Anant Corporation Blog: Our research, knowledge, thoughts, and recommendations about building and managing online business platforms.
Probably foreseeing the need for knowledge platforms, Peter Drucker published “The Coming of the New Organization” in 1988 when I was coming to the United States for the first time as a first grader. I didn’t read the article then, but if I had, I would have started my journey with knowledge & communications systems earlier. In the past, I’ve been told that I over organize things or that I introduce too many sub-directories, tags, categories, etc. I don’t take it personally. I believe that in the long run, our human ability to recognize and name something will ultimately make us better partners with our coming computer counter-parts in the machine learning revolution. In fact, almost all of the machine learning today is done through “training” data which is used to train a computer to do what we do but about a million times faster. Machine learning isn’t going to make your organization a knowledge organization overnight. You are, by asking simple questions of who, what, how, why, what else, and where related to a Sales process.
Real-time data processing is the current state-of-the-art in business platform data engineering practices. Long gone are the days of batch processing and monolithic ETL engines that are turned on at midnight. Today’s demands come from the number of mobile users and things on the internet. Mobile phones and tablets have steadily increased in the realm of customer experience as companies create mobile only or mobile-first interfaces to interact with their commercial systems or business processes. Similarly, there are other “things” in the “Internet of Things” (IoT) such as rental bikes and scooters, key-less home locks, as smart home thermostats.
This resource for monitoring Datastax, Cassandra, Spark, & Solr performance is just the first iteration of a longer initiative to create the best knowledge base on these real-time data platform technologies such as DataStax Enterprise (Cassandra, Spark, and Solr) as well as for Kafka, Docker, and Kubernetes. Our firm, Anant, has been working with Solr/Lucene for several years, and then over the years picked up Spark and Cassandra, and then made the logical move to become experts at and partners with Datastax.
Datastax OpsCenter is good but we’re wise enough to say, however, that it is just the beginning of the toolset needed to really understand what is happening under the hood in the component technologies that comprise of the Datastax Enterprise Platform. When monitoring to scale complex systems such as business platforms you need to review all signals, not just those that come from the database.
Spark,Mesos, Akka, Cassandra, Kafka, Kubernetes? If you don’t already know what these mean and you have no goal or objective to make software that works at a global level, then you don’t need to be reading this article at all. Seriously, it’ll be a waste of your time. These technologies, now open sourced, originated from the extremely high-end university research laboratories of the University of Berkeley and the halls of high-tech companies such as Google, Twitter, LinkedIn, and Facebook. They were built for different purposes for their creators but now being available to the public, they have been flourishing on their own in the wild ether of the Internet. Why would any CIO, CTO, CMO, or a CEO consider these technologies?
Successful companies have strong principles that have helped guide them through their growth. The word principles is often used in business, only occasionally understood, but very rarely exercised the way it is defined. In our own journey be better leaders and business people for our team at Anant, I’ve come across different personal and corporate principles which I have learned from and adopted as my own over the years. Our current set of corporate principles is a combination of ideas from my own Principles of the Modern Enterprise (upcoming post), and Amazon’s Leadership Principles. As we continue to improve our principles to reflect our vision and who we are, we strive to learn from others to be in line with our principle of “Don’t reinvent the wheel” and study those made by Apple, Amazon, Ford, Bridgewater Associates, and General Electric.