As with many other IT solution categories, the scope of data integration has grown considerably over the past decade. Simplifying and automating data integration across a growing number of devices and platforms, both on premise and in the cloud, is important to reducing the complexities and overall cost of IT.
Big Data integration demands platforms that keep pace with market innovations and support on a massive scale. The design and capabilities of a data integration platform are critical, not only for solution performance, but also for high-level business concerns such as time-to-market, cost of ownership, scalability, and innovation.
Our solutions address several challenges companies face moving and integrating data between mainframe, Linux, Unix and Windows (LUW) and Hadoop environments both on premise and in the cloud, such as:
Parallel Data Mover (PDM) was developed to solve these problems. PDM uses I/O protocols between mainframes, LUW, and Hadoop for data sharing, whether onsite or in the cloud. This frees up valuable network bandwidth for business-critical applications. Unlike traditional networks, PDM’s unique parallel data streaming can address today’s high demand for both quality of service and cost optimization.
As data volumes continue to grow drastically, there is increasing pressure to reduce data latency from near real-time to actual real-time. We responded to this market demand by combining the functionality of PDM with our unique channel-based transport called z/OpenGate. z/OpenGate provides a cross-platform environment with high-bandwidth, low-latency, and efficient data movement. Companies around the world are capitalizing on z/OpenGate to reach new heights in application delivery, while still driving significant cost reductions.