The primary purpose of implementing a Data Architecture is to standardize the methods and  protocols, as well as the systems for acquiring, storing, managing, and sharing data across the enterprise for improved decision-making.

In modern businesses, most decisions are made in real time, and to facilitate an efficient and real-time friendly Data Management infrastructure, data architects lay the foundation or underlying blueprint for organizational Data Management.

More recently, the concept of the modern Data Architecture has stemmed from the growing adoption of the cloud by businesses, followed by a radical shift to cloud platforms for all or most Data Management tasks. Only cloud platforms, with their varied solutions, can offer the speed, scalability, and ease of use of enterprise-grade Data Management platforms without compromising the quality of data (governance issues).

The significant departure from traditional Data Architecture lies in the way data is handled in a modern Data Management platform. On-premise data processing was complicated, time-consuming, and resource heavy. The cloud offered revolutionary solutions to data acquisition, storage, preparation, and processing needs.

With a data abstraction layer, the modern Data Architecture makes business data analysis easy, fast, consistent, efficient, and real-time friendly.

Data Architecture Trends: What to Expect in 2022

The salient features of modern Data Architecture are:

• Automated Data Pipelines: Automated data integration processes on the cloud ensure that data flows efficiently to all parts of the organization without compromising Data Quality.
• Data Security: Data without security mechanisms in place cannot be considered a business asset. Cloud-basedData Architectures have stringent data security guidelines in place through controlled data access and authorization mechanisms. These systems are also compliant with GDPR and HIPAA data-privacy regulations.
• Scalability of Data: Cloud facilitates robust Data Management, which can be scaled up or down on demand in a cost-sensitive manner.
• AI or Machine Learning Capabilities: The in-built AI and machine learning capabilities of modern Data Architectures facilitate agile and accurate Data Management processes, from data acquisition to advanced data analytics.
• End User Control of Outcomes: The cloud empowers the users to determine when and what data they need from their Data Management systems.
• Trusted Data Sharing: While data sharing helps dissolve siloed data, it raises concerns about data privacy and governance. The cloud enables trusted data sharing, which means that everyone works with the “same version of truth.”

This Gartner article describes how Data Architecture plays a role in the overall enterprise architecture of a data-driven organization. A DBTA article about building a modern Data Architecture from the ground up.

2022 Data Architecture Trends to Watch

From the long list of Data Architecture trends that shaped 2021, the ones worth mentioning here are democratization of data access, AI-ready architecture, and the rise of the analytics engineer, data fabric, data catalog, DevOps, and of course, the cloud. Many of these 2021 trends will continue to rise, mature, and dominate the 2022 Data Architecture landscape.

Eight 2022 Data Architecture trends to watch and follow are:

• Data Fabric: This trend, continued from 2021, promises standardized and consistent data services throughout the organization. According to Gartner, data fabric “serves as an integrated layer fabric of data and connecting processes,” for real-time analytics with data residing across distributed environments. With data integration technologies maturing, this is a distinct trend possibility in 2022.
• Hybrid and Multi-cloud: Although public cloud is most suited for modern Data Architectures, nagging data security and governance issues will force businesses to consider hybrid and multi-cloud options. As data fabric facilitates fast data analysis in all types of cloud configurations, the growth of data fabric means growth of hybrid and multi-cloud too.
• Information Catalog: Continuing from 2021, this trend promotes architecture built around information catalogs that help data producers and data consumers understand the data available to them. An additional benefit is that information catalogs help both data users and analysts apply “semantics to not just data but also [to] reports, analytic models, decisions, and other analytic assets,” according to Tapan Patel, senior manager for Data Management at SAS. As information catalogs are still maturing, this technology is already receiving positive responses.
• Growth of Data Lakehouse: As enterprises continue to battle with unconnected data silos and proprietary data, the need for a single Data Architecture becomes more apparent.  Lakehouses promise a future of open source, AI- and ML-powered, cloud-friendly, unified single Data Architecture.
• Democratization of Data and Analytics: A joint study by Google and Harvard business Review (HRB) reveals that most business leaders acknowledge the importance of democratized data access and democratized analytics for the success of a business. With cloud Data Architectures, this trend will rise rapidly in 2022.
• Growth of AI/ML Capabilities (Automation): Cloud-based Data Architectures will offer the technical staff quick access to all resources they need to work with. On one hand, the storage, computing, and network resources of cloud environments are vastly superior to those of on-premise data centers; on the other hand, data-infrastructure connectivity makes resource sharing across on-premise, private, public, and hybrid cloud environments for AI/ML operations easy and efficient. Thus, the continuing growth of cloud-based Data Architectures will favor the growth of AI/ML features or automation.
• Data Mesh: The data mesh framework offers “democratization” of data access and Data Management. In this scenario, data is carefully curated and governed by domain experts. Data mesh is a groundbreaking technology for removing technical barriers as well as human issues from Data Management environments.
• Data Governance and Quality: Torn between contradicting forces of innovation and complying with regulatory barriers, business owners and operators are eager to implement stringent Data Governance measures in their businesses. A recent study by Teradata reveals that 77%  of polled business leaders admit that their enterprises are more concerned about Data Quality and Governance than ever before. This new approach will help businesses combat biases in AI-enabled decisions.

Thoughts for the Future of Data Architectures

The three major drivers of the future of data infrastructure can be described as moving to public cloud, more SaaS, and increased data engineering.

Shift to Public Cloud Platforms

From 2015 onward, a shift to cloud for Data Management services signaled the era of open Data Architecture. The public cloud platforms for Data Management services necessitated the separation of storage and computing services, and favored integrating services offered by different service providers (Apache solutions) for different services. This trend is increasingly gaining traction and shows no slowing down. End of proprietary Data Management resources and systems and the growth of the stand-alone data layer in modern Data Architectures have led to more scalable and efficient solutions.

Growth of SaaS Service Layers

This has made open Data Architectures highly successful. SaaS services remove the need for downloads, installs, configuration, or regular maintenance of software assets by individual businesses. Thus open Data Architecture, interspersed with SaaS services, facilitates an easily manageable, Data Management solution with zero on-premise footprint in terms of cost and upkeep. For example, Dremio Cloud, combined with SaaS services offers the most scalable, secure, well governed, multi-engine data processing capabilities for all businesses with fully integrated BI solutions.

Data engineering solutions offeredby data lake solution vendors have streamlined the heavy-lifting tasks of the data engineering and Data Management teams. For example, Project Nessie, a “metastore” solution for data lakes and lakehouses, eases data engineering tasks.

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