Real Time Analytics

As organisations have grown, so have their data needs. Traditionally, the sources of data for organisations were operational tools e.g. CRMs, transactional systems among others. This data would typically be generated in known periods and have known structure. During this era, the analytics needs of the business could easily be…

Introduction Real-time data has become the new powerful type of brand that sets businesses apart in competition when it is well tapped, processed and used. This is because when it is well used, carries the key to hacking decision-making and setting apart businesses in the competing market. Apache Kafka and Memphis…

How to leverage append-only streams and key-value streams to monitor securities portfolios in real time. — Speed matters in capital markets. Just ask the shareholders of SVB. One key component in capital markets that supports traders is post-trade analysis, the analyzing of trading activities and market data after trades have been executed. When a trade is executed, for example, all of the information attached to it…

Welcome to a New Era of Streaming Databases 🎉 For data engineers and analysts, you should have “Streaming Database” in your toolbox, to simplify the data stack, reduce end-to-end latency, reduce costs, and deliver faster. I listed 9 things you need to know about streaming databases. …

Apache Flink is a distributed processing engine and framework for stateful computations over unbounded and bounded data streams. This blog will teach you everything you need to know to get started with Apache Flink. What is Apache Flink? Apache Flink is a framework to do data processing. …

It is you and me Undoubtedly, Real-Time Machine Learning (RTML) models are extremely valuable. For real-time machine learning to succeed though, the enabling platforms need to be credible, scalable, quick, and cost-effective. If we carefully analysed the RTML lifecycle, they would be: data ingestion data filtration through the pipelines feature…