ThingSpeak is an open-source Internet of Things (IoT) application and API to store and retrieve data from things using the HTTP protocol over the Internet or via a Local Area Network.ThingSpeak was originally launched by ioBridge in 2010 as a service in support of IoT applications. ThingSpeak™ is an IoT analytics platform service that allows you to aggregate, visualize and analyze live data streams in the cloud.
ThingSpeak provides instant visualizations of data posted by your devices to ThingSpeak. With the ability to execute MATLAB® code in ThingSpeak you can perform online analysis and processing of the data as it comes in. ThingSpeak is often used for prototyping and proof of concept IoT systems that require analytics.
ThingSpeak allows you to aggregate, visualize and analyze live data streams in the cloud.
Some of the key capabilities of ThingSpeak include the ability to
Easily configure devices to send data to ThingSpeak using popular IoT protocols.
Visualize your sensor data in real-time.
Aggregate data on-demand from third-party sources.
Use the power of MATLAB to make sense of your IoT data.
Run your IoT analytics automatically based on schedules or events.
Prototype and build IoT systems without setting up servers or developing web software.
Automatically act on your data and communicate using third-party services like Twilio® or Twitter®.
Minikube is a tool that makes it easy to run Kubernetes locally. Minikube runs a single-node Kubernetes cluster inside a VM on your laptop for users looking to try out Kubernetes or develop with it day-to-day.
Minikube supports Kubernetes features such as:
ConfigMaps and Secrets
Container Runtime: Docker, rkt, CRI-O and containerd
Enabling CNI (Container Network Interface)
When using a single VM of Kubernetes, it’s really handy to reuse the Minikube’s built-in Docker daemon; as this means you don’t have to build a docker registry on your host machine and push the image into it -
We can just build inside the same docker daemon as minikube which speeds up local experiments. Just make sure you tag your Docker image with something other than ‘latest’ and use that tag while you pull the image. Otherwise, if you do not specify version of your image,
it will be assumed as :latest, with pull image policy of Always correspondingly, which may eventually result in ErrImagePull as you may not have any versions of your Docker image out there in the default docker registry (usually DockerHub) yet.
Polymer provides a number of features over vanilla Web Components:
Simplified way of creating custom elements
Both One-way and Two-way data binding
Conditional and repeat templates
Polymer.js places a hefty set of requirements on the browser, relying on a number of technologies that are in still in the process of standardization (by W3C) and are not yet present in today’s browsers.
Examples include the shadow dom, template elements, custom elements, HTML imports, mutation observers, model-driven views, pointer events, and web animations. These are marvelous technologies, but at least as of now, that are yet-to-come to modern browsers.
The Polymer strategy is to have front-end developers leverage these leading-edge, still-to-come, browser-based technologies, which are currently in the process of standardization (by W3C), as they become available.
The recommended polyfills are designed in such a way that (at least theoretically) will be seamless to replace once the native browser versions of these capabilities become available.
Video for Polymer.Js https://www.youtube.com/watch?v=tvafAyxkuVk
Start quickly with built-in navigators that deliver a seamless out-of-the-box experience.
2) Components built for iOS and Android
Platform-specific look-and-feel with smooth animations and gestures.
3) Completely customizable
4) Extensible platform
React Navigation is extensible at every layer— you can write your own navigators or even replace the user-facing API.
MLlib (Spark) is Apache Spark’s machine learning library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.
Regression: generalized linear regression, survival regression,...
Decision trees, random forests, and gradient-boosted trees
Recommendation: alternating least squares (ALS)
Clustering: K-means, Gaussian mixtures (GMMs),...
Topic modeling: latent Dirichlet allocation (LDA)
Frequent itemsets, association rules, and sequential pattern mining
Spark revolves around the concept of a resilient distributed dataset (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. There are two ways to create RDDs: parallelizing an existing collection in your driver program, or referencing a dataset in an external storage system, such as a shared filesystem, HDFS, HBase, or any data source offering a Hadoop InputFormat.
Statsmodels is a Python package that allows users to explore data, estimate statistical models, and perform statistical tests. Statsmodels is built on top of the numerical libraries NumPy and SciPy, integrates with Pandas for data handling and uses Patsy for an R-like formula interface.
Statsmodels is part of the scientific Python stack that is oriented towards data analysis, data science and statistics. Statsmodels is built on top of the numerical libraries NumPy and SciPy, integrates with Pandas for data handling and uses Patsy for an R-like formula interface. Graphical functions are based on the Matplotlib library. Statsmodels provides the statistical backend for other Python libraries. Statmodels in free software released under the Modified BSD (3-clause) license.
Linear regression models:
Mixed Linear Model with mixed effects and variance components
GLM: Generalized linear models with support for all of the one-parameter exponential family distributions
Bayesian Mixed GLM for Binomial and Poisson
GEE: Generalized Estimating Equations for one-way clustered or longitudinal data
Nonparametric statistics: Univariate and multivariate kernel density estimators
Datasets: Datasets used for examples and in testing
Statistics: a wide range of statistical tests
Imputation with MICE, regression on order statistic and Gaussian imputation
Tools for reading Stata .dta files, but pandas has a more recent version
Scapy is a powerful interactive packet manipulation program. It is able to forge or decode packets of a wide number of protocols, send them on the wire, capture them, match requests and replies, and much more. It can easily handle most classical tasks like scanning, tracerouting, probing, unit tests, attacks or network discovery (it can replace hping, 85% of nmap, arpspoof, arp-sk, arping, tcpdump, tethereal, p0f, etc.).
It also performs very well at a lot of other specific tasks that most other tools can’t handle, like sending invalid frames, injecting your own 802.11 frames, combining technics
Scapy is a packet manipulation tool for computer networks, written in Python by Philippe Biondi. It can forge or decode packets, send them on the wire, capture them, and match requests and replies. It can also handle tasks like scanning, tracerouting, probing, unit tests, attacks, and network discovery.
Scapy provides a Python interface into libpcap, (WinPCap/Npcap on Windows), in a similar way to that in which Wireshark provides a view and capture GUI. It can interface with a number of other programs to provide visualization including Wireshark for decoding packets, GnuPlot for providing graphs, graphviz or VPython for visualisation, etc.
The concept behind Scapy is that it is cable of sending and receiving packets and it can sniff packets. The packets to be sent can be created easily using the built-in options and the received packets can be dissected. Sniffing of packets helps in understanding what communication is taking place on the network.