Understanding data quality monitoring

Equally important as the efficiency of your pipelines in data engineering is the quality of your data. Inaccurate analyses, flawed business decisions, and a loss of faith in data systems can result from poor data quality. Monitoring data quality is not just a one-time activity but a continuous process that needs to be integrated into your data pipelines. It ensures that the data ingested from various sources conforms to your organization’s quality standards, thereby ensuring that the insights derived are trustworthy and actionable.

Data quality metrics

In data engineering, the quality of your data is just as essential as the efficacy of your pipelines. Poor data quality can result in erroneous analyses, faulty business decisions, and a loss of confidence in data systems.

Setting up alerts and notifications

Not only does automation extend to monitoring, but also to alerting. The next step after configuring data quality checks...