By IT Brew Staff
less than 3 min read
Definition:
Data pipelines are the channels that send raw, unstructured data through treatment processes so they can be stored in the proper shape for business analytics and reporting.
The pathways, often built by data engineers, are especially important today as business leaders prepare their datasets for AI-powered insights.
A pipeline funnels data from disparate sources, including databases, applications, emails, and real-time streaming info. While a pipeline flows from these starting points, the details from such repos may have inconsistent formats, or may be missing altogether—factors that could lead to unpredictable, inaccurate, and costly responses from a large language model (LLM).
Manual or automated actions (like the cropping of an image, the replacement or deletion of certain words, the adding of metadata, or the enforcement of quality checks) provide a structured, approved version of the data—one that can be more reliably processed by consumers, machine-learning engineers, and LLMs.
A process known as extract, transform, and load (ETL) often facilitates the action in a data pipeline. ETL tools pull raw data from assets like email, databases, and CRM systems; process and filter the data into a desired form; then send the dataset from a staging area to its final destination—say, a data lake or data warehouse.
The programming language SQL offers an important tool for filtering, consolidating, and augmenting data. Scripts also automate data changes, and pipelines can handle data in a streaming form or in specific, scheduled batches.
Following processing and placement in a data lake or warehouse, business pros like data scientists, senior management, or sales teams can run queries and gain insights from the structured, funneled dataset.
