Analytics, Apache Spark, Apache Storm, Bigdata, Hadoop

Apache Storm key takeaways…

Hadoop moves the code to the data, Storm moves the data to the code. This behavior makes more sense in a stream-processing system, because the data set isn’t known beforehand, unlike in a batch job. Also, the data set is continuously flowing through the code. A Storm cluster consists of two types of nodes: the master node and the worker nodes. A master node runs a daemon called Nimbus, and the worker nodes each run a daemon called a Supervisor. The master node can be thought of as the control center. In addition to the other responsibilities, this is where […]

Analytics, Bigdata, Kafka

Moving to communication of events between subsystems — CQRS-ES with open source…

Before going into definitions of EP, CEP, and QSQS let us start with some basic database term and what problem we are trying to address here. We have commercial databases and database professionals those who publicized CRUD operations a lot. It is one-row-per-pattern works well in most of the projects and enough to build an application more quickly and securely. I have probably implemented 100 CRUD projects (including web applications) and we do that way because we have limited budgets and projects have deadlines. CRUD work well until someone asked for historical data and I saw few managers complaining lack […]