Sparkflows is democratizing data science by enabling business users to derive data insights in a self-serve manner. Our Vision: Become the best integrated self-service platform for citizen data analysts and scientist to derive insights from data across the enterprise in a seamless end-to-end experience. *Business Problems Sparkflows is Going After:* -- 1/ Data is growing at an accelerated rate and silos of data are emerging. Secondly, the growing options for compute clusters that support different types of workloads, makes it hard for organizations to understand and leveraging the most optimal compute to power data-driven decisions across the enterprise. 2/ Building data applications has a steep learning curve and the proliferation of fast evolving platforms and tools at different altitudes of the technology stack, requires users to develop several new skills and makes it challenging to solve for the business needs in a seamless manner and further slows down the journey of an organization towards becoming data-driven. 3/ Customers are trying to move fast at various levels of the organization and adopting various technologies without fully understanding the incompatibilities and only solve for a part of the E2E user journey. This further complicates the E2E user journey, forcing more integrations and drastically delaying time to value. *Sparkflows Offering: A self-serve data analytics platform called Fire Insights* -- 1/ A drag and drop workflow designer to minimize coding to create an E2E data application. 2/ A large number of connectors and processors to connect with diverse data sources, understand schema, prep and blend data, train and manage ML models. 3/ With native integrations to enable push-down analytics to Snowflake, Databricks and AWS Glue etc. Sparkflows’ enables users to execute data applications on the most appropriate compute cluster or on customers' choice of compute.