Data Mesh: Understanding Its Applications, Opportunities, and Constraints 


Data has experienced a metamorphosis in its perceived value and management within the corporate sphere. Previously underestimated and frequently discarded, data was often relegated to basic reports or neglected due to a lack of understanding and governance. This limited vision, combined with emerging technologies, led to an overwhelming influx of data, and nowhere for it to go. There was little to no governance or understanding of what data they had, or how long they had it.  

In the early 2000s, enterprises primarily used siloed databases, isolated data sets with limited accessibility. The 2010s saw the rise of Data Warehouses, which brought together disparate datasets but often led to bottlenecks. Data Lakes emerged as a solution to store vast quantities of raw data and quickly became swamps without adequate governance. Monolithic IT and data engineering groups would struggle to document, catalog, and secure the growing stockpile of data. Product owners and teams that would want, or need access to data would have to request access and wait. Sometimes those requests would end up in a backlog and forgotten about.  

In this new dawn of data awareness, the Data Mesh emerges as a revolutionary concept, enabling organizations to efficiently manage, process, and gain insights from their data. As organizations realize data’s pivotal role in digital transformation, it becomes imperative to shift from legacy architectures to more adaptive solutions, making Data Mesh an attractive option.  

 

The Basics of a Data Mesh 

The importance of personalized customer experiences should not be understated. More than ever, consumers are faced with endless options. To stand out from competitors, businesses must use data and customer behavior insights to curate tailored and dynamic customer journeys that both delight and command their audience. Analyze purchasing history, demographics, web activity, and other data to understand your customer, as well as their likes and dislikes. Use these insights to design customized customer experiences that increase conversion, retention, and ultimately, satisfaction.  

When discussing data architecture concepts, the terms “legacy” or “traditional” imply centralized data management concepts, characterized by monolithic architectures developed and maintained by a data engineering organization within the company. Business units outside of IT would often feel left in the dark, waiting for the data team to address their specific needs and leading to inefficiencies. 

First coined in 2019, the Data Mesh paradigm is a decentralized, self-service approach to data architecture. There are four central principles that Data Mesh is based on: Domain ownership, treating data as a product, self-service infrastructure, and federated computational governance. 

With Data Mesh, teams (Domains) are empowered to own and manage their data (Product). This requires stewardship at the team level to effectively manage their own resources to ingest, persist and serve data to their end users. Data stewards are responsible for the quality, reliability, security, and accessibility of the data. Data stewards bridge the gap between decentralized teams and enterprise-level governance and oversight. 

While teams enjoy autonomy, chaos would ensue without a federated governance approach. This ensures standards, policies and best practices are followed across all product owners and data stewards.  

Implementing a Data Mesh requires significant investment in both infrastructure and enhancing teams with the resources and expertise required to manage their own resources. It requires a fundamental change in companies’ mindset of how they treat data.  

While a Lakehouse would aim to combine the best of Data Lakes and Data Warehouses, Data Mesh ventures further by decentralizing ownership and control of data. While Data Fabric focuses on seamless data access and integration across disparate sources, Data Mesh emphasizes domain-based ownership. On the other hand, event-driven architectures prioritize real-time data flow and reactions, which can be complementary to Data Mesh. 

data mesh decentralized architecture

When and Where to Implement Data Mesh 

  1. Large Organizations with Data Rich Domains: With large organizations, departments often deal with a deluge of data.  From Human Resources to Sales, each team has their own requirements for how their data is used, stored, and accessed. As teams consume more data, time to market and development efficiency suffer in centralized architectures. External resources and time constraints are often the biggest issue. By implementing Data Mesh, teams can work independently and take control of their data, increasing efficiency and quality. As a result, teams can optimize and enrich their product offering and cut costs by streamlining ELT/ETL processes and workflows. 

With direct control over their data, teams can tune and tailor their data solutions to better meet customer needs.  

