How do you prevent common data quality issues in data pipelines?

It would be good to check data integrity as well by using a metric like PSI and CSI score to measure any deviation in the distribution of data.

Here comes the spotlight on the under discussed Data contracts. Its an agreement that specifies key attributes of a particular data asset which improves data quality metrics and standards. As data ecosystems become more complex with distributed systems and teams, formal data contracts enable governance of data pipelines and quality. Deviations from the contract can trigger alerts. Contracts clearly enforce validation rules, formats, SLAs, and most of the common issues in the data pipelines.

Value from Data is as good as Quality of Data! As a Data driven Organization, the focus is, to drive business decisions based on data. This can only be possible when the Stakeholders have Trust on Data. The key aspect behind, Data Trustworthiness, is DQ Metrics. Before defining DQ KPI, one must understand Source data through Profiling. Business should discuss Validity of possible data values. There could be variety of Criteria to highlight DQ i.e. Range, Pattern, Format, NullCheck, Uniqueness etc. The second aspect, Source correction, wherever necessary. Not only value correction but fixing a Business Process, to prevent future scenarios. DQ aim should be Zero re-occurrence! Practically Ambitious; with Business, Achievable!

I would suggest to include business validation rules as earlier as possible within the data pipeline process. In addition, it is useful to create a view on datasets that did not comply with such rule.

Detect early in data pipeline, collect metrics and setup alerts - raw data schema validations - data integrity checks - business domain specific checks

Adhering to data quality is an important aspect for a data engineer. Though it's not that data quality is solely to be taken care by data engineers but the entire data team must focus on the following data validation and testing aspects: - Data Profiling - Data Cleansing - Data Auditing - Data Lineage - Data Anomaly Detection - Data Validation Rules - Automated Testing - Data Quality Metrics These key attributes of data validation and testing can be golden attributes for the organizations to ensure that their data meets the defined quality metrics and standards, identify and resolve data quality issues early, and maintain data integrity throughout the data pipeline.

Many libraries can be used to design data quality checks: 1. pydeequ 2. greatexpectations 3. SQLCheckoperators in Airflow 4. pydantic

Documents on processes will help clarifying what can be automated and what needs to be monitored manually as building an SOP on what the pipeline process is can help mitigate the risk of making mistakes as well as detecting where things go wrong down the line in results evaluation. Process Design document is important for others to help troubleshoot the problems.

In Apache Airflow Dags setup retries and on-failure alerts. on AWS cloud use cloud watch logs and metrics to observe and alert on failures.

Begin with defining data confidentiality categories and process accordingly. Define data contracts and establishing data access mechanisms. Define roles for data processing and follow least privilege principal. Create Data Lineage

In addition, managing CI/CD (continous integration and continous deployment /delivery) process is very important. Furthermore, there should be a machanism to handle change requests.