- The Death of Big Data and the Emergence of the Multi-Cloud Era - Jul 11, 2019.
The Era of Big Data is coming to an end as the focus shifts from how we collect data to processing that data in real-time. Big Data is now a business asset supporting the next eras of multi-cloud support, machine learning, and real-time analytics.
Big Data, Cloudera, Hadoop, Multi-cloud, Realtime Analytics
- Continuous improvement for IoT through AI / Continuous learning - Nov 25, 2016.
In reality, especially for IoT, it is not like once an analytics model is built, it will give the results with same accuracy till the end of time. Data pattern changes over the time which makes it absolutely important to learn from new data and improve/recalibrate the models to get correct result. Below article explain this phenomenon of continuous improvement in analytics for IoT.
AI, Deployment, IoT, Machine Learning, Model Performance, Realtime Analytics
- Getting started with Python and Apache Flink - Nov 13, 2015.
Apache Flink built on top of the distributed streaming dataflow architecture, which helps to crunch massive velocity and volume data sets. With version 1.0 it provided python API, learn how to write a simple Flink application in python.
Flink, Python, Realtime Analytics, Streaming Analytics, Will McGinnis
- Patterns for Streaming Realtime Analytics - Aug 5, 2015.
Design patterns are well-known for solving the recurrent problems in software engineering, on similar lines we can have Streaming Realtime Analytics patterns and avoid reinventing the wheel. Here, you can see the major patterns we found out for it.
Frequent Pattern Mining, Realtime Analytics, Streaming Analytics
- SQL-like Query Language for Real-time Streaming Analytics - Mar 12, 2015.
We need SQL like query language for Realtime Streaming Analytics to be expressive, short, fast, define core operations that cover 90% of problems, and to be easy to follow and learn.
Real-time, Realtime Analytics, SQL, Stream Mining, Streaming Analytics