Modern data-driven products rely on accurate and timely business metrics, yet in many systems, critical insights such as decline rates remain difficult to analyze due to fragmented data sources, manual calculations, and inconsistent logic across teams.

In this talk, Viktoriia Kniazeva presents a practical approach to building an end-to-end data processing pipeline designed to automate metric computation and enable reliable analytics at scale. Starting from raw transactional events, the session walks through the architecture of a layered data platform - from ingestion and normalization to aggregation and metric definition - highlighting key design decisions, trade-offs, and challenges in ensuring consistency and trust in business-critical data.

The talk further explores how this foundation can be extended with LLM-powered capabilities to bridge the gap between data and decision-making. By integrating large language models into the analytics layer, the session demonstrates how automated report generation and natural language querying can significantly reduce the time required to investigate anomalies and understand metric changes.

The session focuses on real-world architectural patterns, including data contracts, pipeline orchestration, and metric standardization, and shows how combining robust data engineering with LLM-driven interfaces can transform raw data into actionable insights.