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Best Practices for Real-Time Analytics
In this article, I will address the key challenges data engineers may encounter when designing streaming data pipelines. We’ll explore use case scenarios, provide Python code examples, discuss windowed calculations using streaming frameworks, and share best practices related to these topics.
In many applications, having access to real-time and continuously updated data is crucial. Fraud detection, churn prevention and recommendations are the best candidates for streaming. These data pipelines process data from various sources to multiple target destinations in real time, capturing events as they occur and enabling their transformation, enrichment, and analysis.
Streaming data pipeline
In one of my previous articles, I described the most common data pipeline design patterns and when to use them [1].
A data pipeline is a sequence of data processing steps, where each stage’s output becomes the input for the next, creating a logical flow of data.
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