What are the best practices for handling data aggregation errors in batch processing?

Other than this, pick a random date and perform validation on data to confirm. Data type validation Range validation Format validation Unique key validation Buisness rule validation It's better to validate before ingestion pipeline is set up to ensure we don't struggle later

Always check for corner cases in data types and gracefully handle them. A simple but important think to keep in mind is your logging needs to be comprehensive especially for batch jobs. I have written a batch job to transfer data from s3 to oracle where I needed to equivalently convert the values to a new data type. Here it’s really important to understand the limitations of your databases. Ex oracle sql db with limited flexibility whereas my data from s3 was an object with no restrictions, in this case it’s important to try all possible edge cases of possible values in your batch job from both ends of the pipeline to ensure the job doesn’t fail.

Handling data aggregation errors in batch processing requires careful planning and robust practices. Firstly, validate input data thoroughly before processing to catch errors early. Implement error handling mechanisms to gracefully handle exceptions during aggregation, logging detailed error information for debugging. Consider data reconciliation processes to ensure data accuracy post-processing. Use checkpoints and transactional operations for data integrity, allowing you to recover from failures. Additionally, prioritize monitoring and alerting to promptly identify and address aggregation issues. Finally, document error handling procedures and continually refine them to improve the reliability of your batch processing workflows.

The key in any aggregation strategy is to have rules around uniqueness - what constitutes the "primary key" in the dataset you are working with? This can be simple if it's just a single column (e.g. User ID), however it can quickly become a lot more complex with multiple dimensions, data types, data collection mechanisms and layers of business logic that the data might be contextualized against. So it's important for your data dictionary to have clear, detailed, consistent rules around how data should be deduplicated - BEFORE that logic is implemented into your data pipelines to handle aggregation. And remember, the data can always change - hence your documentation also needs to stay up to date.

For validation, we can follow RAML definition where you define the JSON structure for a type of data and make sure that the data producer has this check while sending the data to downstream users. This becomes more important when we have multiple sources of data and it becomes hard for downstream job to take care of all. So it is always better to take care of data sanity by producer.

IMHO idempotency and data quality metrics are the best approach to guarantee good data to be used. Using data quality metrics such as completeness, nullable columns, guarantee data types correctly and so on. I don't recommend wast time on unit test, use your time on data quality, using library such as Great Expectations, PyDequee.

It is certainly a good approach. However, it is imperative to validate this technique from a data business semantic perspective. The risk is to adopt idempotent operation and create data which is not compatible with business logic. Just a remind that business logic should prevail.

Data ingestion: When ingesting data from external sources, an idempotent operation ensures that data is not duplicated if the ingestion process is run multiple times. This can be achieved by checking for existing records before inserting new data or using upsert (update or insert) operations. Data replication: When replicating data across different systems or databases, idempotent operations guarantee that duplicate copies of the data are not created. This can be achieved by tracking the replication status and applying updates only when necessary. Database updates: When updating records in a database, an idempotent operation ensures that applying the update multiple times does not result in duplicate or inconsistent data.

There is a pretty much invisible way to break idempotency when you use Spark/PySpark while reading from a data source that might change while the batch process is running: If you use some evaluation function that extracts data properties to be used as parameters later in the load, always apply this function AFTER writing your Spark/PySpark dataframe in storage. Then, continue your pipeline from this storage checkpoint. This way you will avoid Spark's lazy execution from providing one result to your evaluation function, and then, since the data source got updated in the meantime, giving something else to the next action that triggers the physical plan from the beginning.

Using Scd type 2 would help in bringing idempotancy to system Also don't update cumulative tables ,backfill starting from ast data sequentially

To address data aggregation errors in batch processing, it's crucial to employ a multi-tiered data quality framework. This includes: 1. Pre-Processing Checks: Implementing a pre-processing step to verify data schema and integrity before aggregation. 2. Real-Time Monitoring: Utilizing real-time dashboards to monitor data flow and detect anomalies at the moment they occur. 3. Post-Aggregation Audits: Conducting post-aggregation audits to check for data consistency and completeness, ensuring no aggregation logic has been compromised. This holistic approach minimizes the introduction of errors during batch processing and maintains the integrity of the aggregated data.

I agree, in addition while having a foundational error handling framework is a must there also should be an ABC Framework (Audit, Balance and Control) that works with Error handling rules to record what happened, where it happened and how it happened. ABC frameworks ensure longevity of solution and less chance of repeating similar errors.

