Data Architecture encompasses all the practices and rules of a company around the use of data. Find out all you need to know about it: definition, principles, frameworks, training.
In the past, when a corporate decision-maker wanted to access data, he or she had to call on the IT department. The latter would then create a customized system to deliver the data.
This process was tedious, time-consuming and the result didn’t always meet expectations. As a result, the data could not be fully exploited.
With the emergence of real-time data flows from internal and external sources, this model was no longer viable. To meet these new needs, modern data architectures were born.
What is Data Architecture?
A data architecture is the structure of a company’s data and data management resources. This architecture groups together the models, rules, policies and standards for collecting, storing, integrating and using data within the company. It is therefore a standardization process.
The aim of a Data Architecture is to enable every team in the company to access the data they need, when they need it, and to help them make sense of that data.
It’s about enabling strategic decision-makers to access data freely, without having to ask for help from technicians. Paradoxically, the aim is also to foster collaboration between these two distinct areas of expertise.
This collaboration helps determine what data is needed to drive growth, how to collect it, and how to distribute it. With the rise of the Cloud, enabling greater elasticity and lower costs, modern Data Architecture has been able to develop.
A Data Architecture has many benefits for the enterprise. It enables organizations to prepare themselves strategically to evolve rapidly and take advantage of opportunities linked to emerging technologies.
It also translates business needs into data requirements and IT systems. It therefore simplifies the alignment of the IT department with the business.
Data architecture also makes it possible to manage the distribution of complex data and information throughout the enterprise. The organization can thus gain in agility.
The main principles of Data Architecture
Data Architecture is based on several principles. Firstly, data must be considered as a shared resource. It is necessary to eliminate the various data silos between departments, and to benefit from an overall vision of the company.
And everyone must have access to the data they need. A modern architecture must offer the interfaces required to enable users to exploit data with the right tools for their respective needs.
Security must play an essential role in data architecture, with rules and controls for data access. In addition to security, data quality is paramount, and data cleaning is therefore indispensable.
Finally, data flows must be optimized for agility. The number of data moves must be kept to a minimum, to reduce costs, increase data primevality and foster collaboration.
There are a number of frameworks that can be used as a basis for a company’s data architecture. Many organizations rely on these guides to develop their own architectures.
The DAMA-DMBOK 2 or Data Management Body of Knowledge from DAMA International is a framework specifically designed for Data Management. It provides standard definitions for the various roles and functions involved in data management, and lists the practices to be followed.
The Zachman Framework for Enterprise Architecture was created in the 1980s by IBM’s John Zachman. The data column in this framework includes architecture standards, data models and even databases.
The TOGAF or Open Group Architecture Framework is a comprehensive, high-level methodology for enterprise software development. Phase C” of this framework covers the development of a data architecture and the establishment of a roadmap.
A modern Data Architecture must take into account emerging technologies such as artificial intelligence, automation, the Internet of Things or Blockchain. These innovations can bring many benefits.
The Data Architecture must also be “Cloud-Native”, in order to benefit from all the strong points of Cloud Computing: cost and performance elasticity, availability, end-to-end security…
The data architecture must also include scalable and elastic data pipelines, to support real-time data streaming or micro-batch data bursts.
Thanks to standard API interfaces, data architectures can be integrated with traditional applications. They are optimized for data sharing between systems, geographical locations or organizations.
In addition, modern data architectures enable automated validation, classification, management and governance of data in real time. Finally, they are designed to be decoupled, enabling departments to perform minor tasks independently.
Data Architecture and the Cloud
The emergence of Big Data means new Data Architecture requirements. Companies need a scalable, elastic architecture that can adapt without delay to any new requirements.
Cloud computing technology makes it possible to benefit from this elasticity at an affordable cost. By enabling administrators to increase or reduce capacity, the Cloud has given rise to new applications and use cases.
Examples include on-demand test and development environments, and sandboxes for prototyping and analysis.
Another advantage of the Cloud is its resilience. Most modern data architectures run on large server farms in the Cloud, and providers offer salutary redundancy in the event of failure. Service level agreements also ensure sufficient availability.
The emergence of Big Data means new Data Architecture requirements. Companies need a scalable, elastic architecture that can adapt without delay to any new requirements.
Cloud computing technology makes it possible to benefit from this elasticity at an affordable cost. By enabling administrators to increase or reduce capacity, the Cloud has given rise to new applications and use cases.
Examples include on-demand test and development environments, and sandboxes for prototyping and analysis.
Another advantage of the Cloud is its resilience. Most modern data architectures run on large server farms in the Cloud, and providers offer salutary redundancy in the event of failure. Service level agreements also ensure sufficient availability.
How can I learn about Data Architecture?
Every company needs a structured Data Architecture, and therefore an expert capable of managing it. This may be a Data Architect, but also a Data Engineer.
By following the Data Engineer training course offered by DataScientest, you’ll learn all about the theoretical aspect of data architectures covered in the “Big Data Volume” module alongside tools such as Hadoop, Hive, Pig, Spark and Hbase.
At the end of this professional training course, you’ll have mastered all the tools and techniques of data engineering. You’ll be immediately ready to work as a Data Engineer, and take charge of a company’s Data Architecture.
This course can be taken as a Continuing Education program, or in BootCamp format. All our courses adopt an innovative “blended learning” approach, combining physical and distance learning.
Learners receive a diploma certified by Sorbonne University, and 93% of them find a job immediately. Don’t wait any longer and discover the Data Engineer training course today.
You know all about Data Architecture. Discover the tools of the Data Engineer, like the Python programming language or the GitHub code repository.
