Checkpointing in Ensuring Data Integrity During Streaming Operations

Imagine a social media platform processing millions of real-time posts, likes, and shares. Each interaction generates a stream of data that needs to be processed, analyzed, and stored. A small glitch in this processing pipeline can have significant consequences, skewing analytics, impacting user experience, and even affecting advertising revenue. Checkpointing acts as a safety net, ensuring that even if something goes wrong, we can recover without losing valuable data or insights.

Checkpointing is a fundamental technique in streaming data processing. It periodically saves the application's state, creating a recovery snapshot. This is crucial for real-time applications where data loss is unacceptable.

According to IDC research, A business can lose an average of $5,600 per minute during downtime, highlighting the crucial need to implement strong data protection strategies to minimize disruptions and financial losses.

Checkpointing saves the application's processing state and data offsets, enabling recovery from the precise point of interruption. But while checkpointing prevents data loss, it doesn't inherently guarantee data quality.

How Can You Add a Quality Layer to Checkpointing?

Adding data quality checks to streaming pipelines is crucial. While traditional methods involved manual validation and custom scripting, several platforms and tools now automate this process.

Some streaming platforms, like Apache Flink and Spark Structured Streaming, offer basic built-in validation. Others rely on external tools like Apache Griffin or Great Expectations, which provide more advanced features but can add architectural complexity. Commercial cloud solutions offer managed services but can be costly.

But Datachecks goes a step further. Datachecks is an open source platform, provides a streamlined approach to enhance checkpointing by validating data before it's saved which prevents cascading errors and ensuring data integrity without storing data.

Consider a scenario, during a sale an e-commerce platform's streaming pipeline processes millions of orders in real time. This pipeline calculates discounts and updates inventory, and its reliability is crucial. Checkpointing ensures recovery from outages, but what if a bug in the discount logic is introduced just before the sale?

Without a data quality layer, this bug would lead to incorrect order confirmations, inaccurate inventory, and messy financial records. A pipeline crash would save this erroneous state in the checkpoint, perpetuating the problem upon recovery. This could result in significant financial losses and customer dissatisfaction.

However, with Datachecks, a data quality check would catch the faulty discount calculation before checkpointing. For example:

checks:
 - name: check_discount_percentage
   table: orders
   expression: discount_percentage <= 1.00 # Discount should not exceed 100%
   type: row_condition
   description: Ensure discount percentage is reasonable.


This check ensures the discount_percentage doesn't exceed 100%. Datachecks supports metrics like Reliability, Numeric Distribution, Uniqueness, Completeness, and Validity.

Datachecks proactively validates data, halting processing and alerting the team upon detecting errors. This prevents flawed data from reaching the checkpoint, ensuring recovery from a known good state and protecting the business.

Conclusion

Think of checkpointing as building a fortress to protect your data. It provides strong walls against data loss, ensuring you can recover from unexpected attacks (system failures). But those walls are only effective if the fortress is also defended from within.  Data quality checks, act as vigilant guards preventing  data corruption from undermining the entire operation.

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