Data is the most important resource in the modern enterprise, it is like the oil to to a motor engine, it makes everything run smoothly. But like the motor oild analogy, you need a funnel which feeds in the oil to the engine, you need the data contained in the engine and you want to prevent los or leakage at all costs. and just like oil , there are so many types that serve many different environements. However, what would make it easiest is having Engineered Data Pipelines that are robust and scalable, especially in the case of real-time large-scale data (big data) from real data sources.
In this article, we will explore the key principles and best practices for building data pipelines that can handle the demands of modern business intelligence applications. First we must understand what a data pipeline is and why it matters.
What is a Data Pipeline?
A data pipeline is a series of processes that extract data from various sources asynchronously (i.e. not all at once), then transform it into a usable format, and load it into a destination system such as a data warehouse, database system or analytics platform. The goal of a data pipeline is to ensure that data flows smoothly and efficiently from its source to its destination, while maintaining data quality and integrity. This is usually called ETL (Extract, Transform, Load).
In recent years, the rise of cloud computing has meant that data is actually just extracted then loaded into the destination system, completely agnostically and without any transformation. This is called ELT (Extract, Load, Transform) and it has become increasingly popular due to the flexibility it offers in terms of data processing and storage. With ELT, data is loaded into the destination system in its raw form, and transformations are performed within the end-use systems themselves. This allows for greater performance, due to removing transformation overhead in the data stores.
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. This is one of the upcmoing fields due to the demand posed by entrprise data platforms with real-time dahboards and visualisations.
- Lambda Architecture: Combining batch and stream for comprehensive coverage, which seems to the best approach for holistic data management.
Choosing the Right Tools and Right Technologies
From Apache Kafka for streaming to DBT for transformations, the modern data stack offers powerful options. The key is choosing tools that match your team's capabilities and your business requirements - not only this, but also choosing options that lead to increases in measurable metrics such as functionality, performance, and scalability. Ofcourse there are trade-offs. A brief outline of the best tools we use for each data transfer mechanism and scenario:
- 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