How do you ensure data quality when working with multiple pipelines?

1 Define data quality standards

Before you start building and running your pipelines, you need to define what data quality means for your project and stakeholders. Data quality standards are the criteria and metrics that you use to measure and evaluate the quality of your data, such as completeness, accuracy, consistency, timeliness, validity, and relevance. You should align your data quality standards with your business objectives, data governance policies, and user expectations. You should also document and communicate your data quality standards to your team and other data consumers.

2 Implement data quality checks

Once you have your data quality standards, you need to implement data quality checks throughout your pipelines to ensure that your data meets those standards. Data quality checks are the processes and tools that you use to validate, monitor, and report on the quality of your data, such as data profiling, data cleansing, data testing, data auditing, and data quality dashboards. You should design your data quality checks to be automated, scalable, and adaptable to different data sources and formats. You should also integrate your data quality checks with your data pipeline orchestration and alerting systems.

3 Handle data quality issues

Even with data quality checks in place, you may still encounter data quality issues that affect your pipelines and outputs. Data quality issues are the problems and errors that occur in your data, such as missing values, duplicates, outliers, format inconsistencies, and schema changes. You should handle data quality issues by applying appropriate strategies and actions, such as data correction, data rejection, data enrichment, data quarantine, and data feedback. You should also document and communicate your data quality issues and their resolutions to your team and other data consumers.

4 Optimize data quality performance

To ensure data quality when working with multiple pipelines, you also need to optimize the performance of your pipelines and data quality checks. Data quality performance is the efficiency and effectiveness of your pipelines and data quality checks in delivering high-quality data, such as speed, reliability, scalability, and maintainability. You should optimize your data quality performance by applying best practices and techniques, such as data partitioning, data caching, data indexing, data compression, and data parallelization. You should also review and improve your data quality performance regularly based on feedback and metrics.

5 Collaborate with data quality stakeholders

Data quality is not only a technical issue, but also a collaborative one. When working with multiple pipelines, you need to collaborate with various stakeholders who are involved in or affected by the quality of your data, such as data producers, data consumers, data analysts, data scientists, and data managers. You should collaborate with your data quality stakeholders by establishing clear roles and responsibilities, sharing data quality standards and expectations, soliciting and providing data quality feedback, and resolving data quality conflicts and issues.

6 Learn from data quality best practices

Finally, you can ensure data quality when working with multiple pipelines by learning from the best practices and experiences of other data engineers and organizations. Data quality best practices are the proven and recommended methods and guidelines that you can follow or adapt to improve your data quality processes and outcomes, such as data quality frameworks, data quality tools, data quality case studies, and data quality communities. You should learn from data quality best practices by researching, benchmarking, and networking with other data quality practitioners and experts.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?