Error Handling Strategies: Industry Scenarios

Imagine you're building a data integration pipeline that extracts, transforms, and loads customer data into a database. Occasionally, the source data file contains invalid records with missing or incorrect information. Without error handling, a single bad record could halt the entire pipeline.

For example, while processing a batch of 500 documents, you observe that the 100th document is corrupted, leading to a processing error. Despite this issue, you may wish to proceed with the execution of the remaining 499 valid documents and identify the problematic document. You would use an error pipeline to capture and log the problematic records, allowing the main pipeline to continue processing valid data. This ensures that the pipeline continues to function, even if there are a couple of erroneous documents in the source data. The errors can be reviewed by examining the pipeline execution statistics. These statistics provide insights into the number of documents passing through the output and error views, respectively. Moreover, companies prefer a standardized approach to error handling, and error pipelines facilitate the uniform processing of errors across all pipelines.

Following are the various industry scenarios where you can use error handling to process different types of data:

Data Validation and Logging:
Scenario: You have a pipeline that processes order data from an e-commerce website. The data contains fields like product ID, quantity, and price. Occasionally, orders come in with missing or invalid data.
Error Handling: In your main pipeline, you use a data validation Snap to check the incoming orders. If a validation error is detected, you route that specific order to an error pipeline. In the error pipeline, you log details about the problematic order, including the specific validation error. Meanwhile, the main pipeline continues processing valid orders.
API Integration with Retry:
Scenario: Your organization relies on external APIs to fetch data. Occasionally, these APIs experience transient errors or rate limiting, which can disrupt the data retrieval process.
Error Handling: In your main pipeline, when an API request fails, you route the error to an error pipeline. In the error pipeline, you implement logic to retry the API request a few times with a delay between attempts. If all retries fail, you log the error and take appropriate action (e.g., sending a notification). Meanwhile, the main pipeline continues with other data sources.
Database Insertion and Alerting:
Scenario: You have a pipeline that inserts data into a database. Sometimes, the database server experiences connection issues or unique constraint violations.
Error Handling: In your main pipeline, after the database insertion Snap, you configure error handling to route database-related errors to an error pipeline. In the error pipeline, you log the error details and send an alert to the database administrator. The main pipeline continues inserting other records into the database.
Data Transformation with Data Cleansing:
Scenario: Your pipeline performs data transformation, including cleansing of text data. Occasionally, the data source contains non-standard characters that cause transformation errors.
Error Handling: In your main pipeline, you use a data transformation Snap that may generate errors when it encounters non-standard characters. You route these errors to an error pipeline where you implement custom logic to clean the data or flag problematic records. The main pipeline continues processing the rest of the data.
Data Ingestion from Multiple Sources:
Scenario: Your pipeline ingests data from multiple sources, and the format of data may vary. Some sources might send data in JSON, while others send CSV.
Error Handling: In your main pipeline, you use a Snap to detect the source type and handle each source differently. If a source-specific processing error occurs (e.g., invalid JSON), you route the error to an error pipeline dedicated to handling that particular source type. This allows you to manage different types of errors gracefully while the main pipeline continues working with other sources.