Streaming & Machine LearningEvent Stream Processing combined with Machine Learning has become a must have for data-driven companies.
What is event stream processing?
Event Stream Process works with an infinite stream of data with continuous computation, as it flows, with no need to collect or store the data to act on it. It’s used to query continuous data stream and detect conditions, quickly, within a small time period from the time of receiving the data .
Why ESP is necessary?
Some insights are more valuable shortly after it has happened with the value diminishes very fast with time. Stream Processing enables such scenarios, providing insights faster, often within milliseconds to seconds from the trigger.
“Event stream processing is the ability to work with an infinite stream of data with continuous computation, as it flows,
with no need to collect or store the data to act on it.”
Kafka Streams in Action – William P. Bejeck Jr.
Why using Event Stream Processing?
Streaming systems have finally reached a point of maturity
Batch using streaming
It’s possible to obtain batch features directly using modern stream processors
Stream vs Batch
Streaming is a superset of batch
Bounded vs unbounded data
Bounded data: finite stream of data
Unbounded data: infinite stream of data
Near Real-time vs Real-time
There is a delay introduced between the occurrence of an event and the processing of the data
The events are processed as soon as possible by the stream processor
Data analytics and machine learning
Machine learning advancements such as neural networks and deep learning algorithms can discover hidden patterns in unstructured data sets and uncover new information, vital for companies. Actually, when machine learning and streaming data meet, amazing things can happen. Radicalbit Data Management Platform offers a set of tools specifically designed around data scientist’s needs, a simple yet powerful way to reduce the time to market in deploying machine learning enhanced analytics applications.
Radicalbit Technology Stack
From Lambda to Kappa architecture
The solutions developed by Radicalbit are based on the Kappa Architecture, a powerful and agile approach able to manage natively streaming data sources together with batch.
Popular Big Data Architecture for data processing. It is typically batch based, and treat streaming sources in a separate flow that needs to be merged at a later stage.
Streaming-Oriented, Event based architecture for Data Processing. It Matches Streaming AND Batch requirements in a unique flow.
At Radicalbit we like to explore: here’s some of the core technologies we’re using within our platform -either as enabling frameworks or as programming techniques.
Event broker / Distributed Streaming Platform
Distributed Cluster computing framework
AKKA / PLAY
Machine Learning and A.I. platform