Key Requirements for Modern Data Architectures
Find out what a modern data architecture looks like and improve yours.
In this age of big data, organisations are finding their data to be a source of competitive advantage. However, the needs of your organisation and the data landscape evolve over time. It is essential to regularly review your data architecture to keep pace with changing requirements and technological advancements. Research projects and institutions can consider the following characteristics of a modern data architecture to maximise their data architecture capabilities:
- scalable and reusable data pipelines that combine intelligent workflows, data science, data analytics and real-time integration in a single framework
- seamless data integration from various sources – implement extract, load and transform (ELT) processes or consider modern approaches like data pipelines and event-driven architectures for efficient data integration
- data flows automation to reduce manual data processing and analysis – technologies such as data integration and machine learning can help
- clear data flow design principles that take into account both batch and real-time processing requirements
- data analytics and artificial intelligence (AI) to create a modern data architecture that can manage, process and analyse massive volumes of data – Charlie Rivera outlined 6 essential phases for modernising current data architecture with analytics and AI
- flexible to support on-premise and cloud data needs
- real-time data validation, classification, management and governance
- optimised to balance cost and simplicity
- appropriate data storage technologies based on the nature of your data and access patterns – consider options like data lakehouses to meet scalability needs
- novel technology solutions like the data lakehouse to handle unstructured or raw data.
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