Best Data Integration Tools for Workday Extend

Most organisations already have the data. The problem is it sits in disconnected systems - ERPs, databases, SCADA, IoT devices, files, and APIs - and nobody has built the layer that ties it all together in real-time. AI makes this worse. Models need clean, integrated, multi-source data to work. Most data environments are not ready. 95% of AI projects never reach production. Rayven builds that layer. It connects IT, OT, IoT, cloud systems, files, data streams, APIs, and proprietary protocols into a single managed data platform - then executes automation, AI models + agentic AI directly on live data. Five unified layers: integration (600+ connectors, bidirectional, real-time), data (processing, storage, AI-ready structuring), execution (automation, AI models, and agentic AI), presentation (custom apps, dashboards, and conversational interfaces), and security, governance + hosting (role-based access, audit trails, and data residency - built in as standard). What gets built on top: custom software, AI agents, predictive analytics, field applications, workflow automations, dashboards, and portals - all from one platform with no external tooling required. Serves businesses from growth-stage to enterprise across 24+ industries globally. Delivery: DIY, Done-For-You (fixed scope, fixed price, two to 12 weeks) + Hybrid. More than 600 pre-built connectors. Ten native AI capabilities. More than 240 deployments live. Rated 5/5 across more than 140 independent reviews.

Datameer is your go-to data tool for exploring, preparing, visualizing, and cataloging Snowflake insights. From exploring raw datasets to driving business decisions – an all-in-one tool.

IBM Cloud Pak for Integration® serves as a comprehensive hybrid integration platform that employs an automated, closed-loop strategy to facilitate various integration styles within a cohesive interface. It allows businesses to unlock their data and assets as APIs, seamlessly connect cloud and on-premises applications, and ensure reliable data movement through enterprise messaging systems. Additionally, it enables real-time event interactions, facilitates cross-cloud data transfers, and allows for scalable deployment using cloud-native architecture alongside shared foundational services, all while maintaining robust enterprise-grade security and encryption. By leveraging this platform, organizations can optimize their integration processes using a multi-faceted approach that is both automated and efficient. Moreover, innovations such as natural language-driven integration flows, AI-enhanced mapping, and robotic process automation (RPA) can be implemented to further streamline integrations and utilize specific operational data for ongoing enhancements, including improved API test generation and workload management. Ultimately, this comprehensive suite empowers businesses to achieve superior integration outcomes and adapt to evolving demands effectively.

Meltano offers unparalleled flexibility in how you can deploy your data solutions. Take complete ownership of your data infrastructure from start to finish. With an extensive library of over 300 connectors that have been successfully operating in production for several years, you have a wealth of options at your fingertips. You can execute workflows in separate environments, perform comprehensive end-to-end tests, and maintain version control over all your components. The open-source nature of Meltano empowers you to create the ideal data setup tailored to your needs. By defining your entire project as code, you can work collaboratively with your team with confidence. The Meltano CLI streamlines the project creation process, enabling quick setup for data replication. Specifically optimized for managing transformations, Meltano is the ideal platform for running dbt. Your entire data stack is encapsulated within your project, simplifying the production deployment process. Furthermore, you can validate any changes made in the development phase before progressing to continuous integration, and subsequently to staging, prior to final deployment in production. This structured approach ensures a smooth transition through each stage of your data pipeline.