Evolution in cloud data platforms witnesses that enterprises are turning more toward leveraging diversities of systems to satisfy some selected analysis. Sorting out useful choices gives rise to great diversity-pick, whereas structured data are mainly carried out through Snowflake, a cloud-native data warehousing platform, while real-time log analytics is one space within which Azure Data Explorer, Kusto, shines. Data analytics integration of the latter presents a hurdle to overcome for data analytics between structured and semi-structured closets. A unified framework for directly linking Snowflake and Kusto, long desired as a dream, is presented here as an awfully mighty rider such a dream turned into reality. Snowflake’s stored procedures, which are typically implemented using Python, are integrated into the Kusto query engine and complemented by the credential management capabilities of Azure Key Vault. Kusto data can be dynamically queried, and results placed back in Snowflake as a table. These are encapsulated in reusable stored procedures, permitting scaling up through reuse and through quite granular collaboration for workflow automation among the entire user community. Among the design and implementation strategies in this research, the operational profit of the actors are in the discussion. This will also shed light on the performance and scalability considerations and unveil real-life use-case examples for innovation purposes. This framework for integration allows synergy in structured data analytics and log monitoring in real time, which, in turn, can allow businesses to enhance actionable insight draws from such diverse sources.