  1. Complex Ecosystem: Organizations, especially those operating in dynamic environments with intricate interdependencies, often face challenges in centralized data structures. In such architectures, there’s limited control over resource allocation, utilization, and management, which can hinder teams from maximizing the potential of their data. Centralized approaches can curtail innovation due to rigid schemas, inflexible data pipelines, and lack of domain-specific customization. Data Mesh offers organizations the flexibility to adapt to evolving data needs and utilize domain-specific expertise to curate, process, and consume data tailored to their unique requirements. 
  1. Rapidly growing data environments: Today’s digital age sees organizations collecting data at an unprecedented scale. The sheer volume of data can be overwhelming with the influx of IoT devices, vendor integrations, user interactions, and digital transactions. Centralized teams often grapple with scaling issues, processing delays, and the challenge of timely data delivery. Data Mesh addresses this by distributing the data responsibility across different domains or teams. Multiple decentralized units handle the influx as data inflow increases, ensuring timely processing and reducing system downtime. The result is a more resilient data infrastructure ready to meet both current demands and future needs. 

When Not to Implement Data Mesh 

  1. Small to Medium-sized Enterprises (SMEs): While Data Mesh presents numerous advantages, it may not be suitable for all organizations or projects. Smaller organizations typically handle lower data volumes and may not possess the resources needed to manage their data independently. In these cases, a centralized data architecture would be more suitable to minimize complications in design and maintenance with fewer resources to manage them. 
  1. Mature and Stable Centralized Architectures: Organizations usually only turn to new solutions when they are experiencing problems. If a well-established centralized architecture is performing and fitting the needs of the company, there isn’t a need necessarily for Data Mesh adoption. Introducing a fundamental change in how data is managed is an expensive and disruptive undertaking. Building new infrastructure and expanding team capabilities changing organizational culture takes time.  
  1. Short-term Projects: Implementing a Data Mesh requires significant time and resource investment. The benefits of a Data Mesh won’t be seen when building or designing a limited lifespan project or proof of concept. If a project’s duration doesn’t justify the investment of a Data Mesh or the scope doesn’t require domain-specific data solutions, then the benefits of a Data Mesh aren’t utilized. Traditional data architectures are usually more appropriate for these applications and don’t need the oversight/governance that a Data Mesh requires.

  

Opportunities Offered by Data Mesh 

  1. Scalability: Data Mesh enables organizations to scale their data processing capabilities more effectively by enabling teams to control how and when their data is processed, optimizing resource use and costs, and ensuring they remain agile amidst expanding data sources and consumer bases.  
  1. Enhanced Data Ownership: Treating data as a product rather than a byproduct or a secondary asset is revolutionary. By doing so, Data Mesh promotes a culture with a clear sense of ownership and accountability. Domains or teams that “own” their data are more inclined to ensure its quality, accuracy, and relevance. This fosters an environment where data isn’t just accumulated but is curated, refined, and optimized for its intended purpose. Over time, this leads to more prosperous, more valuable data sets that genuinely serve the organization’s needs. 
  1. Speed and Innovation: Decentralization is synonymous with autonomy. When teams have the tools and the mandate to manage their data, they are not bogged down by cross-team dependencies or bureaucratic delays. They can innovate, experiment, and iterate at a faster pace, resulting in expanded data collection and richer data sets. This agility accelerates data product development, enabling organizations to adapt to changing needs quickly, capitalize on new opportunities, and stay ahead of the curve in the competitive market.  
  1. Improved Alignment with Modern Architectures: Decentralization isn’t just a trend in data management; it’s a broader shift seen in modern organizational architectures, especially with the rise of microservices. Data Mesh naturally aligns with these contemporary structures, creating a cohesive environment where data and services coexist harmoniously. This alignment reduces friction, simplifies integrations, and ensures that the entire organizational machinery, services, and data operate in a unified, streamlined manner. 
  1. Enhanced Collaboration: As domains take ownership of their data, there’s an inclination to collaborate with other domains. This cross-functional collaboration fosters knowledge sharing, best practices, and a unified approach to data challenges, driving more holistic insights.

