IMTI

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

The eth-netstats project provides a great dashboard interface for monitoring the status of an Ethereum Blockchain from the perspective of its nodes. The website https://ethstats.net/ reports statistics from an extensive list of Ethereum nodes on the public Ethereum Blockchain, however, the eth-netstats software that drives https://ethstats.net/ can also be used to monitor a Private Ethereum Blockchchain as I demonstrate in the previous article Deploy a Private Ethereum Blockchain on a Custom Kubernetes Cluster.

Blockchain technologies have been made famous by Cryptocurrencies such as Bitcoin and Ethereum. However, the concepts behind Blockchain are far more reaching than their support for cryptocurrency. Blockchain technologies now support any digital asset, from signal data to complex messaging, to the execution of business logic through code. Blockchain technologies are rapidly forming a new decentralized internet of transactions.

kubefwd helps to enable a seamless and efficient way to develop applications and services on a local workstation. Locally develop applications that intend to interact with other services in a Kubernetes cluster. kubefwd allows applications with connection strings like http://elasticsearch:9200/ or tcp://db:3306 to communicate into the remote cluster. kubefwd can be used to reduce or eliminate the need for local environment specific connection configurations.

FaaS or Function as a Service also known as Serverless computing implementations are gaining popularity. Discussed often are the cost savings and each implementations relationship to the physical and network architecture of a specific platform or vendor. While many of the cost and infrastructure advantages of FaaS are compelling, its only one of many advantages. Below, I hope to demonstrate how easy it is to develop and deploy FaaS components into a custom Kubernetes cluster. The functions I develop are nearly all business logic, and I believe therein lies the advantage, high-density business logic. Functions can have a higher degree of focus directly on business logic and communication with other services. Functions can communicate with other functions, microservices or monoliths. In this article, I demonstrate this with Elasticsearch.

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.

RBAC (Role Based Access Control) allows our Kubernetes clusters to provide the development team better visibility and access into the development, staging and production environments than it has have ever had in the past. Developers using the command line tool kubectl, can explore the network topology of running microservices, tail live server logs, proxy local ports directly to services or even execute shells into running pods.

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.”

Many of the resources on Cloud Native Microservices show you how easy it is to get up and running with AWS or GKE. I think this is great but for the fact that I see a trend (in my clients at least) of associating concepts with particular products or worse, companies. I love Amazon, but it’s not THE cloud). In my opinion, to embrace Cloud Native and Microservices you should develop some, and host them yourself. The cloud is not Google or Amazon; it’s any cluster of virtualized systems, abstracted from their hardware interfaces and centrally managed.

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.

Reverse proxies are standard components in many web architectures, from Nginx in front of php-fpm serving Drupal or Wordpress, to endless mixtures of load balancers, security appliances, and popular firewall applications. Reverse proxies differ from forward proxies in little but their intended implementation, be it service-side or client side. The following information is useful in either context. However, I focus on a service-side architecture. Further down this article, I’ll be going over the reasonably simple go code needed to develop a basic, yet production quality proxy, but first I’ll give you my take on why they solve so many problems and offer up my little workhorse, n2proxy.

Jupyter Notbooks have been a popular technology in the Python data science community for a while now, especially in academics. Jupyter Notebooks are a way to mix inline, executable code with documentation in a presentation format. Best practices in organizing source code are not always the most efficient at communicating it’s functionality to a user.

This article is a quick tour of basic Python 3 syntax, components and structure. I intend to balance a cheat sheet format with hello world style boilerplate. If you are already a software developer and need a quick refresh on Python then I hope you benefit from my notes below.

Using ingress-nginx on Kubernetes makes adding CORS headers painless. Kubernetes ingress-nginx uses annotations as a quick way to allow you to specify the automatic generation of an extensive list of common nginx configuration options.