The very first step is to set clear objectives that align with your company’s level of data maturity and business needs. This means involving stakeholders from different departments and deciding whether your focus is on exploratory analysis, business intelligence, or innovation through AI and ML. These goals will guide your choices in data stack, platform, and hiring.

Great question! At Sifflet, we believe that bringing together data cataloging, data quality monitoring, and lineage tracking into a single observability platform helps reduce Data Entropy and streamline how teams manage and trust their data. By centralizing these capabilities, users can quickly discover assets, monitor their health, and troubleshoot issues without switching tools.

Data quality monitoring ensures your data is accurate, complete, and consistent. A good data catalog should include profiling and validation tools that help teams assess data quality, which is crucial for maintaining SLA compliance and enabling proactive monitoring.

Unlike tool-specific catalogs, Sifflet serves as a 'Catalog of Catalogs.' It brings together metadata from across your entire data ecosystem, providing a single source of truth for data lineage tracking, asset discovery, and SLA compliance.

Callum and his team were impressed by how quickly Sifflet’s cross-repo data lineage tracking gave them visibility into their pipelines. Within days, they had a working proof of concept and were debugging in minutes instead of days. The unified view across their stack made Sifflet the right fit for scaling data observability across teams.

SQL Table Tracer uses a Monoid-based design to handle complex SQL structures like Common Table Expressions (CTEs) and subqueries. This approach allows it to incrementally and safely compose lineage information, ensuring accurate root cause analysis and data drift detection.

At Etam, every data project begins with the question, 'How does this help us reach our OKRs?' This alignment ensures that data initiatives are directly tied to business impact, improving sponsorship and fostering collaboration across departments. It's a great example of business-aligned data strategy in action.

Reverse ETL automates the syncing of data from your warehouse to business apps, helping reduce the number of manual data requests across teams. This improves data reliability by ensuring consistent, up-to-date information is available where it’s needed most, while also supporting SLA compliance and data automation efforts.