One must work with support team and business teams to understand who needs to be informed when the validation failed. Also handling of validation failure (either to reject whole lot or reject rows or ingest everything with warning ) should also be finalized

Effective handling of data aggregation errors in batch processing involves setting up a robust error handling and logging system. This system should capture and log detailed information about errors, including their nature, affected data, and when they occur. Additionally, it should trigger immediate error notifications to relevant stakeholders for prompt action. This systematic approach ensures accurate troubleshooting, analysis, and the identification of recurring issues, ultimately leading to more reliable data processing.

1. Documentation, Documentation!! - Simple and Structured is worth in gold?? 2. Defining the severity and automating the flows ?? 3. Balancing potential retries and resources costing ?? 4. Clear definition of Point of Contact and handovers for type of issues post-handover to support?? 5. Data deletion of logging table to keep it light

The data quality is crucial for the analysis, data science ML and AI. Therefore, the data quality should be tested at several stages. Beofre the ingest to your system (e.g. with schema validations). During the processing and aggregation of your data (e.g. with smoke tests). Also the data itself needs to be tested for the data quality. After certain events the data can drift significatly which is not due to pipeline errors. However, not only the technical settings is important of monitring the data quality. Its crusial to have setup clean processes in your data pipeline. Who will be informed? How will be informed? What are the actions?

We must asses how critical the data is also. Also we must communicate the Turn around time if there is an error there have to be signed off before the pipeline is set uo. If thr data is critical there need to be more eyes watching the data and exception

Aggregating data is an operation that is very well defined in advance, therefore it serves the opportunity to write robust unit tests that cover edge cases which may or may not be covered with real data. This is a prognostic approach. Automated reconciliations are also a tried and tested method to ensure aggregations are working correctly. This would serve as a diagnostic approach.

with modern trends of AI and machine learning, we can Employ machine learning models for error prediction and anomaly detection. Continuously monitor the performance of these models and retrain them periodically to adapt to changing data patterns

Define data quality metrics: Start by determining the key metrics that measure data quality. These metrics could include completeness, accuracy, consistency, validity, timeliness, uniqueness, & integrity. Clearly define the thresholds or benchmarks for each metric. Implement data lineage & metadata management: Maintain a record of the data's journey through the pipeline & track its transformations. This helps in understanding the origin & transformations applied to the data, aiding in data quality analysis & issue resolution. Implement data quality checks: Develop automated checks to assess the quality of the data. These checks can be performed at various stages of the data pipeline, like during data ingestion, transformation, & loading.

Optimize query performance: Evaluate the queries used for aggregation. Check for opportunities to improve performance by optimizing SQL queries, leveraging indexes, using appropriate join types, & reducing unnecessary calculations or data scans. Consider data partitioning: Partitioning data can improve the performance of data aggregation operations, especially for large datasets. Evaluate if partitioning based on specific criteria, such as time or geographical location, can be applied to the data. Implement monitoring and alerts: Set up monitoring systems to track the performance & quality of aggregation process. Establish alerts that notify you of any failures, anomalies in the process, enabling you to take immediate action,if required.

Ensure you use the right tool based on the velocity, veracity and volume of data. Sometimes we need efficiency in handling these aggregation operations

Always keep in mind that at some point your pipeline will most likely fail! Each time this happens, see that as a chance to improve the pipeline and yourself. Don't get worried too much about this (unless you keep making the same mistakes again and again), even the biggest companies face short outages every once in a while.

Still best way miles ahead of everything is make your Data Engineers and Analysts to know the bigger picture????. Helping folks to develop 'Data Sense' to identify and address the problems efficiently??? could save lot of bucks ??.

Try to use common codes for regular transformations lower the chance of errors and facilitating the debugging processes. Bug in the common codes can be detected by multiple users. Also, the bugs needed to be fixed in one place instead of multiple codes if happened.

In my experience, it is important to establish robust data validation, error handling and monitoring processes to ensure that data aggregation pipelines are reliable, consistent and performant.

One thing I've found helpful is to have established data quality metrics. These metrics enable your data team to handle data aggregation errors in batch processing. These include error rates, duplicate records or values, precision and consistency checks, record and field completeness checks, and timeliness checks. These data quality metrics help organizations maintain data that is reliable, accurate, and fit for its intended purpose. Depending on the specific requirements of your data and business processes, you may choose to focus on certain metrics more than others.