Constraints and Challenges 

  1. Cultural Shift: Teams may not want to own their own data or have the experience to take on the responsibility. Training initiatives, workshops, and even hiring external experts might be necessary to bridge these skill gaps. 
  1. Increased Complexity: Developing an environment that supports a Data Mesh architecture is not without its challenges. As the Data Mesh model expands, managing the growing number of interconnected resources and solving integration issues to ensure smooth communication between various domains can be a considerable obstacle. Planning appropriately to support teams with access, training and management of a Data Mesh is critical to its evolution and success. This includes well defined requirements for APIs, data exchange, and interface protocols. 
  1. Cost Implications: Transitioning to a Data Mesh could entail substantial upfront costs, including hiring additional resources, training personnel, investing in new infrastructure, and possibly overhauling existing systems. 
  1. Governance: Data Governance has become a hot topic as data architectures grow and mature. Ensuring a consistent view of data across all domains can be challenging, especially when multiple teams update or alter their datasets independently. Tools to manage integrity, security and compliance are a requirement in a Data Mesh architecture. The need for teams to have autonomy in a decentralized environment is balanced with a flexible but controlled governance model that is the foundation for federated governance. This can be a challenge when initially designing the model based on team requirements, but it’s an important step to take as early as possible when building a data platform.  

Skillset: Evolving with the Data Mesh Paradigm

With an evolved mindset, the Data Mesh paradigm demands expertise that may not have previously been cultivated within traditional data teams. This transition from central data lakes to domain-oriented data products introduces complexities requiring a deep understanding of the data and the specific use cases it serves, both internally and externally. Skills such as collaboration, domain-specific knowledge translation, and data stewardship become vital. As data responsibility becomes decentralized, each team member’s role becomes more critical in ensuring data integrity, relevance, and security. As data solutions evolve, teams must adopt a mindset of perpetual learning, keeping pace with the latest methodologies, tools, and best practices related to managing their data effectively. 

Embracing the Data Mesh

In the evolving landscape of data management, the Data Mesh presents a promising alternative to traditional architectures. It’s a journey of empowerment, efficiency, and decentralization. The burgeoning community support for Data Mesh, evident from the increasing number of case studies, forums, and tools developed around it, underscores its pivotal role in the future of data management. However, its success hinges on an organization’s readiness to embrace the cultural and operational shifts it demands. As with all significant transformations, due diligence, meticulous planning, and an understanding of the underlying principles are crucial for its fruitful adoption. Embracing the Data Mesh is more than just a technological shift; it’s a paradigm transformation. Organizations willing to make this leap will find themselves not just keeping up with the rapid pace of data evolution but leading the charge in innovative, data-driven solutions.  

Digital Transformation Trends that Future-Proof Your Business


The core of future-proofing your business lies in the incorporation of cutting-edge technological trends and strategic digitization of your business operations. Combining new, transformative solutions with tried-and-true business methods is not only a practical approach but an essential one when competing in this digital age. Using the latest digital transformation trends as your guide, start envisioning the journey of future-proofing your business in order to unlock the opportunities of tomorrow. 

#1 Personalization  

The importance of personalized customer experiences should not be understated. More than ever, consumers are faced with endless options. To stand out from competitors, businesses must use data and customer behavior insights to curate tailored and dynamic customer journeys that both delight and command their audience. Analyze purchasing history, demographics, web activity, and other data to understand your customer, as well as their likes and dislikes. Use these insights to design customized customer experiences that increase conversion, retention, and ultimately, satisfaction.  

#2 Artificial Intelligence  

AI is everywhere. From autonomous vehicles and smart homes to digital assistants and chatbots, artificial intelligence is being used in a wide array of applications to improve, simplify, and speed up the tasks of everyday life. For businesses, AI and machine learning have the power to extract and decipher large amounts of data that can help predict trends and forecasts, deliver interactive personalized customer experiences, and streamline operational processes. Companies that lean on AI-driven decisions are propelled into a world of efficiency, precision, automation, and competitiveness.  

#3 Sustainability 

Enterprises, particularly those in the manufacturing industry, face increasing pressure to act more responsibly and consider environmental, social, and corporate governance (ESG) goals when making business decisions. Digital transformations are one way to support internal sustainable development because they lead to reduced waste, optimized resource use, and improved transparency. With sustainability in mind, businesses can build their data and technology infrastructures to reduce impact. For example, companies can switch to more energy-efficient hardware or decrease electricity consumption by migrating to the cloud.  

