All data is first generated in real-time, be it financial transactions, sensor data, web clickstreams, geolocation data, weather reports, market data, social media, and other event streams. A real-time streaming analytics platform can help business gain insights from the high-velocity flow of data as they originate, optimize decisions, improve business insights, and accelerate responses to critical events.
This white paper explores the key considerations while choosing a real-time streaming analytics platform.
- Visual low code development
- Application lifecycle management
- Support for ‘real-time’, ‘near real-time’, and batch processing
- Technology agnostic and open source enabled
Download the white paper now to learn more.
You may also be interested in…
Enterprises and IoT applications can benefit immensely from real-time streaming analytics by visualizing the business in real-time, cutting preventable losses, detecting urgent situations…
As the Internet of Things (IoT) generates incessant data, organizations need smarter and more efficient ways to manage and process…
StreamAnalytix Created $5 Million Annual Savings with a New Agent Monitoring System Leading Wireless & Telecom Services Provider
Requirements A leading US-based wireless and telecommunications service provider wanted to optimize call center cost by tracking the desktop activities…
To keep up with the new digital consumer and remain competitive, the auto insurance industry is increasingly investing in connected…
Streaming data ingestion, ETL, and integration along with real-time machine learning are becoming more and more critical capabilities as enterprises look to create and monetize the real-time enterprise.
An Easy Approach to ETL with Apache Spark – Visually prepare, integrate, and transform data as it arrives
Executing analytical queries on massive data volumes with traditional databases and batch ETL processes is complex, expensive, and time-consuming.