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The original writeup, How to Move
Beyond a Monolithic Data Lake to a Distributed Data Mesh –
which I encourage you to read before
joining me back here – empathized with today’s pain points of architectural
and organizational challenges in order to become data-driven, use data to
compete, or use data at scale to drive value. It offered an alternative
perspective which since has captured many organizations’ attention, and
given hope for a different future. While the original writeup describes the
approach, it leaves many details of the design and implementation to one’s
imagination. I have no intention of being too prescriptive in this article,
and kill the imagination and creativity around data mesh implementation.
However I think it’s only responsible to clarify the architectural aspects
of data mesh as a stepping stone to move the paradigm forward.
This article is written with the intention of a follow up. It summarizes
the data mesh approach by enumerating
its underpinning principles, and the high level logical architecture
that the principles drive. Establishing the high level logical model
is a necessary foundation before I dive into detailed architecture of
data mesh core components in future articles.
Hence, if you are in search of a prescription around exact tools and recipes
for data mesh, this article may disappoint you. If you are seeking a simple
and technology-agnostic model that establishes a common language, come along.
The great divide of data
What do we really mean by data? The answer depends on whom you ask.
Today’s landscape is divided into operational data and
analytical data. Operational data sits in databases behind business
capabilities served with microservices, has a transactional nature,
keeps the current state and serves the needs of the applications running
the business. Analytical data is a temporal and aggregated view of the
facts of the business
over time, often modeled to provide retrospective or future-perspective
insights; it trains the ML models or feeds the analytical reports.
The current state of technology, architecture
and organization design is reflective of the divergence of these two data
planes – two levels of existence, integrated yet separate.
This divergence has led to a fragile architecture.
Continuously failing ETL (Extract, Transform, Load) jobs and ever growing
complexity of labyrinth of data pipelines, is a familiar sight to many who
attempt to connect these two planes, flowing data from operational data plane
to the analytical plane, and back to the operational plane.
Figure 1: The great divide of data
Analytical data plane itself has diverged into two main architectures
and technology stacks: data lake
and data warehouse;
with data lake supporting data science access patterns, and data warehouse
supporting analytical and business intelligence reporting access patterns.
For this conversation, I put aside the dance between the two technology stacks:
data warehouse attempting to onboard data science
workflows and data lake
attempting to serve data analysts and
The original writeup on data mesh explores the
challenges of the existing
analytical data plane architecture.
Figure 2: Further divide of analytical data – warehouse
Figure 3: Further divide of analytical data – lake
Data mesh recognizes and respects the differences between these two planes:
the nature and topology of the data, the differing use cases, individual personas
of data consumers, and ultimately their diverse access patterns. However it
attempts to connect these two planes under a different structure – an inverted
model and topology based on domains and not technology stack – with
a focus on the analytical data plane. Differences in today’s available technology
to manage the two archetypes of data, should not lead to separation of organization,
teams and people work on them.
In my opinion, the operational and transactional data technology and topology is
relatively mature, and driven largely by the microservices architecture; data is
hidden on the inside of each microservice, controlled and accessed through the
microserivce’s APIs. Yes there is room for innovation to truly achieve
multi-cloud-native operational database solutions, but from the architectural
perspective it meets the needs of the business. However it’s the management and
access to the analytical data that remains a point of friction at scale. This is
where data mesh focuses.
I do believe that at some point in future our technologies will evolve to
bring these two planes even closer together, but for now, I suggest we keep their
Core principles and logical architecture of data mesh
Data mesh objective is to create a foundation for getting
value from analytical data and historical facts at scale – scale being
applied to constant change of data landscape, proliferation of both
sources of data and consumers, diversity of transformation and
processing that use cases require, speed of response to change.
To achieve this objective, I suggest that there are four
underpinning principles that any data mesh
implementation embodies to achieve the promise of scale, while delivering
quality and integrity guarantees needed to make data usable : 1) domain-oriented
decentralized data ownership and architecture, 2) data as a product,
3) self-serve data infrastructure as a platform, and 4) federated computational governance.
While I expect the practices, technologies and implementations of these
principles vary and mature over time, these principles remain unchanged.
