Stateful Functions-as-a-Service (SFaaS) platforms, such as Styx, are emerging as powerful abstractions for building distributed, serverless cloud applications. By combining the abilities of FaaS with strong transactional guarantees, they enable complex, stateful workflows without requiring developers to manage infrastructure. However, they lack built-in support for analytical queries across distributed function state. This thesis addresses that gap by proposing H-Styx, whose hybrid architecture extends Styx with a snapshot-based Query Engine, enabling near-real-time OLAP queries over global state while maintaining performance isolation for transactions. The Query Engine integrates seamlessly into the Styx architecture, leveraging periodic snapshots transmitted via a loosely-coupled, asynchronous interface. It ingests partitioned state from object store MinIO into columnar database DuckDB, supports incremental delta loads, and delivers results over a Kafka-based interface to achieve scalable, low-latency analytical querying while employing robust fault tolerance.

Empirical evaluation demonstrates that H-Styx preserves transactional throughput and latency under hybrid workloads, while significantly outperforming a baseline HTAP architecture (Postgres with Streaming Replication) on analytical throughput and providing superior workload isolation. These results validate the feasibility of supporting hybrid transactional and analytical processing in SFaaS environments. Overall, H-Styx bridges a crucial capability gap in SFaaS, enabling more powerful data-driven applications in distributed, event-driven architectures.