Software applications today deal with a deluge of data, necessitating a new landscape of architecture and deployment. A historical view of data is no longer adequate and there is a need to generate insights and value in real-time.
If data is not analysed as soon as it emerges, even a few seconds later the insights may perish forever, losing their value if not acted upon immediately. The fallout of this delay could be a potential loss of business.
The large volume of data poses a significant challenge to applications which are not geared up to process such volumes.
A further challenge is that the data comes in high volumes from multiple streams – each offering a wealth of operational or business insights. Correlation of the data from these streams can offer even richer insights.
How a Streaming Analytics solution helps:
- Detect transactions, identify risks and alert stakeholders in real-time
- Enhance sales by associating raw data with historical data about usage patterns in real-time to offer contextual intelligence to customers
- Identify risks due to the load on devices and equipment wear and tear before failure occurs
- Prevent undesirable events by detecting problems early and avoiding resultant losses
- Cut down the resolution time of service tickets from days to minutes as problems are detected in real-time by analyzing hundreds of thousands of logs or events per second
- Multi-cluster scalability can handle billions of events per day without loss of data or increase in latency
GS Lab has developed a Streaming Analytics Platform to enable your organization to quickly and easily benefit from insights in real-time. The expertise and experience gained from our work on a number of analytics challenges across domains such as healthcare, finance, eCommerce, education and IT have gone into the design of this platform. This customizable platform can be configured to the unique requirements of your business.
- A configurable data handling pipeline based on tested and proven Open Source tools
- Tools to build a custom tool chain using Kafka or RabbitMQ (messaging cluster) and a streaming framework that suits your need (Apache Spark, Apache Storm, Apache Flink or Akka)
- Customizable dashboards or integration with application’s user interface
- Scalable: handles hundreds of thousands of events per second using a high throughput cluster setup
- Ease of deployment: on-premise or on Amazon Web Services
- Easy to use Custom Rules Configuration engine to define complex event processing rules via a simple UI rather than having to create custom code in the underlying framework
- Ability to embed custom/ product specific analysis in data pipeline
- Ability to filter out non-relevant data upfront, as soon as it enters the data pipeline