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An Overview of Key Components of a Data Pipeline

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Dive into how a data pipeline helps process enormous amounts of data, key components, various architecture options, and best practices for maximum benefits.
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With the consistent growth of data-driven applications, the complexities of consolidating data from multiple sources for streamlined decision making is often considered a key challenge. While data forms the foundation of analytics and operational efficiency, processing big data requires holistic data-driven strategies for real-time ingestion and processing. To help with this, data pipelines enable organizations to aggregate and analyze huge datasets by defining a series of activities that convert raw data into actionable insights. In this article, we dive into how a data pipeline helps process enormous amounts of data, key components, various architecture options, and best practices to achieve the maximum benefits. A data pipeline is the collection of tasks, tools, and techniques used to process raw data. Pipelines consist of multiple interrelated steps connected in series, which enable the movement of data from its origin to the destination for storage and analysis. Once data is ingested, it is taken through each of these steps, where the output of one step acts as the input for the subsequent step. In the modern technology landscape, big data applications rely on a microservice-based model, which allows monolithic workloads to be broken down into modular components with smaller codebases. This encourages data flow across many systems, with data generated by one service being an input for one or more services (applications). An efficiently designed data pipeline helps manage the variety, volume, and velocity of data in these applications. Primary advantages of implementing an optimally designed data pipeline include:
When building infrastructure for data processing applications, a data pipeline enables the use of replicable patterns — individual pipes that can be repurposed and reused for new data flows, helping to scale IT infrastructure incrementally. Repeatable patterns also blend security into the architecture from the ground up, enabling the enforcement of reusable security best practices as the application grows. Data pipelines help extend a shared understanding of how data flows through the system along with the visibility of the tools and techniques used. Data engineers can also set up telemetry for data flows within the pipeline, enabling continuous monitoring of processing operations. With a shared understanding of data processing operations, data teams can efficiently plan for new data sources and flows, reducing the time and cost for integrating newer streams.

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