Streaming
Data Processing — Data
Streaming data pipelines process events in real time — ingesting and transforming continuous data flows to power live analytics and applications.
Technologies available for this scope
What organizations face today
- Low latency — processing events as they arrive
- Scale — handling high-throughput streams
- Ordering & consistency — exactly-once semantics
- State management — stateful stream processing
- Reliability — resilient event pipelines
Future Challenges
Streaming-first
Real-time as the default architecture.
Unified batch & stream
One engine for both paradigms.
AI-driven processing
Smart, adaptive stream pipelines.
Serverless streaming
Event processing without clusters.
Data mesh
Decentralized, real-time data products.
A Virtuous Cycle of Services
Services define the WHAT — the desired action according to the service type. They follow a virtuous cycle aligned with your Streaming initiatives.
Define
Knowledge Transfer- Deliverable
We define a Streaming skills development plan covering automation, security and observability.
Teach
Knowledge Transfer- Videos recorded session
- Deliverable
We deliver technical Streaming sessions tailored to your teams' level and objectives.
Validate
Knowledge Transfer- Deliverable
We validate Streaming knowledge through practical exercises and real-world scenarios.
Evaluate
Knowledge Transfer- Deliverable
We evaluate Streaming skills and identify improvement areas for continuous progression.
Mentor
Knowledge Transfer- Deliverable
We provide Streaming technical mentoring for troubleshooting, design and optimization.
Inform
Knowledge Transfer- Webinar
- Deliverable
We keep you informed about Streaming trends, best practices and emerging technologies.
AI & Automation
AI and automation enhance every aspect of your Streaming scope: detection, analysis, configuration, remediation, prediction and optimization.
- Stream optimization
- Anomaly detection
- Latency analysis
- Capacity forecasting