The new data technologies, along with legacy infrastructure, are driving market-driven innovations like personalized offers, real-time alerts, and predictive maintenance. However, these technical additions - ranging from data lakes to analytics platforms to stream processing and data mesh —have increased the complexity of data architectures. They are significantly hampering the ongoing ability of an organization to deliver new capabilities while ensuring the integrity of artificial intelligence (AI) models. https://us.sganalytics.com/blog/evolving-big-data-strategies-with-data-lakehouses-and-data-mesh/

1. Evolving Big Data Strategies: Bringing Data Lake and Data Mesh Vision
to Life
The new data technologies, along with legacy infrastructure, are
driving market-driven innovations like personalized offers, real-
time alerts, and predictive maintenance. However, these technical
additions - ranging from data lakes to analytics platforms to
stream processing and data mesh —have increased the
complexity of data architectures. They are significantly hampering
the ongoing ability of an organization to deliver new capabilities
while ensuring the integrity of artificial intelligence (AI) models.
Today's transactional data systems run between data warehouses
and operational databases like Oracle, Microsoft SQL Server, or
PostgreSQL. On the contrary, machine learning (ML) and analytics
have usually occurred in data lakes or data warehouses. While this
can indicate that the organization is on the right track, they can
probably notice a rise in costs related to ETL (extract, transform
and load), data access, and data management.
A recent MIT Technology Review survey stated that 47% of data
executives are experiencing a reduction in duplicated data as the
main factor of their data strategy initiatives. Approximately 50% of
organizations are copying the transactional data system from the
warehouse to the data lake. This has led to creating a negative
impact due to the rising cost of data movements. The associated
data latency, as well as the reliability implications, are also on the
rise.
Read more: How are Organizations Modernizing their Data
Security and Management?

2. Organizations can perform near real-time analysis on operational
databases, such as SQL server-based databases, or non-SQL DB,
such as Cosmos DB. Gartner Inc. defined this framework as hybrid
transactional/analytical processing (HTAP). Many cloud providers
are investing in tools to simplify this integration. In addition, if the
organization has a data catalog, it can contain all the data in the
data lake, allowing users to discover and use the data without
needing IT resources.
Organizations can reduce their ETL (extract, transform and load)
related costs as well as maximize the return on their investments
by loading all of their data into the data lake. They can prepare the
data based on the desired business logic and store it back for
applications and report usage. This approach assists in identifying
the data that the business requires without reinvesting in data
ingestion, thus significantly impacting the current
implementation.
But the current market dynamics do not allow for such slowdowns.
Hence industry leaders are making use of technological

3. innovations to upend the traditional data models, requiring
laggards to reimagine the aspects of data architecture.
Understanding Data Management Architecture
Data architecture specialists are familiar with the emerging
concept of data lakehouse and data mesh. While a data
lakehouse refers to different formats of data storage, analysis, and
queries, Data Mesh encompasses a series of concepts connected
to data management in a decentralized and large-scale manner.
These data architectures are enabling organizations to
democratize the use of data within operations. It is also assisting
them in managing data in a more flexible pattern than ever
before.
Data Lakehouse
Data leaders are investing their time and effort to establish a
unified platform to reduce their analytics infrastructure complexity
as well as to promote collaboration across crucial roles like data
engineers, data scientists, and business analysts. By doing so, they
can reduce costs, operate on the data in a more efficient manner,
focus on organizational challenges and adapt to ongoing
changes.
A unified platform will also enable the data scientists to develop,
deploy and operationalize their machine-learning models quickly.
This approach will assist in enriching the organizational data with
predictions, implying business analysts incorporate those into their
Power BI reports, thus shifting the insights from descriptive to
predictive.
Read more: The Rise of a Data-Driven Work Environment: What
Do Enterprises Need to Know
Organizations are shifting to an easy-to-use experience where the
data workload is purpose-built while being deeply integrated. The
potential evolution of the data architecture forms the basis of the