I have intended for the four principles to be collectively necessary
and sufficient; to enable scale with resiliency while addressing concerns
around siloeing of incompatible data or increased cost of operation.
Let’s dive into each principle and then design the conceptual architecture
that supports it.
Data mesh, at core, is founded in decentralization
and distribution of responsibility to people who are
closest to the data in order to support continuous change and scalability.
The question is, how do we decompose and decentralize the components of the
data ecosystem and their ownership.
The components here are made of analytical data,
its metadata, and the computation necessary to serve it.
Data mesh follows the seams of organizational units as the axis of
decomposition. Our organizations today are decomposed based on their
business domains. Such decomposition localizes the impact of continuous
change and evolution – for the most part – to the domain’s
Hence, making the business domain’s bounded context a good candidate for
distribution of data ownership.
In this article, I will continue to use the same use case as the
original writeup, ‘a digital media company’. One can imagine that the
media company divides its operation, hence the systems and teams that
support the operation, based on domains such as ‘podcasts’, teams and
systems that manage podcast publication and their hosts; ‘artists’,
teams and systems that manage onboarding and paying artists, and so on.
Data mesh argues that the ownership and serving of the analytical data
should respect these domains. For example, the teams who manage
‘podcasts’, while providing APIs for releasing podcasts, should also be
responsible for providing historical data that represents ‘released
podcasts’ over time with other facts such as ‘listenership’ over time.
For a deeper dive into this principle see
decomposition and ownership.
Logical architecture: domain-oriented data and compute
To promote such decomposition, we need to
model an architecture that arranges the analytical data by domains.
In this architecture, the domain’s interface to the rest of
the organization not only includes the operational capabilities but also
access to the analytical data that the domain serves. For example,
‘podcasts’ domain provides operational APIs to ‘create a new
podcast episode’ but also an analytical data endpoint for retrieving
‘all podcast episodes data over the last <n> months’. This implies
that the architecture must remove any friction or coupling to let
domains serve their analytical data and release the code that computes
the data, independently of other domains. To scale, the architecture
must support autonomy of the domain teams with regard to the release and
deployment of their operational or analytical data systems.
The following example demonstrates the principle of domain oriented
data ownership. The diagrams are only logical representations and
exemplary. They aren’t intended to be complete.
Each domain can expose one or many operational APIs, as well as one or many
analytical data endpoints
Figure 4: Notation: domain, its analytical data
and operational capabilities
Naturally, each domain can have dependencies to other domains’ operational and
analytical data endpoints. In the following example, ‘podcasts’ domain consumes analytical data
of ‘users updates’ from the ‘users’ domain, so that it can provide a picture of the demographic of
podcast listeners through its ‘Podcast listeners demographic’ dataset.
Figure 5: Example: domain oriented ownership of analytical
data in addition to operational capabilities
Note: In the example, I have used an imperative language for accessing
the operational data or capabilities, such as ‘Pay artists’.
This is simply to emphasize the difference
between the intention of accessing operational data vs. analytical data.
I do recognize that in practice operational APIs are implemented
through a more declarative interface such as accessing a RESTful resource
or a GraphQL query.
Data as a product
One of the challenges of existing analytical data architectures is the
high friction and cost of discovering, understanding, trusting, and ultimately using
quality data. If not addressed, this problem only exacerbates with data
mesh, as the number of places and teams who provide data – domains –
increases. This would be the consequence of our first principle of
Data as a product principle is designed to address the data quality and age-old
problem; or as Gartner calls it dark data
– “the information assets
organizations collect, process and store during regular business activities,
but generally fail to use for other purposes”. Analytical data provided by
the domains must be treated as a product, and the consumers of that data
should be treated as customers – happy and delighted customers.
The original article enumerates a list of
discoverability, security, explorability, understandability, trustworthiness,
etc., that a data mesh implementation should support for a domain data
to be considered a product. It also details the roles such as
domain data product owner that
organizations must introduce, responsible for the objective measures that
ensure data is delivered as a product. These measures include data quality,
decreased lead time of data consumption, and in general data user satisfaction
through net promoter score. Domain data
product owner must have a deep understanding of who the data users are, how do they
use the data,and what are the native methods that they are comfortable with consuming the data.
