Optimizing Pipelines for Analytical or Transactional Purposes
In this section, you will learn about efficient optimization, which involves applying best practices and advanced techniques tailored for analytical and transactional workloads. The following are some key points to consider:
- Resource scaling: You can allocate resources based on the specific workload. For analytical tasks consider using services, such as Azure Databricks, for efficient data processing.
- Incremental loading: Implement incremental loading strategies to minimize data transfer and improve pipeline efficiency. This approach ensures that only changed or new data is processed, reducing the overall processing time.
- Exception handling: Configure robust exception-handling mechanisms to manage errors during pipeline execution. This includes handling connectivity issues, data format mismatches, and other unexpected scenarios.
- Monitoring and optimization: Continuously monitor pipeline performance using...
