A new Enterprise data model, Data Mesh, is gaining popularity.
Data meshes are essentially a combination of storage, compute and data analytics software to provide solutions for enterprises looking for easy access to their data assets.
Data meshes differ from traditional data lakes in several ways. For example:
A data mesh uses technologies like Hadoop, Spark and Kafka as a platform for storing and processing data. This allows companies to perform real-time processing on their datasets as opposed to waiting until after the fact like with most traditional approaches that use MySQL databases or NoSQL databases (like MongoDB).
Data meshes have built-in collaboration tools that allow multiple users in an organization to share information seamlessly without any additional effort required by IT teams. This allows employees across departments who may not normally work together at all times but need access to each other’s projects be able to collaborate effectively on projects without having access issues due lack of communication between departments; so instead of having multiple people working on different tasks only one person will do all of them at once without needing approval from anyone else because they can see what others are doing while also doing their own thing at once which cuts down overall costs significantly when compared with using traditional methods like emailing back forth between teams before decisions need approval.
The difference between a data lake and a data mesh
The difference between a data lake and a data mesh is the way they store and analyze data. A centralized architecture like Hadoop, Kafka, or Spark clusters can be considered a data lake. These single-point failures are susceptible to outages or errors that may impact your entire organization’s ability to get real-time answers.
By contrast, decentralized architectures involving an array of business domain specific systems and applications that can be used in conjunction with each other offer an alternate approach: building an open source system that uses multiple copies of the same dataset across different nodes in order to keep things consistent without exposing your organization to single points of failure.
Put simply, Data mesh is a decentralized data architecture that eliminates the need for a centralized data lake. Data lakes are clearly inefficient and scale poorly, which makes them unsuitable for modern business needs. In contrast, the decentralized nature of the data mesh makes it more efficient and scalable than any centralized solution.
Data Mesh architecture is designed to overcome some of the architectural disadvantages of Data Lake.
Here are some ways that Data Mesh aims to overcome the disadvantages of a traditional Data Lake:
Data Mesh is a decentralized architecture; it’s not dependent on any single point of failure. You can scale up or down as needed and you have control over your own data stores.
Data Mesh enables you to store your data independently from application services that consume it so there are no bottlenecks in accessing your data or integrating with third-party applications. This also enables greater flexibility in terms of deployments and maintenance by eliminating the risk of an outage resulting from changes made by a single team member.
A distributed system scales horizontally while maintaining performance because each technological node or business unit operates independently without any dependencies on other nodes or central services such as message queues, databases etc.
Data meshes are an ideal solution for organizations looking to better manage and make data more readily available to users throughout the organization. With a data mesh model, users can access data through an API (application programming interface) rather than having to access the centralized data lake. This is beneficial because it makes it easier for IT teams to scale as needed, which isn’t possible with traditional models like lakes or cubes.