Such intimate knowledge of data users results in design of data product interfaces that meet their needs.
In reality, for majority of data products on the mesh, there a few conventional personas with
their unique tooling and expectations, data analysts and data scientists. All data products
can develop standardized interfaces to support them. The conversation between users of the data
and product owners is a necessary piece for establishing the interfaces of data products.
Each domain will include data product developer roles, responsible
for building, maintaining and serving the domain’s data products. Data product
developers will be working alongside other developers in the domain.
Each domain team may serve one or multiple data products. It’s
also possible to form new teams to serve data products that don’t naturally
fit into an existing operational domain.
Note: this is an inverted model of responsibility compared to past paradigms.
The accountability of data quality shifts upstream as close to the source of the data
Logical architecture:data product the architectural quantum
Architecturally, to support data as a product that domains can
autonomously serve or consume, data mesh introduces the concept of data
product as its architectural quantum.
Architectural quantum, as defined by
Evolutionary Architecture, is
the smallest unit of architecture that can be
independently deployed with high functional cohesion, and includes all the
structural elements required for its function.
Data product is the node on the mesh that encapsulates three structural components required
for its function, providing access to domain’s analytical data as a product.
- Code: it includes (a) code for data pipelines responsible for
consuming, transforming and serving upstream data – data received from domain’s
operational system or an upstream data product; (b) code for APIs that provide
access to data, semantic and syntax schema, observability metrics and other metadata;
(c) code for enforcing traits such as access control policies,
compliance, provenance, etc.
- Data and Metadata: well that’s what we are all here for, the underlying
analytical and historical data in a polyglot form.
Depending on the nature of the domain data and its
consumption models, data can be served as events, batch files, relational
tables, graphs, etc., while maintaining the same semantic.
For data to be usable there is an associated set of metadata including data
computational documentation, semantic and syntax declaration,
quality metrics, etc; metadata that is intrinsic to the data e.g. its semantic definition,
and metadeta that communicates the traits used by
computational governance to
implement the expected behavior e.g. access control policies.
The infrastructure component enables building, deploying and running the data product’s
code, as well as storage and access to big data and metadata.
Figure 6: Data product components as one architectural quantum
The following example builds on the previous section, demonstrating the
data product as the architectural quantum. The diagram only includes
sample content and is not intended to be complete or include all design
and implementation details. While this is still a logical representation it
is getting closer to the physical implementation.
Notation: domain, its (analytical) data product and operational system
Figure 8: Data products serving the
domain-oriented analytical data
Note: Data mesh model differs from the past paradigms where pipelines (code)
are managed as independent components from the data they produce;
and often infrastructure, like an instance of a warehouse or a lake storage account,
is shared among many datasets.
Data product is a composition of all components – code, data and infrastructure – at
the granularity of a domain’s bounded context.
Self-serve data platform
As you can imagine, to build, deploy, execute, monitor, and access a
humble hexagon – a data product – there is a fair bit of infrastructure
that needs to be provisioned and run; the skills needed to provision this
infrastructure is specialized and would be difficult to replicate in each
domain. Most importantly, the only way that teams can autonomously own
their data products is to have access to a high-level abstraction of
infrastructure that removes complexity and friction of provisioning and
managing the lifecycle of data
products. This calls for a new principle, Self-serve data infrastructure
as a platform to enable domain autonomy.
The data platform can be considered an extension of the delivery
platform that already exists to run and monitor the services. However the
underlying technology stack to operate data products, today, looks very
different from delivery platform for services. This is simply due to divergence of
big data technology stacks from operational platforms. For example,
domain teams might be deploying their services as
Docker containers and the delivery platform uses Kubernetes for their
orchestration; However the neighboring data product might be running its
pipeline code as Spark jobs on a Databricks cluster. That requires
provisioning and connecting two very different sets of infrastructure,
that prior to data mesh did not require this level of interoperability and
interconnectivity. My personal hope is that we start seeing a convergence
of operational and data infrastructure where it makes sense. For example,
perhaps running Spark on the same orchestration system, e.g.
