Stream processing delivers real time, data-driven insights that were previously impossible — creating a major competitive advantage. This is transforming enterprise data architecture and disrupting the $200 billion-plus big data industry as stream processing replaces slower and less efficient batch data analytics.
But the cost and complexity of stream processing overwhelms many organizations. This paper explores:
- Why traditional business insights (BI) analytics and dashboards based on batch processing aren’t keeping up with the needs of business
- Use cases for streaming data across industries — who is adopting it fastest? What do analysts say?
- How stream processing helps organizations understand events as they happen, and automatically respond to them with ML and AI
- Why data scientists aren’t able to take advantage of streaming data or easily use it in their machine learning models
- Mistakes companies make when building streaming data infrastructure (and how to avoid them)