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Frequently asked questions
How does Etam ensure pipeline health while scaling its data operations?
Etam uses observability tools like Sifflet to maintain a healthy data pipeline. By continuously monitoring real-time metrics and setting up proactive alerts, they can catch issues early and ensure their data remains trustworthy as they scale operations.
Can Sifflet help me trace how data moves through my pipelines?
Absolutely! Sifflet’s data lineage tracking gives you a clear view of how data flows and transforms across your systems. This level of transparency is crucial for root cause analysis and ensuring data governance standards are met.
How does the Model Context Protocol (MCP) improve data observability with LLMs?
Great question! MCP allows large language models to access structured external context like pipeline metadata, logs, and diagnostics tools. At Sifflet, we use MCP to enhance data observability by enabling intelligent agents to monitor, diagnose, and act on issues across complex data pipelines in real time.
Why did Adaptavist choose Sifflet over other observability tools?
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.
What role does metadata play in a data observability platform?
Metadata provides context about your data, such as who created it, when it was modified, and how it's classified. In a data observability platform, strong metadata management enhances data discovery, supports compliance monitoring, and ensures consistent, high-quality data across systems.
Why is aligning data initiatives with business objectives important for Etam?
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.
How does Flow Stopper support root cause analysis and incident prevention?
Flow Stopper enables early anomaly detection and integrates with your orchestrator to halt execution when issues are found. This makes it easier to perform root cause analysis before problems escalate and helps prevent incidents that could affect business-critical dashboards or KPIs.
Is it hard to set up the Sifflet and ServiceNow integration?
Not at all! It only takes a few minutes to get started. Just follow our step-by-step integration guide, and you’ll be ready to connect your data observability alerts directly to ServiceNow in no time.