In reality, to make analytical data product development accessible to
generalist developers, to the existing profile of developers that domains
have, the self-serve platform needs to provide a new category of tools
and interfaces in addition to simplifying provisioning. A self-serve data
platform must create tooling that supports a domain data product
developer’s workflow of creating, maintaining and running data products
with less specialized knowledge that existing technologies assume;
self-serve infrastructure must include capabilities to lower the current
cost and specialization needed to build data products. The original
writeup includes a list of capabilities that a self-serve data platform
provides, including access to scalable polyglot data storage, data
products schema, data pipeline declaration and orchestration, data products lineage,
compute and data locality, etc.
Logical architecture: a multi-plane data platform
The self-serve platform capabilities fall into multiple categories or
planes as called in the model. Note: A plane is representative of a level
of existence – integrated yet separate. Similar to physical and
consciousness planes, or control and data planes in networking. A plane is
neither a layer and nor implies a strong hierarchical access model.
Figure 9: Notation: A platform plane that provides
a number of related capabilities through self-serve interfaces
A self-serve platform can have multiple planes that each serve a
different profile of users. In the following example, lists three different
data platform planes:
- Data infrastructure provisioning plane: supports the provisioning of
the underlying infrastructure, required to run the components of a data
product and the mesh of products. This includes provisioning of a
distributed file storage, storage accounts, access control
management system, the orchestration to run data products internal code,
provisioning of a distributed query engine on a graph of data
products, etc. I would expect that either other data platform planes
or only advanced data product developers use this interface directly.
This is a fairly
low level data infrastructure lifecycle management plane.
- Data product developer experience plane: this is the main interface
that a typical data product developer uses.
This interface abstracts many of
the complexities of what entails to support the workflow of a data product developer.
It provides a higher level of abstraction than the ‘provisioning plane’.
It uses simple declarative interfaces to manage the lifecycle of a data product.
It automatically implements the cross-cutting concerns that are
defined as a set of standards and global conventions, applied to all data
products and their interfaces.
- Data mesh supervision plane: there are a set of capabilities that are
best provided at the mesh level – a graph of connected data products –
globally. While the implementation of each of these interfaces might rely
on individual data products capabilities, it’s more convenient to provide
these capabilities at the level of the mesh. For example, ability to
discover data products for a particular use case, is best provided by
search or browsing the mesh of data products; or correlating multiple data
products to create a higher order insight, is best provided through
execution of a data semantic query that can operate across
multiple data products on the mesh.
The following model is only exemplary and is not intending to be
complete. While a hierarchy of planes is desirable, there is no strict
layering implied below.
Figure 10: Multiple planes of self-serve data platform
*DP stands for a data product
Federated computational governance
As you can see, data mesh follows a distributed system architecture;
a collection of independent data products, with independent lifecycle,
built and deployed by likely independent teams. However for the majority
of use cases, to get value in forms of higher order datasets, insights
or machine intelligence there is a need for these independent data
products to interoperate; to be able to correlate them, create unions,
find intersections, or perform other graphs or set operations on them at scale.
For any of these operations to be possible, a data mesh implementation
requires a governance model that embraces decentralization and domain
self-sovereignty, interoperability through global standardization, a dynamic
topology and most importantly automated execution of decisions by
the platform. I call this a federated computational governance. A
decision making model led by the federation of domain data product
owners and data platform product owners, with autonomy and domain-local
decision making power, while creating and adhering to a set of global
rules – rules applied to all data products and their interfaces – to
ensure a healthy and interoperable ecosystem. The group has a difficult
job: maintaining an equilibrium between centralization and
decentralization; what decisions need to be localized to each domain and
what decisions should be made globally for all domains. Ultimately
global decisions have one purpose, creating interoperability and
a compounding network effect through discovery and composition
of data products.
The priorities of the governance in data mesh are different from
traditional governance of analytical data management systems. While they
both ultimately set out to get value from data, traditional data
governance attempts to achieve that through centralization of decision
making, and establishing global canonical representation of data with
minimal support for change. Data mesh’s federated computational governance,
in contrast, embraces change and multiple interpretive contexts.
Placing a system in a straitjacket of constancy can cause fragility to evolve.
— C.S. Holling, ecologist
Logical architecture: computational policies embedded in the mesh
A supportive organizational structure, incentive model and
architecture is necessary for the federated governance model to
function: to arrive at global decisions and standards for
interoperability, while respecting autonomy of local domains, and
implement global policies effectively.
