What Is a Data Pipeline?

A data pipeline is an automated sequence of steps that moves data from one or more sources to a destination — typically a database, data warehouse, or data lake — where it can be queried and analyzed. Without a reliable pipeline, organizations end up with manual data exports, inconsistent reports, and engineering bottlenecks.

This guide walks through building a basic-but-production-minded pipeline from scratch.

Step 1: Define Your Requirements

Before writing a single line of code, answer these questions:

  • What data sources are you ingesting? APIs, databases, flat files, event streams?
  • How often should the pipeline run? Real-time, hourly, daily batch?
  • What is the destination? PostgreSQL, Snowflake, BigQuery, S3?
  • Who consumes the output? Analysts, dashboards, ML models?
  • What are the SLA requirements? How fresh does the data need to be?

Clear requirements prevent over-engineering and scope creep. A daily batch pipeline to a PostgreSQL database is often all you need to start.

Step 2: Choose Your Architecture

The two dominant patterns are:

  • ELT (Extract, Load, Transform): Load raw data first, transform inside the warehouse. Best for cloud warehouses with abundant compute (Snowflake, BigQuery). dbt is typically used for the transform step.
  • ETL (Extract, Transform, Load): Transform before loading. Better when you need to reduce data volume before storing or when your destination has compute limitations.

For most modern setups, ELT is the preferred approach because it preserves raw data and makes re-transformation easy.

Step 3: Build the Extraction Layer

The extraction step pulls data from your source systems. Key considerations:

  • Use incremental extraction where possible (e.g., only pull records updated since the last run) to avoid full reloads
  • Handle pagination for REST APIs gracefully
  • Store raw extracted data in an intermediate landing zone (e.g., S3 bucket or staging schema) before processing
  • Log extraction metadata: row counts, timestamps, source system version

Step 4: Handle Transformations

Transformations clean, enrich, and reshape raw data into a usable form. Common transformations include:

  1. Parsing and standardizing date/time formats
  2. Deduplication — removing duplicate records by primary key
  3. Handling nulls — deciding whether to fill, drop, or flag missing values
  4. Joining with reference data (e.g., mapping product IDs to names)
  5. Aggregating to the required granularity (daily totals, user-level summaries)

Step 5: Load to Your Destination

Load strategies depend on your use case:

  • Full refresh: Truncate and reload the table. Simple but expensive for large tables.
  • Append-only: Add new rows. Works well for event and log data.
  • Upsert (merge): Insert new records, update changed ones. Best for slowly changing dimensions and entity tables.

Step 6: Schedule and Orchestrate

A pipeline that only runs manually isn't a pipeline. Use an orchestration tool to schedule and monitor runs:

  • Apache Airflow: Powerful and widely adopted. Best for complex dependency graphs.
  • Prefect / Dagster: Modern alternatives with better developer experience.
  • cron + shell scripts: Perfectly valid for simple, low-complexity pipelines.

Step 7: Add Monitoring and Alerting

Silent failures are the enemy of reliable data. At minimum, implement:

  • Row count checks (if a table drops from 1M to 0 rows, alert immediately)
  • Freshness checks (data older than expected triggers a warning)
  • Schema change detection (new or dropped columns in source systems)
  • Pipeline failure notifications via email or Slack

Conclusion

A well-built data pipeline is repeatable, observable, and incrementally improvable. Start simple: get data flowing reliably, then add robustness layer by layer. The best pipeline architecture is the one your team can maintain and trust — not the most technically impressive one.