A Guide to Decentralized Data Architectures
Traditional data architectures have often been structured like medieval towns – with resources, workers, and supplies kept in centralized locations that are easy to control and defend. This is not without reason, as storing, managing, and accessing data from a single repository can enable your organization with consistent controls over who can access which data, when, and for what purpose.
However, the continued growth of data and users has made one fault of centralized architectures clear – they simply cannot scale effectively. Just as medieval nobles needed to expand their reach to leverage advancing agriculture and trade, data-driven practices cannot evolve and scale in rigid, confined spaces. This is especially true for teams that are growing exponentially, as they need an architecture that can adapt at the pace of business.
In this blog, we’ll explore the increasing popularity of decentralized data architectures, sharing their key components, inherent benefits and challenges, and the most popular forms in contemporary data use.
What is a Decentralized Data Architecture?
A decentralized data architecture is an approach to cloud data management that separates data storage and data analysis capabilities across various platforms and tools, rather than uniting them within a single centrally managed ecosystem.
The National Institute of Standards and Technology (NIST) defines a decentralized network as “a network configuration where there are multiple authorities that serve as a centralized hub for a subsection of participants.”
Ultimately, a decentralized architecture enables distributed ownership and use of data, while still maintaining some level of centralized control over data access and security. Data can be stored and analyzed in distinct domains, each controlled and managed by a relevant user or team within your organization.
Components of a Decentralized Data Architecture
Some of the key components of decentralized data architectures include:
- Distributed Storage: Data is no longer stored in a single, centralized location. Instead, it is distributed across multiple domains, each of which is maintained for a specific purpose or use case, and managed by the team(s) who work most closely with the data.
- Federated Governance: Since data is not centrally stored, it is also not controlled by one centralized entity. Teams must create and apply local, domain-specific data access controls, while the central data security team sets global policies to which every domain must adhere.
- Interoperability: While data and users operate within distinct domains, data should not be completely siloed or inaccessible. Users often need to work cross-functionally to drive business initiatives, and decentralized architectures should facilitate cross-domain data sharing to foster collaboration.
By combining these components, your team can create a decentralized data architecture without losing some of the core functions of centralized architectures – adequate storage, accessibility, and data security.
Benefits & Challenges of Decentralized Data Architectures
As with other architectural paradigms, decentralization has both inherent benefits and challenges.
Benefits of Decentralized Data Architectures
1. Data Democratization
Decentralization begets data democratization – easier, more scalable data access for a larger number of users within your organization, regardless of their role. This is crucial for businesses that are experiencing rapid growth, as an increase in data users should not be a burden on your data ecosystem or the teams that manage it. With decentralization, new users can more quickly gain access to the domains and data that they need, without having to request access from a single, centralized resource.
2. Reduced Management Burden
This segues into another benefit: a reduced management burden on central data teams. Centralized architectures are often maintained by consolidated teams, which must review and grant access requests from all of an organization’s users. As users multiply, if this team stays the same, the burden of access management quickly grows. When teams are responsible for their own domains, these centralized managers are free to maintain more effective oversight across the whole organization.
3. Operability at Scale
Lastly, and arguably most importantly, decentralized architectures are built to scale. This is a significant change from centralized ecosystems, which are ultimately limited by their unified nature. When domains can be created and managed at will – becoming an interoperable part of the larger ecosystem – your organization’s data-driven operations can grow at the pace of your business needs.
Challenges of Decentralized Data Architectures
1. Lack of Consistency
With data spread across a variety of platforms and domains, it can be more difficult for teams to ensure its quality and consistency. If a domain team drops the ball on quality assurance, their faulty or inaccurate data could impact not only their data-driven decisions, but those of any teams with which they are collaborating. A lack of interoperability can also damage data consistency, as some domains may have more up-to-date resources than others.
2. Siloed Data Resources
Decentralization can also heighten the risk of data silos. These silos occur when teams work entirely independently from one another, without sharing or allowing access to the data kept in their domains. This can create discordance within your organization, and heighten the potential for inaccurate analysis and insights coming from stale or outdated data. To avoid siloing data and data users, teams need to ensure that any technological shift towards a decentralized data architecture is complimented by an organizational enablement and cultural buy-in.
3. Additional Security Risks
Lastly, decentralization does introduce a certain level of risk to your data’s security and privacy. While a centralized architecture could apply rigid controls on all data, decentralized architectures place this responsibility in the hands of various domain teams. This requires a security-first company culture, or else risks slips in protective measures that could expose domains to leak, breach, or misuse.
Types of Decentralized Data Architectures
As decentralized data architectures become more popular in modern data stacks, there are two main forms that they have taken:
The data mesh is an approach to the decentralized data architecture that distributes data across data domains in order to create use case-specific data products, increase usage, and reduce management burdens.
The concept of data mesh is built on four key pillars – domain-centric ownership, data-as-a-product, self-service data platforms, and federated computational governance. By combining these pillars, your team can create an ecosystem that promotes independent data use and management without sacrificing holistic security and privacy.
The data fabric, while similar to the data mesh, has a few distinct differences. Data fabrics are centered around the concept of synthesis, bringing together a range of disparate data, platforms, and tools into a single “fabric” that unites and organizes them coherently.
This includes tools that enable data ingestion, discovery, integration, abstraction, access, querying, security, governance, and orchestration, among others. While it does not foster a system of interoperable domains like the data mesh, the data fabric does create an architecture that unites these distinct platforms and tools within your data stack and ensures that they work systematically towards your data-driven goals.
Enabling a Secure Decentralized Data Architecture
To create a secure, decentralized data architecture for your organization, you need to maintain the essential balance between accessibility and security.
Data needs to be distributed across accessible domains for your teams to use, while being secured by comprehensive access controls that ensure it is proactively protected from risk. A dynamic data security platform helps federate governance effectively across domains, allowing central teams to apply global policies while domain teams create and enforce their own local policies. With global and local policy managed and applied through a central location, you can rest assured that your distributed data is kept safe.
To learn more about securing a distributed architecture, check out our Data Security for Data Mesh Architectures eBook.