Figure 11: Notation: federated computational
As mentioned earlier, striking a balance between what shall be
standardized globally, implemented and enforced by the platform for all
domains and their data products, and what shall be left to the domains
to decide, is an art. For instance the domain data
model is a concern that should be localized to a domain who is most
intimately familiar with it. For example, how the semantic and syntax of
‘podcast audienceship’ data model is defined must be left to the
‘podcast domain’ team. However in contrast, the decision around
how to identify a ‘podcast listener’ is a global concern. A podcast listener
is a member of the population of ‘users’ – its
upstream bounded context – who
can cross the boundary of domains and be found in other domains such as ‘users play streams’.
The unified identification allows correlating information about ‘users’
who are both ‘podcast listeners’ and ‘steam listeners’.
The following is an example of elements involved in the data mesh
governance model. It’s not a comprehensive example and only
demonstrative of concerns relevant at the global level.
Figure 12: : Example of elements of a
federated computational governance: teams, incentives, automated implementation,
and globally standardized aspects of data mesh
Many practices of pre-data-mesh governance, as a centralized
function, are no longer applicable to the data mesh paradigm. For example,
the past emphasis on certification of golden datasets – the datasets
that have gone through a centralized process of quality control and
certification and marked as trustworthy – as a central
function of governance is not longer relevant. This had
had stemmed from the fact that in the previous data management
paradigms, data – in whatever quality and format – gets extracted from
operational domain’s databases and gets centrally stored in a warehouse
or a lake that now requires a centralized team to apply cleansing,
harmonization and encryption processes to it; often under the
custodianship of a centralized governance group. Data mesh completely
decentralizes this concern. A domain dataset only
becomes a data product after it locally, within the domain, goes
through the process of quality assurance according to the
expected data product quality metrics and the global standardization
rules. The domain data product owners are best placed to decide how to
measure their domain’s data quality knowing the details of domain
operations producing the data in the first place. Despite such localized
decision making and autonomy, they need to comply with the modeling of
quality and specification of SLOs based on a global standard, defined by
the global federated governance team, and automated by the platform.
The following table shows the contrast between centralized (data
lake, data warehouse) model of data governance, and data mesh.
|Pre data mesh governance aspect||Data mesh governance aspect|
|Centralized team||Federated team|
|Responsible for data quality||Responsible for defining how to model what constitutes|
|Responsible for data security||Responsible for defining aspects of data security i.e. data|
sensitivity levels for the platform to build in and monitor
|Responsible for complying with regulation||Responsible for defining the regulation requirements for the|
platform to build in and monitor automatically
|Centralized custodianship of data||Federated custodianship of data by domains|
|Responsible for global canonical data modeling||Responsible for modeling polysemes – data elements that|
cross the boundaries of multiple domains
|Team is independent from domains||Team is made of domains representatives|
|Aiming for a well defined static structure of data||Aiming for enabling effective mesh operation embracing a|
continuously changing and a dynamic topology of the mesh
|Centralized technology used by monolithic lake/warehouse||Self-serve platform technologies used by each domain|
|Measure success based on number or volume of governed data (tables)||Measure success based on the network effect – the|
connections representing the consumption of data on the
|Manual process with human intervention||Automated processes implemented by the platform|
|Prevent error||Detect error and recover through platform’s automated|
Principles Summary and the high level logical architecture
Let’s bring it all together, we discussed four principles underpinning
|Domain-oriented decentralized data ownership and architecture||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.
|Data as a product||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.
|Self-serve data infrastructure as a platform||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
|Federated computational governance||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 baked computationally into the platform.
These principles drive a logical architectural model that while brings
analytical data and operational data closer together under the same domain, it
respects their underpinning technical differences. Such differences include
where the analytical data might be hosted, different compute technologies for
processing operational vs. analytical services, different ways of querying and
accessing the data, etc.
Figure 13: Logical architecture of data mesh approach
I hope by this point, we have now established a common language and a logical
mental model that we can collectively take forward to detail the blueprint
of the components of the mesh, such as the data product, the platform, and
the required standardizations.
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