Data Pipelines: Ingestion, Processing, and Orchestration
Data pipelines move data from many sources to a place where people can use it. They are built in layers: ingestion brings data in, processing cleans or transforms it, and orchestration coordinates tasks and timing. Together they turn raw data into reliable information.
Ingestion
Ingestion is the entry door. It handles sources such as databases, logs, files, sensors, and APIs. You can pull data on a schedule (batch) or receive it as it changes (streaming). A good practice is to agree on a data format and a schema early, and to keep a simple, testable contract. Techniques like incremental loads, change data capture (CDC), and backfill plans help keep data fresh and consistent. Think about retry logic and idempotence to avoid duplicates. Be ready for schema drift and governance rules that may adjust fields over time.
Processing
Processing means cleaning, transforming, and enriching data. Common steps are standardizing names, filtering bad records, joining related data, and computing aggregates. Validation checks catch missing values and outliers. Enrichment adds context, such as geo data or customer segments. Keep transformations modular: small steps are easier to test and reuse. Store intermediate results when helpful to debug issues.
Orchestration
Orchestration coordinates when tasks run and how they depend on each other. It handles retries, failure alerts, and parallel execution where possible. Clear lineage helps teams track how data moves through the system. Use metadata, versioned configurations, and access controls to protect sensitive data. Popular tools exist, but the best choice fits team workflow: simple dashboards, reliable scheduling, and good observability.
Examples: A typical flow could be: an ingestion job runs at night, a processing job cleans and aggregates the data, and an end load writes to the data warehouse. If the API rate limit drops, the orchestrator retries or pauses and sends a quick alert—avoiding data gaps.
Design tips: start with a small, repeatable pipeline, then add checks, tests, and monitoring. Treat data contracts as living guidelines and keep a clear log of changes.
Key Takeaways
- Data pipelines have three core parts: ingestion, processing, and orchestration.
- Plan for quality, observability, and clear data contracts.
- Start small, test often, and iterate with feedback from users.