Data is one of the most valuable resources in the modern enterprise, but it only creates value when it can move reliably from source systems into operational and analytical platforms. That is why engineered data pipelines matter. They help organisations ingest, protect, transform, and deliver data at the scale required for reporting, automation, and real-time decision-making.
In this article, we explore practical principles for building data pipelines that can support modern business intelligence workloads. We start with the basics, then move into architecture patterns and tool selection.
What is a Data Pipeline?
A data pipeline is a sequence of processes that extracts data from multiple sources, transforms it into a usable structure, and loads it into a destination such as a data warehouse, database, or analytics platform. The goal is to keep data moving efficiently while preserving quality, consistency, and traceability. This pattern is commonly described as ETL, or Extract, Transform, Load.
In many modern cloud architectures, teams now favour ELT, or Extract, Load, Transform. In that model, raw data is landed first and transformed later inside the target platform. ELT has become popular because it gives teams greater flexibility in modelling, testing, and reusing raw data without rebuilding the ingestion path.
Pipeline Architecture Patterns
- Batch Processing: Ideal for large-volume, periodic data transformations. This is usually performed at certain pre-defined triggers like daily or weekly schedules.
- Stream Processing: Real-time data ingestion for time-sensitive analytics, operational dashboards, and event-driven systems.
- Lambda Architecture: A combined batch and streaming model that balances historical accuracy with near real-time responsiveness.
Choosing the Right Tools and Right Technologies
From Apache Kafka for streaming to DBT for transformations, the modern data stack offers powerful options. The right choice depends on team capability, infrastructure constraints, and the business outcomes you need to improve, whether that means functionality, performance, reliability, or scalability. There are always trade-offs, but a practical tool choice beats a fashionable one. Below is a simple outline of tools we use for different scenarios.
- Batch: Apache Airflow (GUI & CLI are available), Linux BASH with crontab (Classic)
- Stream: Apache Kafka (large data streams), Redis (more of a MQTT broker), Apache ActiveMQ (For smaller use cases focusing on real-time data), SignalR .NET Real-Time events handler (a bulk events handler for real-time data streams over websockets)
Key Takeaways
- Data pipelines are essential for modern business intelligence, enabling efficient data flow from source to destination.
- Architectural patterns like batch, stream, and lambda offer different approaches to data processing based on use case.
- Choosing the right tools is critical for building scalable and maintainable pipelines that meet your specific needs.
- Investing in monitoring, testing, and documentation is crucial for ensuring pipeline reliability and performance.
This blog focuses on entirely open-source or self-hosted solutions for building and managing data pipelines.
Paul Namalomba
Lead Backend @ ComputeMore