How can you use the Observer pattern to notify objects of changes in Data Engineering?

1 Observer pattern

The Observer pattern is a behavioral design pattern that defines a one-to-many relationship between a subject and multiple observers. The subject is an object that holds some state or data, and the observers are objects that depend on or are interested in the subject's state or data. The Observer pattern allows the subject to notify the observers of any changes in its state or data, without coupling them tightly or knowing their details.

3 Examples of Observer pattern

The Observer pattern can be applied to various data engineering scenarios, such as data streaming, data warehouse, and data quality. For example, in data streaming, the subject can be a data source that produces data streams, such as Kafka, and the observers can be data consumers that process or analyze the data streams, such as Spark or Flink. This pattern enables the data consumers to receive the data streams as soon as they are available, without waiting or blocking. Similarly, when applied to a data warehouse, the subject can be a data warehouse that stores historical and aggregated data, such as Redshift or BigQuery, and the observers can be data analysts or business users that query the data warehouse. This pattern allows the users to get notified of any changes in the data warehouse without refreshing or reloading it manually. Finally, for data quality, the subject can be a service that monitors and validates the pipelines and systems, such as Great Expectations or Deequ. The Observer pattern then enables engineers or scientists to get alerted of any issues without checking or logging frequently.

4 Implementation of Observer pattern

The implementation of the Observer pattern can vary depending on the programming language, framework, or tool that you use. However, the general steps involve defining an interface or abstract class for both the subject and the observer, implementing a concrete class for the subject that maintains a list of observers and a state or data attribute, and implementing one or more concrete classes for the observer that define how to update their state or behavior. Additionally, instances of the subject and observer must be created and the observers must be attached to the subject using its attach method. Finally, when the subject's state or data changes, its notify method should be called which will loop through the list of observers and call their update methods with the subject's state or data as an argument. As an example, see below for a Python example of this pattern.

5 Challenges of Observer pattern

The Observer pattern can present certain challenges, such as performance overhead if the subject has too many observers or memory leaks if the observers are not detached properly. In addition, unexpected or inconsistent behaviors can arise if the observers have dependencies or side effects that are not handled well. Therefore, it is important to use the Observer pattern with caution and follow best practices for data engineering. These include using appropriate data structures and formats to store and transmit the subject's state, using communication protocols and mechanisms to notify the observers, employing synchronization and concurrency techniques to ensure thread safety and atomicity, utilizing error handling and logging techniques to catch and report exceptions, and implementing testing and debugging techniques to verify and validate functionality and performance.