#4 Cloud Migration 

More and more companies are migrating their data from on-premises to the cloud. In fact, by 2027, it is estimated that 50% of all enterprises will use cloud services1. What is the reason behind this massive transition? Cost saving is one of the biggest factors. Leveraging cloud storage platforms eliminates the need for expensive data centers and server hardware, thereby reducing major infrastructure expenditures. And while navigating a cloud migration project can seem challenging, many turn to cloud computing partners to lead the data migration and ensure a painless shift.  

Future-Proof Your Business Through Digital Transformation with Kopius

By embracing these digital transformation trends, your company is not only adapting to the current business landscape but also unlocking new opportunities for growth. Future-proofing your business requires a combination of strategic acumen and technical expertise. This is precisely where a digital transformation partner, who possesses an intimate grasp of these trends, can equip your business with the resources and solutions to confidently evolve. Reach out to Kopius today and let’s discuss a transformational journey that will future-proof your business for the digital future.  

Data Mesh Architecture in Cloud-Based Data Warehouses


Data is the new black gold in business. In this post, we explore how shifts in technology, organization processes, and people are critical to achieving the vision for a data-driven company that deploys data mesh architecture in cloud-based warehouses like Snowflake and Azure Synapse.

The true value of data comes from the insights gained from data that is often siloed and spans across structured, semi-structured, and unstructured storage formats in terabytes and petabytes. Data mining helps companies to gather reliable information, make informed decisions, improve churn rate and increase revenue.

Every company could benefit from a data-first strategy, but without effective data architecture in place, companies fail to achieve data-first status.

For example, a company’s Sales & Marketing team needs data to optimize cross-sell and up-sell channels, while its product teams want cross-domain data exchange for analytics purposes. The entire organization wishes there was a better way to source and manage the data for its needs like real-time streaming and near-real-time analytics. To address the data needs of the various teams, the company needs a paradigm shift to fast adoption of Data Mesh Architecture, which should be scalable & elastic.

Data Mesh architecture is a shift both in technology as well as in organization, processes, and people.

Before we dive into Data Mesh Architecture, let’s understand its 4 core principles:

  1. Domain-oriented decentralized data ownership and architecture
  2. Data as a product
  3. Self-serve data infrastructure as a platform
  4. Federated computational governance

Big data is about Volume, Velocity, Variety & Veracity. The first principle of Data mesh is founded on decentralization and distribution of responsibility to the SME\Domain Experts who own the big data framework.  

This diagram articulates the 4 core principles of Data Mesh and the distribution of responsibility at a high level.

Azure: Each team is responsible for its own domain, and data is decentralized and shared with other domains for data exchange and data as a product.
Snowflake: Each team is responsible for its own domain, and data is decentralized and shared with other domains for data exchange and data as a product.

Each Domain data is decentralized in its own data warehouse cloud. This model applies to all data warehouse clouds, such as Snowflake, Azure Synapse, and AWS Redshift.  

A cloud data warehouse is built on top of a multi-cloud infrastructure like AWS, Azure, and Google Cloud Platform (GCP), which allows compute and storage to scale independently. These data warehouse products are fully managed and provide a single platform for data warehousing, data lakes, data science team and to provide data sharing for external consumers.

As shown below, data storage is backed by cloud storage from AWS S3, Azure Blob, and Google, which makes Snowflake highly scalable and reliable. Snowflake is unique in its architecture and data sharing capabilities. Like Synapse, Snowflake is elastic and can scale up or down as the need arises.

From legacy monolithic data architecture to more scalable & elastic data modeling, organizations can connect decentralized enriched and curated data to make an informed decision across departments. With Data Mesh implementation on Snowflake, Azure Synapse, AWS Redshift, etc., organizations can strike the right balance between allowing domain owners to easily define and apply their own fine-grained policies and having centrally managed governance processes.

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Cloud Migration and Cloud Services


By Luca Junghans

A look inside these cloud capabilities

By joining forces, Valence and MajorKey offer an even greater set of cloud services for businesses that want to power their digital transformation with cloud technologies. 

MajorKey works with clients to migrate business applications to the cloud, and Valence builds services on the cloud. This is one reason these businesses are such a powerful combined force. 

The cloud refers to software and services that run on a (usually) regionally located server owned by the cloud service provider, instead of on an on-premise server owned by a customer. Cloud servers are in data centers all over the world. By using cloud computing, companies don’t have to manage physical servers or run software applications on their own machines. 

