Data pipelines are essential for connecting data across systems and platforms. Here’s a deep dive into how data pipelines are implemented, what they’re used for, and how they’re evolving with genAI.
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For a simplistic view of data processing architectures, we can draw an analogy with the structure and functions of a house. The foundation of the house is the data management platform that provides storage, query, transactions, security, and other fundamental data functions. Throughout the house are various appliances, including microservices, APIs, applications, analytics, generative AI models, and other machine learning models. These are used for searching, analyzing, and publishing data to end users and other services.
Connecting all these systems are pipes, plumbing, and filters—the data processing tools that move data from one system to another. Data processes can be relatively simple in small organizations with few data sources and appliances. Larger businesses often require a wider range …