For sure! Our CEO, Salma Bakouk, will be speaking about the top data trends to watch in 2025, including how GenAI and advanced anomaly detection are shaping the future of observability platforms. You’ll walk away with actionable insights for your data strategy.

The right technology can make a big difference. Adopting observability tools that offer features like data lineage tracking, data freshness checks, and pipeline health dashboards can reduce manual firefighting and help your team work more autonomously. This not only improves productivity but also makes day-to-day work more enjoyable.

Aligning KPIs with your data team’s goals is essential for clarity and motivation. When everyone knows what success looks like and how it’s measured, it creates a sense of purpose. Tools that support data quality monitoring and metrics collection can help track those KPIs effectively and ensure your team is on the right path.

Data lineage provides visibility into data dependencies, which helps teams assign, prioritize, and resolve alerts more effectively. In an observability platform like Sifflet, this means faster incident response, better alert correlation, and improved on-call management workflows.

Yes, we’re hosting an exclusive after-party at our booth on October 15! Come join us for great conversations, a champagne toast, and a chance to connect with data leaders who care about data governance, pipeline health, and building resilient systems.

A good observability platform should fit right into your current stack. That means supporting tools like dbt, Airflow, and your cloud infrastructure. Seamless integration ensures better pipeline orchestration visibility and makes it easier to act on data issues without disrupting your workflows.

In 2025, expect to see more focus on AI-driven anomaly detection, dynamic thresholding, and predictive analytics monitoring. Staying ahead means experimenting with new observability tools, engaging with peers, and continuously aligning your data strategy with evolving business needs.

MCP enables LLMs to access structured context like schema changes, validation rules, and logs, making it easier to detect and explain data quality issues. With tool calls and memory, agents can continuously monitor pipelines and proactively alert teams when data quality deteriorates. This supports better SLA compliance and more reliable data operations.