December 2025 Summaries

Multi-tenant databases are essential for SaaS providers, ISVs, and enterprises, enabling multiple tenants to share a single application instance while keeping their data isolated. However, the growth in tenant numbers often leads to table proliferation, posing challenges for Change Data Capture (CDC). Streamkap provides a solution by automating CDC processes, efficiently aggregating tables using Kafka for streaming and Flink for processing, offering sub-second latency without complex management. This platform reduces total cost of ownership (TCO) by supporting hybrid, on-prem, and cloud environments, ensuring real-time data flows to destinations like Snowflake and Databricks while automating schema evolution and security measures. Streamkap's enterprise-grade solution handles various multi-tenant database architectures, including logical and structural models, and supports major databases like Postgres, MySQL, SQL Server, Oracle, and MongoDB. It leverages regex-based grouping and logical sharding strategies to manage high volumes of tables, offering ultra-low latency, enhanced security, reduced costs, and operational stability to transform fragmented data into actionable intelligence.

Limble, a maintenance and asset management company, faced significant challenges managing a vast and complex data architecture with nearly 400,000 database tables, leading to high costs, maintenance headaches, and limited product analytics capabilities. Initially relying on an in-house data pipeline using DMS, CDC, and Snowpipe, Limble encountered substantial issues with scalability, cost, and latency. The introduction of Streamkap provided a crucial solution, allowing Limble to collapse similar tables into unified structures in Snowflake, reducing costs by 40 times and dramatically cutting maintenance hours. This change enabled real-time analytics and improved internal product analytics, setting Limble up for a scalable future. Streamkap's support was instrumental in their transition, enabling Limble to deploy a more efficient and cost-effective data infrastructure while planning for future expansions and feature enhancements.