Data Pipeline Architecture

The data pipeline architecture conceptualizes the series of processes and transformations a dataset goes through from collection to serving.

Architecturally, it is the integration of tools and technologies that link various data sources, processing engines, storage, analytics tools, and applications to provide reliable, valuable business insights.

1. Collection: As the first step, relevant data is collected from various sources, such as remote devices, applications, and business systems, and made available via API.

2. Ingestion: Here, data is gathered and pumped into various inlet points for transportation to the storage or processing layer.

3. Preparation: It involves manipulating data to make it ready for analysis.

4. Consumption: Prepared data is moved to production systems for computing and querying.

5. Data quality check: It checks the statistical distribution, anomalies, outliers, or any other tests required at each fragment of the data pipeline.

6. Cataloging and search: It provides context for different data assets.

7. Governance: Once collected, enterprises need to set up the discipline to organize data at a scale called data governance.

8. Automation: Data pipeline automation handles error detection, monitoring, status reporting, etc., by employing automation processes either continuously or on a scheduled basis.

Check out this comprehensive guide on data pipelines, their types, components, tools, use cases, and architecture with examples