What Are the Characteristics of a Modern Data Platform?
Modern data platforms can gather information from a variety of sources into a centralized medium, such as a data lake. The data is then interpreted for business intelligence applications.
According to Raihan, a modern data platform is composed of three key components:
- An analytics layer, which Raihan says “enables easy querying of data so practitioners who are subject-matter experts can ask relevant questions in a business context”
- A search component, which “enables federation of data across multiple data sources, allowing for a deeper level of analytics in the layer above”
- A storage layer, which allows data to be organized by age and business value
All of this data is ingested from a variety of tools, such as Internet of Things devices. This data may have existed previously in separate departments, but such silos are now less desirable, according to ESG’s Catanzano.
“People are realizing that data needs to be real-time in a lot of cases to get to decision-making,” he says. “So, streaming is really picking up just to get to quality data as quickly as possible. And there are some great advantages once you get there.”
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Data Platform Building Blocks: Agile, DevOps and DataOps
Modern and enterprise data platforms are built on two key methodologies of modern software development: DevOps and agile development. Though similar in nature, the process of agile development focuses on quick iteration while DevOps brings development processes to operations.
At the center of this discussion is the concept of DataOps, an operations-driven approach to data. Modern data platforms fit neatly into the DataOps concept because, as Gartner explains, it allows for collaborative data management and automated flows between data manager and data consumers.
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