, 2022 – Source: Gartner. This forecast does not necessarily imply that the paradigm is currently obsolete.
Is data mesh the future
Data mesh is not going away anytime soon.
As firms mature and accelerate digital and AI investments, they will focus more on business-value-driven data product creation. Domain ownership defines the context of the data product domain. Self-service will continue to grow.A data mesh is best for distributed organizations where data is a key component of cross-functional operations. These organizations tend to leverage large volumes of data sources and require faster experimentation with that data as a key component of their business operations.Gartner situates data mesh in the “innovation trigger” phase in 2022, which is often seen as the conception of a technology's lifecycle. Interestingly, Gartner also predicts that the data mesh concept will become “obsolete before plateau.” Snowflake's platform allows domain teams to operate independently and yet easily share data products with each other. Each domain can designate which data objects to share and then publish product descriptions in a Snowflake Data Exchange, which serves as an inventory of all data products in the data mesh.
Why the data mesh doesn t work : Lack of domain talent density
One of the biggest reasons why data mesh initiatives are so frequently unsuccessful is due to the level of talent that's required to make them work.
Why is Snowflake a good match for data mesh : Snowflake's platform allows domain teams to operate independently and yet easily share data products with each other. Each domain can designate which data objects to share and then publish product descriptions in a Snowflake Data Exchange, which serves as an inventory of all data products in the data mesh.
Is data mesh federated
Data mesh is an alternative to data architectures that separate the operational and analytical data planes. Data mesh, by contrast, seeks to harmonize these two planes through a federated model that focuses on data domains instead of technology. In the 2022 Data Management Hype Cycle, Gartner moved data mesh to “Obsolete before plateau”, but this is a prediction. Data mesh will continue to grow but be broken down into smaller components that will be subsumed by other aspects of emerging data tools.Despite its advantages, such as extreme scalability, automatic performance tuning, and strong data security, Snowflake faces challenges like higher costs compared to competitors, lack of native cloud integration, and limited support for unstructured data.
When would you not use Snowflake : Snowflake is not suited for low-latency, high-concurrency queries. Even if you do decide to just “throw money at it” and use Snowflake for user-facing features, you'll quickly run into freshness, latency, and concurrency constraints. Snowflake processes queries in a job pool.
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Antwort What are the downsides of data mesh? Weitere Antworten – What is the disadvantage of data mesh
Data meshes can require significant investment in terms of time, resources, and expertise. They can be complex to operate and maintain, especially at scale. It's a trade-off between modularity and simplicity. Data meshes can be difficult to change or adapt once they are in place.Data Mesh encourages businesses to create cross-functional teams, each responsible for a specific domain. Small businesses can start by identifying key domains : sales, marketing, customer service.The Benefits of Using a Data Mesh Architecture
The data mesh enables data from disparate systems to be collected, integrated, and analyzed all at once, thus eliminating the need to extract data from disparate systems in one central location for further processing.
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Is data mesh not a technology : Data mesh is often misunderstood as a specific technology or solution – when instead, it's more of a conceptual framework. To be exact, it's a “sociotechnical approach,” as described by Zhamak Dehghani, the creator of the data mesh concept.
Why is data mesh obsolete
In its 2022 data management hype cycle, Gartner regarded data mesh as obsolete before plateau, arguing that the original data mesh concept will be obsolete before it reaches what Gartner calls the “plateau of productivity.” That doesn't mean it is dead, but it might die out, or be absorbed or recast via another …
What problems does Datamesh solve : Data mesh, using data-as-a-product, enables organizations to expose data to all domains. This empowers sharing and collaboration across teams. It also avoids duplication of effort and helps ensure agility. The image below shows how different domains can share data-as-a-product, increasing agility.
A data mesh can help large organizations manage data successfully—if it's understood that implementing one involves more than technology considerations.
![]()
Gartner's stance on data mesh
Interestingly, Gartner also predicts that the data mesh concept will become “obsolete before plateau.” The Gartner Hype Cycle for Data Management, 2022 – Source: Gartner. This forecast does not necessarily imply that the paradigm is currently obsolete.
Is data mesh the future
Data mesh is not going away anytime soon.
As firms mature and accelerate digital and AI investments, they will focus more on business-value-driven data product creation. Domain ownership defines the context of the data product domain. Self-service will continue to grow.A data mesh is best for distributed organizations where data is a key component of cross-functional operations. These organizations tend to leverage large volumes of data sources and require faster experimentation with that data as a key component of their business operations.Gartner situates data mesh in the “innovation trigger” phase in 2022, which is often seen as the conception of a technology's lifecycle. Interestingly, Gartner also predicts that the data mesh concept will become “obsolete before plateau.”
![]()
Snowflake's platform allows domain teams to operate independently and yet easily share data products with each other. Each domain can designate which data objects to share and then publish product descriptions in a Snowflake Data Exchange, which serves as an inventory of all data products in the data mesh.
Why the data mesh doesn t work : Lack of domain talent density
One of the biggest reasons why data mesh initiatives are so frequently unsuccessful is due to the level of talent that's required to make them work.
Why is Snowflake a good match for data mesh : Snowflake's platform allows domain teams to operate independently and yet easily share data products with each other. Each domain can designate which data objects to share and then publish product descriptions in a Snowflake Data Exchange, which serves as an inventory of all data products in the data mesh.
Is data mesh federated
Data mesh is an alternative to data architectures that separate the operational and analytical data planes. Data mesh, by contrast, seeks to harmonize these two planes through a federated model that focuses on data domains instead of technology.
![]()
In the 2022 Data Management Hype Cycle, Gartner moved data mesh to “Obsolete before plateau”, but this is a prediction. Data mesh will continue to grow but be broken down into smaller components that will be subsumed by other aspects of emerging data tools.Despite its advantages, such as extreme scalability, automatic performance tuning, and strong data security, Snowflake faces challenges like higher costs compared to competitors, lack of native cloud integration, and limited support for unstructured data.
When would you not use Snowflake : Snowflake is not suited for low-latency, high-concurrency queries. Even if you do decide to just “throw money at it” and use Snowflake for user-facing features, you'll quickly run into freshness, latency, and concurrency constraints. Snowflake processes queries in a job pool.