The Power of Data Extraction: Pull vs. Push Strategies

Nnaemezue Obi-Eyisi
3 min readOct 26

In the realm of Data Engineering, the methods we use to extract data are just as pivotal as the data itself. Two primary strategies, known as Pull and Push, stand at the forefront of shaping how we collect and process information. Let’s delve into the world of data extraction strategies to understand their merits, drawbacks, and real-world applications.

The Pull Strategy

Flexible and Controlled Data Retrieval

The Pull strategy is akin to having the data world at your fingertips. It allows you to “pull” data from its source when you need it. This approach provides data engineers with the flexibility and control to decide when and how data should be retrieved. It’s an ideal choice when you need to work with large datasets or when the source systems are not capable of actively sending data.

Pros of the Pull Strategy:

  1. Flexibility in Scheduling Extractions: With the Pull strategy, you can set up extraction schedules that align with your needs, ensuring that data is available when you require it.
  2. Ideal for Large Datasets: When working with vast amounts of data, Pull is the go-to strategy, as it allows you to fetch data in manageable chunks.
  3. Minimal Impact on Source Systems: Unlike Push strategies, Pull methods generally have a smaller impact on source systems. This is crucial when you want to avoid overloading your source databases or applications.

Cons of the Pull Strategy:

  1. Not Real-time: If your application requires real-time or near-real-time data, the Pull strategy may not be the best choice, as it relies on scheduled extractions.
  2. Requires Robust Scheduling and Monitoring: Effective use of the Pull strategy necessitates careful scheduling and monitoring to ensure data is collected and updated as needed.

The Push Strategy

Real-time Insights and Event-Driven Data

The Push strategy, on the other hand, is all about data actively being “pushed” from source systems to the target. This approach is renowned for its real-time capabilities and is ideal for applications that demand up-to-the-minute insights, such as…

Nnaemezue Obi-Eyisi

I am passionate about empowering, educating, and encouraging individuals pursuing a career in data engineering. Currently a Senior Data Engineer at Capgemini