Apache Spark and dbt are powerful tools for managing different aspects of data transformation. Spark is great for large-scale, distributed processing, especially when working with complex transformations and high data volumes. dbt, on the other hand, brings software engineering best practices to SQL-based transformations, making it ideal for analytics engineering. Both tools benefit from integration with observability platforms to ensure transformation pipelines run smoothly and reliably.

By surfacing impacted tables, dashboards, and other assets directly in GitHub or GitLab, Sifflet’s dbt Impact Analysis gives teams real-time visibility into how changes affect the broader data pipeline. This supports better data pipeline monitoring and helps maintain data reliability.

Business teams can access data quality insights directly within their BI dashboards, reducing their reliance on data engineers. This democratizes data observability and empowers teams to make confident, data-driven decisions with full transparency into data lineage and reliability.

Besides measurable improvements, data observability also boosts trust in data, enhances decision-making, and improves the overall satisfaction of your data team. When your team spends less time debugging and more time driving value, it fosters a healthier data culture and supports long-term business growth.

CTEs help simplify complex queries by breaking them into manageable parts. This boosts readability and performance, making it easier to identify issues during root cause analysis and enhancing your data quality monitoring efforts.

That's a great topic! While there's no one-size-fits-all answer, the best data quality programs are collaborative. Everyone from data engineers to business users should play a role. Some organizations adopt data contracts or a Data Mesh approach, while others use centralized observability tools to enforce data validation rules and ensure SLA compliance.

Great question! Data observability helps you monitor the health and integrity of your data as it moves to the cloud. By using an observability platform, you can track data lineage, detect anomalies, and validate consistency between environments, which reduces the risk of disruptions and broken pipelines.

That's a great distinction to understand! Technical data quality focuses on things like accuracy, completeness, and consistency—basically, whether the data is structurally sound. Business data quality, on the other hand, asks if the data actually supports how your organization defines success. For example, a report might be technically correct but still misleading if it doesn’t reflect your current business model. A strong data governance framework helps align both dimensions.