dataengineeringpipelines
Data engineering pipelines are structured sequences that move data from source systems to target storage where it can be analyzed and used for reporting, ML, and decision making. They automate the capture, processing, and movement of data across domains, handling issues of scale, latency, and reliability. A pipeline typically combines data ingestion, transformation, storage, and consumption layers, and is designed to be repeatable, auditable, and recoverable.
Data sources include databases, log files, APIs, and streaming platforms. Ingestion methods can be batch-oriented or
Orchestration tools schedule and monitor tasks, manage dependencies, retries, and failure handling. Common processing engines include
Key design considerations include idempotence, fault tolerance, scalability, and security. ELT approaches often push transformations to
Typical challenges include schema drift, data skew, backpressure, and managing dependencies across teams. Effective pipelines balance