In the past decade a number of technologies have revolutionized the way we do analytics in banking. In this talk we would like to summarize this journey from classical statistical offline modeling to the latest real-time streaming predictive analytical techniques. In particular, we will look at hadoop and how this distributing computing paradigm has evolved with the advent of in-memory computing and distributed machine learning using Spark.Finally, we will describe how to make data science actionable and how to overcome some of the limitations of current batch processing with streaming analytics.
We are living the big data revolution. But what about fast data? Analytics on recent data is becoming increasingly relevant, since it provides better insight and better models in a world of rapidly changing trends and conditions.
Streaming Analytics allows to compute and process data and events as soon as they enter the data system, providing unprecedented levels of reactiveness. Customers are enjoying live, personalized information streams. Companies can be more effective with respect to marketing, security, operation excellence and business process management.
In this talk we will start from traditional batch processes, touching upon the latest development about big data and hadoop, to move further into the world of fast moving data.
We will explore some of the bespoken systems and tools in streaming Analytics such as Spark, Samza, Kafka, Akka and describe some typical it architectures and data processing related to streaming data. Finally we will look at how to combine Streaming Data with an existing batch, off-line analytical solution.
Presented at Big Data & Analytics Innovation Summit
The Innovation Enterprise, November 11 & 12, London, 2015