Data Mesh

Data Mesh architecture is a decentralized approach that enables teams to perform cross-domain data analysis independently. It involves a cultural shift in how companies think about their data. Instead of data acting as a by-product of a process, it becomes the product, where data producers act as data product owners.

Data Mesh is like microservices but for analytical data. And as the microservices, there is no single solution for Data Mesh architecture. Different organizations will have various Data Mesh implementations supported by multiple architectures.

It’s worth noting that data mesh promotes the adoption of cloud-native and cloud platform technologies to scale and achieve data management goals. As this distributed architecture is beneficial in scaling data needs across an organization, it is understood that a data mesh may not be for all types of businesses. Smaller companies may not reap the benefits of a data mesh as their enterprise data may not be as complex as a larger organization.

To come up with the best implementation, you need to understand the core principle of Data Mesh from a technology perspective:

  • Domain ownership requires an organization to divide data products according to clear value streams rather than technical boundaries. It’s also essential that every data product team is responsible for looking after their own pipelines and policies, as well as the data storage and output ports (such as APIs).
  • Data as a product principle requires an ecosystem that is flexible enough to fulfill the demands of its consumers. The domain team is responsible for the operations of the data product during its entire lifecycle. Remember, as a product, the data should always be well-maintained, well-versioned, and well-documented.
  • Self-serve data platform empowers and supports developers to focus on building great data products without wasting their time and energy on manual configurations, infrastructure maintenance, or any unnecessary repetitive tasks.
  • Federated computational governance ensures that distributed ownership is balanced with standardization. Two critical reasons for this are making data products more discoverable inside an organization and guaranteeing and maintaining certain quality, interoperability, and security standards.

vs. Data Lake

Data Lake is a low-cost storage environment aggregating data from various sources. It is typically used as a dumping ground for data as it frequently ingests data that still needs to have a defined purpose. On the other hand, a Data Mesh is an architectural approach to data, which a data lake can be a part of. Data Mesh is a distributed data architecture where data is organized by its domain to make it more accessible to users across an organization.

vs. Data Fabric

Data Fabric is an architectural concept that focuses on automating data integration, data engineering, and governance in a data value chain between data providers and consumers.

A data fabric is complementary to a data mesh instead of mutually exclusive. In fact, the data fabric makes the data mesh better because it can automate key parts of the data mesh, such as creating data products faster, enforcing global governance, and making it easier to orchestrate the combination of multiple data products.

Data Mesh Technology

Data Mesh implementation can be done using off-the-shelf cloud and data platform solutions. If you’re interested in using them, please check this overview. However, it’s important to remember that they are not Data Mesh platforms; none (at the time of writing) are a silver bullet for a successful Data Mesh implementation.

These are some caveats that you need to be aware of when considering using the off-the-shelf approach:

  • They are based on services/features, not data products.
  • They don’t have a concept of custom APIs for data. There may be generic data APIs, but if developers want to write code to expose a custom API for data or a machine learning model, that may require going outside the data platform.
  • Their ETL philosophy isn’t polyglot — they are typically opinionated.
  • They offer a generic experience without any option to curate what tools developers can choose.
  • The account-based tenancy model can be very restrictive. You’ll likely hit limits if you try to do many things in a single cloud account.

In summary, if you are on a journey to find the best technology for Data Mesh implementation in your organization, you need to identify the critical use cases early on and what you want to achieve. Every tool has its own character and features that best suit a specific scenario. You want to avoid getting trapped by the means other organizations say are best for data mesh architecture and later find that they pursue different things like yours.


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