Overcoming incompatibility with openness
One approach to breaking the implicit system oligarchy is to embrace openness as a fundamental practice of data management and analysis. The process has been a success in the operating system (OS) and platform tools arena – note that Linux has been successful and its disruptive effect on the square of proprietary OS software.
The evolution of the open source Hadoop ecosystem reflects a modern approach to openness that addresses the challenges of event flow analysis. How? Much like the Apache ecosystem is used for many new high-performance applications, the scalable, massively parallel hardware configuration works with a variety of extensible components that can be standardized from both operations and content management.
What openness means for analyzing event flows
From the perspective of streaming analytics, openness involves standards of development, implementation, access and application. But openness also includes two critical facets:
- It provides a single virtual framework which simultaneously supports the needs of different types of analysts without requiring significant hacks to ensure interoperability. This means being able to access the data, use analytics tools, integrate event flow analysis and deliver reports and populate dashboards – all from the same environment.
- It simplifies governance and daily oversight of operations.
Finally, an open environment must address performance and cost challenges. One approach is to use a high-performance data architecture stored on top of a scalable, yet resilient, computer environment. This type of scalable configuration in memory meets the need for computational performance. At the same time, deploying on a resilient cloud system allows it to grow and shrink if necessary and address similar cost constraints.
Creating a common, consolidated environment to develop, implement and manage tasks across the entire analytical life cycle streamlines operational management by ensuring tool direction. Standard APIs simplify discovery analytics and application development, especially streaming analytics.
More importantly, effective communication – combined with the combination of a common environment and standard APIs – is a means for different types of analysts to continuously collaborate on end-to-end application design, development and implementation processes. Analysts can work together to standardize definitions, evaluate analytical models, and ensure consistency as they adapt discovered models into operational streaming processes.
The most important takeaway is this: An open big data environment facilitates a smooth development life cycle. It again helps you accelerate, manage and manage the full streaming analytics lifecycle.