Event-based and real-time data streaming architectures shape the backbone of companies that deal with mobile, Internet of Things or other solutions where high-speed volumes of data need to be processed. Let’s build scalable, no-ops pipelines for Machine Learning.
The era of data streaming
Over the years transactional systems have served as the backbone of information driven companies. Now we are in a different era, however. It is a time when data and events are more likely to be streamed than recorded, and business models depend on real-time input and massive quantities of data.
Streaming Data is data that is generated continuously by thousands of data sources, which typically send in the data records simultaneously, and in small sizes (order of Kilobytes). Streaming data includes a wide variety of data such as log files generated by customers using your mobile or web applications, ecommerce purchases, in-game player activity, information from social networks, financial trading floors, or geospatial services, and telemetry from connected devices or instrumentation in data centers.
From real-time data to intelligence
This data needs to be processed sequentially and incrementally on a record-by-record basis or over sliding time windows, and used for a wide variety of analytics including correlations, aggregations, filtering, and sampling. This needs to be done real-time, as the nature of the business has changed, so are the requirements for getting analytics and intelligence out of petabytes of real-time, streaming data.
We are often asked by our distinguished customers to deliver on following expertise:
- Setting up real-time data streaming architectures
- Setting up HPC and parallel computing architectures
- Setting up real-time ETL data pipelines
- Developing robust, secure and scalable API interfaces
- Applying state-of-the-art Machine Learning models for sensor data for:
- predictive asset maintenance,