IMTI

I’ve been distracted for over a year now, writing a (~500 page) end-to-end tutorial on constructing data-centric platforms with Kubernetes. The book is titled “Advanced Platform Development with Kubernetes: Enabling Data Management, the Internet of Things, Blockchain, and Machine Learning”

Kafka is a fast, horizontally scalable, fault-tolerant, message queue service. Kafka is used for building real-time data pipelines and streaming apps.

The following is an overview for querying Elasticsearch. Over the years I have tried to assemble developer notes for myself and my team on a variety of platforms, languages and frameworks, a type of cheat-sheet but with context, not a comprehensive how-to, but a decent 15-minute overview of the features we are most likely to implement in a given iteration.

This guide walks through a process for setting up Kibana within a namespace on a Kubernetes cluster. If you followed along with Production Grade Elasticsearch on Kubernetes then aside from personal or corporate preferences, little modifications are necessary for the configurations below.

Installing production ready, Elasticsearch 6.2 on Kubernetes requires a hand full of simple configurations. The following guide is a high-level overview of an installation process using Elastic’s recommendations for best practices. The Github project kubernetes-elasticsearch-cluster is used for the Elastic Docker container and built to operate Elasticsearch with nodes dedicated as Master, Data, and Client/Ingest.

There is an overwhelming number of options for developers needing to provide data visualization. The most popular library for data visualization in Python is Matplotlib, and built directly on top of Matplotlib is Seaborn. The Seaborn library is “tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels.”

Pandas bring Python a data type equivalent to super-charged spreadsheets. Pandas add two highly expressive data structures to Python, Series and DataFrame. Pandas Series and DataFrames provide a performant analysis and manipulation of “relational” or “labeled” data similar to relational database tables like MySQL or the rows and columns of Excel. Pandas are great for working with time series data as well as arbitrary matrix data, and unlabeled data.

Python is one of The Most Popular Languages for Data Science, and because of this adoption by the data science community, we have libraries like NumPy, Pandas and Matplotlib. NumPy at it’s core provides a powerful N-dimensional array objects in which we can perform linear algebra, Pandas give us data structures and data analysis tools, similar to working with a specialized database or powerful spreadsheets and finally Matplotlib to generate plots, histograms, power spectra, bar charts, error charts and scatterplots to name a few.

We live in a world of process isolation and tools that make utilizing it extremely simple, with apps like Docker we can perform dependency management with dependency isolation. As I am slowly becoming a fanboy of containerization, I look forward to the day when typing ps on my local workstation or remote server is nearly synonymous with commands like docker ps or kubectl get services.