It’s big business. In fact, one of our partners, AWS contributed 14.5% of revenue to Amazon’s overall business in 2021, which would have operated at a $1.8 billion loss in Q4 without it – and AWS revenue was up nearly 39% compared to 2020. 

There are many ways to use and understand the business impact of cloud technology. We are breaking down the distinction between cloud services and cloud migration for you here!

Cloud Migration and Cloud Services 

Simply put, cloud migration is what happens when a company moves some or all of its software onto cloud servers.

In other words, cloud migration is moving your software to a managed server operated by the cloud provider; and cloud services are technology solutions built on top of those managed servers. There’s a whole range of capabilities bridging the two. 

Let’s take a closer look.  

Cloud services range in how much they abstract away from the customer.  A good example is Amazon Cognito, which is a user management cloud service. Amazon Cognito has implementations of basic user functions such as login, logout, sessions, and security, so a customer doesn’t have to worry about a deeper technical implementation of these features and can focus on managing users.  

Cloud services are so flexible that there are seemingly infinite ways to deploy them for a business. Cloud services are the infrastructure, platforms, and software hosted by cloud providers, and there are three common solutions:   

  1. Infrastructure as a service: The renting out of virtual machines and space to customers, while providing a way to remotely manage the resource. When a company migrates to the cloud, they are using this service. 
  2. Platforms: Providers like AWS and Azure build specialized software on top of their own cloud hardware and offer the software to customers as a service. These are specialty services and can provide patterns for things such as Data Analysis, Compute, IoT, APIs, Security, Identity, and Containerization. We wrote about Digital Twins in a previous post, which referenced Digital Twin platforms offered by AWS and Azure.  
  3. Software as a service (SaaS): Software can be built on top of the platforms offered by the cloud providers. Software developers can also partner with other third parties to provide fully built instances of software that typically come with subscription rates, customer support, and personal configurations of the software. Examples of this include Atlassian Jira and Confluence, Dropbox, Salesforce, and G suite

These services can be transformative for businesses in general, but it’s not always easy to know the best way for your business to use them. The added benefits to this migration range per case, and here are four examples: 

  • Scalability: Cloud services often offer on demand scaling options that can satisfy unexpected or planned growth. Depending on your product, this can be a lot easier than upgrading on-premise hardware, but not always cheaper. 
  • Cost: Although we expect the costs to be passed to the consumer in some way, the logistics of maintenance and upgrades to the cloud systems is handled by the provider. In many cases this can translate to a huge amount of money saved for the customers. 
  • Performance: Performance-enhancing services like CDNs and regional hosting, when understood and configured properly, can have tangible and positive performance impacts. 
  • Local Management: Being on the cloud means access to the digital portals to manage the services (most times). This creates a lower bar of entry for employees to manage and observe the resources. 

Many businesses start their digital transformation journey by migrating infrastructure or applications from on-premises servers to the cloud. Notably, cloud migration can also refer to a situation where a business needs to bring the cloud resources they manage into an on-premises environment. It can also describe a situation where a business moves its data resources from one cloud provider to another.  

Cloud migration to use cloud services is a process that presents many upsides, and is worth investigating!  The process will add additional complexities – specifically, security and governance will generally be instituted upfront as a base for the rest of the migration. We design and engineer performant, scalable, and maintainable applications that save businesses money, fill in knowledge gaps, and provide users with a positive experience.  

Here are two examples of cloud services that we’ve built for clients:  

  • Building cloud applications with AWS lambda: We have bridged the gap between multiple third-party APIs and created new databases that consolidate data and deliver it to a web application. Cloud services remove the need for our clients to interact with these multiple services, which saves them time and money. At the same time, we used AWS Cognito to help our customer manage roles and users in a secure and trusted way. This removed the need for our engineers to write our own user management software, a cumbersome task. 
  • Data pipelines:  We identify problems in our customers’ current database providers and migrate data to a more performant and better structured database in cloud-to-cloud migrations.  

We will continue to build and migrate while we investigate the future of the cloud. What are the new services and platforms? Who can benefit the most from them? How can we do it right? We will be prepared for the cloud migration and services needed from the real world to the metaverse, and beyond.  

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