Managing Data Orchestration and Integration at Scale

Why is data integration still a challenge today? And what does data orchestration mean? In this report, Kevin Poskitt and Ginger Gatling from SAP provide in-depth examples that show how companies have evolved from using data integration to data orchestration. By combining streaming data with application data, external data, and social data, data engineers and developers can achieve trusted business outcomes.

You'll learn how to use R, Python, TensorFlow, Apache Kafka, and other open source tools–either to extract data from SAP to put into a data lake or to orchestrate and integrate massive data volumes across complex landscapes. If you're ready to close the gap between the data experts on the SAP team and the development professionals in your company, this guide is indispensable.

You'll examine

Data integration challenges–and why data orchestration needs to evolve
The business imperative for data integration
The reality of hybrid data management today
Examples of how companies can use OS technologies for data integration
The challenges of managing multiple open source stacks
How to orchestrate integration and processing across OS tools while scaling across enterprise apps
How to leverage OS technologies with SAP Data Intelligence
How to address tool and data sprawl when using multiple tools and engines
Complex data orchestration examples
Machine learning within data orchestration