It depends on your company's size, data maturity, and use cases. Some teams report to engineering or product, while others operate as independent entities reporting to the CEO or CFO. The key is to avoid silos and unclear ownership. A centralized or hybrid structure often works well to promote collaboration and maintain transparency in data pipelines.

Schema evolution can introduce unexpected fields or data type changes that disrupt both batch and streaming data workflows. With proper data pipeline monitoring and observability tools, you can track these changes in real time and ensure your systems adapt without losing data quality or breaking downstream processes.

Absolutely! While dbt tests are a great starting point, Sifflet takes things further with advanced observability tools. By ingesting dbt tests into Sifflet, you can apply powerful features like dynamic thresholding, real-time alerts, and incident response automation. It’s a big step up in data reliability and SLA compliance.

Data observability is the ability to monitor, understand, and troubleshoot data health across the entire data stack. It's essential for modern data teams because it helps ensure data reliability, improves trust in analytics, and prevents costly issues caused by broken data pipelines or inaccurate dashboards. With the rise of complex infrastructures and real-time data usage, having a strong observability platform in place is no longer optional.

By combining Sifflet with Google Cloud, you get end-to-end cloud data observability, real-time metrics, and proactive monitoring across your data stack. It’s a powerful way to boost your data reliability and meet your SLA compliance goals.

Table-level lineage helps teams perform impact analysis, debug broken pipelines, and meet compliance standards by clearly showing how data flows between systems. It's foundational for data quality monitoring and root cause analysis in modern observability platforms.

Traditional monitoring often relies on batch processing, leading to delays in inventory updates. It also struggles with data silos, lacks robust data quality monitoring, and is mostly reactive. In contrast, modern observability tools provide real-time insights, dynamic thresholding, and predictive analytics monitoring to keep up with fast-paced retail environments.

Data quality monitoring is essential for maintaining the integrity of your data assets. It helps catch issues like missing values, inconsistencies, and outdated information before they impact business decisions. Combined with data observability, it ensures that your data catalog reflects trustworthy, high-quality data across the pipeline.