Mastering Modern Data Pipelines | Stackademic
Explore the stages of a modern data pipeline, the stages and their components.
In In today’s data-driven world, organizations rely on well-designed data pipelines to extract actionable insights from raw data. A data pipeline forms the backbone of modern analytics and decision-making, automating the collection, processing and delivery of data from its source to its destination.
But how does a data pipeline work and what are its key stages?
Hello, my name is CyCoderX and today in this article we are going to break down the journey of data through a modern pipeline, detailing each step and the components that make it efficient.
Let’s go!
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The first step in any data pipeline is to collect raw data from its origin. This raw data serves as the basis for all downstream processes.
Aim:
Collect raw data from a variety of sources, including sensors, applications, databases, and user interactions.
Components:
- Data store: Centralized repositories like relational databases or NoSQL stores contain operational data.
- Data flow: Real-time data streams from IoT devices, transactional systems or event logs.
- Application data: Data collected directly from APIs, web services or application logs.
Result:
A diverse pool of raw data is ready to be ingested into the pipeline.
