Data warehouses and data lakes are critical elements used by many organizations to support modern data platforms. However, the outdated architectural design of present-day data platforms poses considerable challenges and have inevitable drawbacks that require attention.
Data Mesh, one of the most recent data architectures, addresses long-standing bottlenecks and constraints that might be present in monolithic architecture designs. Data Mesh prevents data siloes by developing an architecture where data is purposefully spread among multiple mesh nodes.
Data Mesh: A Mystified Concept?
Zhamak Dehghani from ThoughtWorks originally put down the phrase “Data Mesh,” in 2019 which characterized it as “a socio-technical paradigm shift—a new strategy for acquiring, processing, and exchanging data for analytical reasons.”
Data Mesh is designed in a way that splits massive monolithic organizational data architectures into further domains and subsystems. The decentralized approach of Data Mesh enables the use of data as a product, and also makes data more readily available and accessible by distributing data ownership to the business teams.
Demystifying the 4 Key Pillars of Data Mesh Architecture
Pillar 1: Domain-oriented Decentralized Data Ownership
This approach aims to enhance scalability by providing decentralized control over data delivery. Data Mesh enables the segmentation of data among several business domains. Under this approach, each domain such as sales, finance, customer support, and human resources, has ownership of its data.
Pillar 2: Data-as-a-product
Data-as-a-product refers to treating data as a first-class citizen with capabilities to accomplish specific objectives. With this approach, data is easily accessible and provides a 360° view of organizational activity across all sources. Data Mesh enables data to be viewed as a product to share instead of an asset to collect and in this way guarantees core data functionalities such as discoverability, addressability, trustworthiness, self-describing semantics, interoperability standards, data security, and more.
4 Principals of Data Mesh and their interplay
Pillar 3: Self-service Data Infrastructure As a Platform
A self-service data infrastructure platform entails applying platform thinking to the data infrastructure. In other words, it involves offering a sound foundation, standardized tools, and interfaces for users across all domains. The infrastructure leverages self-service data platforms, allowing a variety of domain owners to manage their data effectively using the integrated mechanism.
Pillar 4: Federated Computational Governance
When implementing Data Mesh, it is necessary to have an effective governance model in place. Serving as the key link, this governance model maintains the proper equilibrium between centralized and decentralized data ecosystems. Governance models should support decentralized domain sovereignty, international regulations, and automated decision-making, all of which are made feasible with federated computational governance architecture and practices.
Data Mesh: Recognizing and Overcoming Challenges
Although Data Mesh may provide more robust and adaptable data delivery than legacy architectures, it is not a one-size-fits-all approach for businesses. Even with a decentralized structure and comprehensive data governance, some underlying challenges will need to be addressed.
Below are the top 3 challenges of implementing a Data Mesh architecture:
Data Ingestion and Duplication
One of the most challenging elements of the Data Mesh architecture is configuring the data ingestion framework. Data ingestion and configuration are often performed recurrently, which increases the likelihood of data duplication, inconsistencies, and waste. Such inefficiencies can result in discrepancies that may impact the enterprise data ecosystem or raise the total cost of ownership.
Long-drawn-out Adoption Process
The adoption of Data Mesh has been slow as most businesses still choose conventional data management methodologies. Data Mesh adoption requires decentralizing all business data along with 360° infrastructure upgrades, which can be complex and time-consuming processes.
Cross-dimensional Analytics
Although the Data Mesh architecture overcomes many data challenges, it does not provide cross-domain analytics. This can be catered to by adding features such as data formatting rules, search capabilities, and other properties to the Data Mesh architecture.
Why Should Businesses Consider Adopting Data Mesh Architecture?
By 2025, global data volumes will surpass 180 zettabytes, estimates by Statista. Today, many businesses’ data analysis capacities are held back due to the architectural limitations of traditional data platforms. However, Data Mesh can overcome these challenges as it was designed primarily to address the data scaling issue. Here are some key things organizations should consider before implementing Data Mesh:
Redundancy of Centralized or Monolithic Data Models
It is not surprising that centralized distributed systems occasionally experience problems such as data silos, inconsistencies in data, and lack of data integration. The Data Mesh architecture resolves this issue by disconnecting and breaking down the centralized architecture, which makes ETL tasks more efficient.
More Accessibility to Data
Data accessibility is unquestionably one of the most elementary benefits of Data Mesh architectures. With the help of this framework, business owners can manage, modify, or integrate data easily.
Standardized Data Observability
Standardized data discoverability and observability are key elements of data infrastructure, and Data Mesh delivers these features seamlessly. As a part of its overall strategy, Data Mesh optimizes data health while conforming to best practices and allows business users to make the most from their data.
Increased Speed and Scalability
After deploying a robust Data Mesh architecture, businesses can experience increased speed and scalability. By providing direct access to data, Data Mesh empowers business users through reduced reliance on business intelligence teams. It further eliminates the need to manually update data, thereby empowering teams with greater flexibility when configuring and analyzing data.
Key Takeaway
Data Mesh is a paradigm shift, a unique method of managing data as it breaks down siloed data and makes data more accessible and observable. It allows business users across the organization to access and analyze data securely, quickly, and easily. Consider Data Mesh if your centralized data management ecosystem isn’t giving you the outcomes you require and you want to overcome the shortcomings of conventional data lakes. With Data Mesh, data owners within your organization will have more autonomy and flexibility over their data, enabling easier access to data for analysis. Your company will benefit from enhanced data governance, data scalability, and a robust data platform, all of which will enable you to meet your business objectives.
Looking to revamp your data architecture for improved decision-making and system efficiency? Get in touch with Visionet.