ETL in data-driven projects

Almost every project has an ETL process in some form, whether it is a set of manual actions or an automated solution.

Data-driven projects often have specific requirements that dictate the design and operation of their data pipelines. These can include data delivery speed, where data must be made available within a defined timeframe, or adherence to a particular data normalization format, ensuring consistency and compatibility across systems.

Other common requirements might involve specific data quality thresholds, data retention policies, or compliance with regulatory standards.

These requirements introduce specific engineering considerations for ETL pipelines.

Balancing the need for near real-time data delivery (low latency) with the capacity to process large volumes of data efficiently (high throughput). This often influences the choice between batch processing and stream processing architectures.

The amount and speed of incoming data dictate the scalability of the pipeline, requiring distributed processing frameworks and optimized storage solutions.

Normalization and quality thresholds, error handling, and potential data quarantine mechanisms within the transformation layer.

The pipeline must be designed to grow with increasing data needs and, ideally, dynamically adjust resources to manage fluctuating loads without manual intervention

Meeting regulatory standards (e.g., GDPR, HIPAA) imposes requirements for data masking, encryption, access controls, and comprehensive auditing throughout the ETL process

The complexity introduced by diverse requirements demands well-documented code, modular design, and comprehensive monitoring tools to quickly identify and resolve issues.