The main objective of the implementation of a Data architecture is to standardise methods and protocols, as well as systems for acquiring, storing, managing and sharing data across the enterprise to improve decision-making.
In modern business, most decisions are made in real time, and to facilitate a real-time infrastructure, it is essential to have an efficient and efficient Data Management efficient and user-friendly in real time, data architects lay the foundations or the underlying blueprint for the organisation's Data Management.
More recently, the concept of modern data architecture has emerged from the increasing adoption of the cloud by enterprises, followed by a radical shift towards 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-level data management platforms without compromising data quality (governance issues).
The significant difference with respect to the data architecture The traditional problem lies in the way data is handled in a modern data management platform. On-premise data processing was complicated, time-consuming and resource-intensive. The cloud offered revolutionary solutions to data acquisition, storage, preparation and processing needs.
With a data abstraction layer, Modern Data Architecture makes analysis of enterprise data easy, fast, consistent, efficient and real-time.
What to expect from data architecture in 2022?
The most salient features of the modern Data Architecture are:
- Automated data pipelinesAutomated data integration processes in the cloud ensure that data flows efficiently to all parts of the organisation without compromising Data Quality.
- Data securityData without security mechanisms cannot be considered a business asset. Cloud-based data architectures have strict data security guidelines through controlled data access and authorisation mechanisms. These systems also comply with GDPR and HIPAA data privacy regulations.
- Data scalabilityCloud facilitates robust data management, which can be scaled up or down according to demand in a cost-sensitive manner.
AI or machine learning capabilities: The built-in AI and machine learning capabilities of modern Data Architectures facilitate agile and accurate Data Management processes, from data acquisition to advanced data analysis.
- End-user control of resultsThe cloud empowers users to determine when and what data they need from their Data Management systems.
Trusted data sharing: While data sharing helps to dissolve siloed data, it raises concerns about privacy and data governance. The cloud enables trusted data sharing, which means that everyone is working with the "same version of the truth".
Data architecture trends for 2022
Of the long list of data architecture trends that marked the year 2021, the ones worth mentioning here are the democratisation of access to data, data-ready architecture, and data-ready architecture. IA and the rise of the analytics engineer, the data fabric, the data catalogue, DevOps and of course the cloud. Many of these trends of 2021 will continue to grow, mature and dominate the data architecture 2022.
The eight trends of the data architecture of 2022 to watch and follow are:
- Data FabricThis trend, which continues from 2021, promises standardised and consistent data services across the organisation. According to Gartner, the data fabric "serves as an integrated layered fabric of data and connecting processes", for real-time analytics with data residing in distributed environments. With the maturation of data integration technologies, this is a clear trend possibility in 2022.
- Hybrid and multi-cloudWhile the public cloud is best suited for modern data architectures, persistent data security and governance issues will force enterprises to consider hybrid and multi-cloud options. As the data fabric facilitates rapid data analytics across all types of cloud configurations, the growth of the data fabric also means the growth of the hybrid and multi-cloud.
- Information catalogueContinuing from 2021, this trend promotes architecture built around information catalogues that help data producers and data consumers understand the data they hold. An additional benefit is that information catalogues help both data users and analysts apply "semantics not only to data, but also to reports, analytical models, decisions and other analytical assets," according to Tapan Patel, senior director of Data Management at SAS. Although information catalogues are still maturing, the technology is already receiving positive responses.
- Growth of the Data LakehouseLakehouses: As enterprises continue to struggle with disjointed data silos and proprietary data, the need for a single data architecture becomes more apparent. Lakehouses promise a future of open source, driven by the IA and ML, cloud-friendly and with a single Data Architecture unified.
- Democratisation of data and analyticsA joint study by Google and Harvard Business Review (HRB) reveals that most business leaders recognise the importance of democratising access to data and democratising analytics for business success. With data architectures in the cloudThis trend is set to increase rapidly by 2022.
- Growth of AI/ML capabilities (automation): Cloud Data Architectures will offer technical staff quick access to all the resources they need to work with. On the one hand, the storage, compute and network resources of cloud environments are far superior to those of on-premises data centres; on the other hand, the connectivity of the data infrastructure makes the sharing of resources between on-premises, private, public and hybrid cloud environments for AI/ML operations easy and efficient. Thus, the continued growth of cloud-based data architectures will support the growth of IA/ML or automation.
- Data gridThe data grid framework offers the "democratisation" of data access and data management. In this scenario, data is carefully curated and governed by domain experts. Data Mesh is an innovative technology to remove technical barriers and human problems from data management environments.
- Governance and data qualityTorn between the conflicting forces of innovation and compliance with regulatory barriers, business owners and operators are eager to implement stringent Data Governance measures in their businesses. A recent Teradata study reveals that 77% of business leaders surveyed admit that their companies are more concerned about Data Quality and Data Governance than ever before. This new approach will help companies combat biases in decisions based on the IA.
Thoughts for the future of data architectures
The three main drivers of the future of data infrastructure can be described as the move to the public cloud, more SaaS and the rise of data engineering.
- Switching to public cloud platforms
From 2015 onwards, the shift to the cloud for Data Management services marked the era of open data architecture. Public cloud platforms for Data Management services demanded the separation of storage and compute services, and favoured the integration of services offered by different service providers (Apache solutions) for different services. This trend is gaining more and more ground and shows no signs of slowing down. The end of proprietary data management systems and resources and the growth of the independent data layer in modern data architectures have led to more scalable and efficient solutions.
- Growth of SaaS service layers
This has led to the data architectures open source services are highly successful. SaaS services eliminate the need for downloads, installations, configuration or regular maintenance of software assets by individual companies. Thus, the Data Architecture open, interleaved with SaaS services, provides an easily manageable data management solution with a zero on-premises footprint in terms of cost and maintenance. For example, Dremio Cloud, combined with SaaS services, offers the most scalable, secure, well-governed and multi-engine data processing capabilities for all enterprises with BI fully integrated.
Data engineering solutions offered by data lake solution providers have streamlined the burdensome tasks of data engineering and management teams. For example, Project Nessie, a metastore solution for data lakes and lake houses, facilitates data engineering tasks.