4. data lakehouse introduced. It proposes the idea that a single data
lakehouse is capable of supporting machine learning and
analytics in the same place while avoiding silos. With a single data
lakehouse, organizations can reduce data duplication, thus
facilitating a more effective usage of ML across their data
operations.
Data Mesh
Data mesh is emerging as a preferred data strategy by
organizations. A domain-driven analytical data architecture, data
mesh, helps businesses in facilitating data democratization as
data is becoming a company’s product, and data products have
different data product patterns.
Data Mesh functions on a socio-technical paradigm that works on
four principles:
• Principle of Domain Ownership - decentralization and
distribution of data responsibility to those who are closest
to the data.
• Principle of Data as a Product - the analytical data
delivered is treated as a product, and the consumers are
treated as customers.
• Principal of the Self-serve Data Platform - self-serve data
platform services that enable domains’ cross-functional
teams to transfer data.
• Principal of Federated Computational Governance - a
decision-making model for data product owners and
data platform product owners that provides them the
autonomy as well as the domain-local decision-making
power to create and adhere to a set of global rules.
For example: If a marketing business unit (BU) is looking to publish
a data product that defines the company’s top-selling products.
On the contrary, the operations BU also wanted to build demand-

5. like models, considering the mentioned top-selling products data
as input. With data mesh, the team will not have to transfer the
data between business units and create a disconnected copy.
Instead, they can just subscribe to the marketing data product
and utilize it in their analysis.
Data mesh is likely to fit in every organization. It functions best for
global and complex organizations that need to ensure that their
business units are sharing data effectively while working
independently on the data products.
Read more: What Is Data Democratization? How is it Accelerating
Digital Businesses?
The Process of Implementation and
Evolution
Implementing data lakehouse and data mesh as a part of the
organization’s big data strategy is a journey. And organizations
should embark on this journey without impacting their existing
business operations. For smooth incorporation of these data
components, businesses can undertake the following steps:

6. • Defining the desired future state by aligning business
goals, people development, and the evolution of
processes.
• Undertaking a data state assessment to identify the
organization’s current state.
• Performing a gap analysis to quantify the difference
between the current and desired state, recognize
opportunities, and design an actionable road map.
• Estimating different data lake options and performing a
pilot with a use case that involves data engineers and
data scientists.
• Processing data in place. Instead of employing a
traditional ETL for data movement into a data lakehouse,
businesses can opt for a TEL (transform, extract and load)
to process the data within the distributed data store.
• Establishing a data-driven culture plan that involves the
board of directors as stakeholders.

7. • Deploying the data mesh foundation services to create
the first data products.
• Formulating data governance services like data catalog,
data usage detection, and data classification.
Key Highlights
• Adaptive AI systems, data sharing, and data fabrics are
the emerging trends that data and analytics leaders
need to build on to drive resilience and innovation.
• These data and analytics trends will authorize
organizations to anticipate change and manage
uncertainty.
• Investing in trends most relevant to the organization can
help in meeting the CEO’s priority of returning to and
accelerating growth.
• With data being the most critical and significant
component of data architectures, organizations are
easing into the idea of incorporating data components
like data lakehouses and data mesh to build a
sustainable data environment.
Read more: Data Fabric and Architecture: Decoding the Cloud
Data Management Essentials
In Conclusion
As data, analytics, and AI are becoming more embedded in the
day-to-day operations of most organizations; it can be clearly
stated that a radically distinct approach to data architecture is
necessary to create and grow data-centric enterprises. Data and
technology leaders who are open to embracing this new
approach will better position their organizations and lead them on
the path to becoming agile, resilient, as well as competitive.

8. Organizations are now evolving their data architecture by
evaluating and integrating data lakehouse as well as data mesh
when required. To implement modern analytics and data
governance at scale, organizations must align their technology,
people, and processes to evolve as an intelligence-driven
enterprise.
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