Data Architecture must not be new to a lot of us. But, with frequent changes in technology, the principles continue to evolve as well.
Data architecture is the structure to collect, store, integrate, use and secure logical and physical data assets and defining models, policies, rules and standard to govern it.
Today we have exabytes of structured and unstructured data from different sources like transactions, machines, social media, government, IoT devices etc. These are available in different formats, like text, audio, video, images. It becomes really important to understand and bring it together for business purposes. Modern data architecture has to take into account all the sources, formats and business needs to define the principle.
Guiding principles for modern data architecture are sharing of the data, access control, common language, security, curation and optimization. Each of these in itself is a separate topic, but together they make a good foundation of how organizations need to look at data when thinking or building a data architecture.
Talking of the key components of modern data architecture, we have,
- Building scalable data pipe lines to collect, store and distribute data.
- Cloud native approach, considering the organization needs. It could be public, private or hybrid.
- Cloud computing to enrich, transform, calculate, generate recommendation and analyze data.
- Data streaming for real-time or near real-time needs.
- Data analytics capabilities to relate and visualize.
- Ml and AI for all smart and automation needs.
With every quintillion bytes of data being produced every day, the modern data architecture has to be scalable, reliable and maintainable with seamless integration, real-time enablement, completely decoupled and fully secure.
