goutham rajanala's starred repositories
Diagrams-as-Code
Cloud & DevOps Architecture Diagrams-as-Code in Python and D2 languages
the-container-security-book
The Container Security Book—a free book for practitioners
modern-linux.info
Learning Modern Linux book website
argo-workflows
Workflow Engine for Kubernetes
airflow-image
This is a hands-on guide to build your working, production-ready airflow-image using the KubernetesExecutor.
kubestrike
A Blazing fast Security Auditing tool for Kubernetes
workshop-setup
Setup instructions to get started with a workshop (https://ibm.github.io/workshop-setup/)
calico-security-controls-for-k8s
[Free Training Workshop] Security Controls for Kubernetes
piping-server
Infinitely transfer between every device over pure HTTP with pipes or browsers
goldilocks
Get your resource requests "Just Right"
openshift-airflow-helm
Deploying Apache Airflow to OpenShift using the standard Helm chart
Lazy_Listener
.exe application that helps to listen files rather than reading them .Can be used for both local and online files
SparkInternals
Notes talking about the design and implementation of Apache Spark
spark-overflow
A stack overflow for Apache Spark
trace-analysis
Scripts to analyze Spark's performance
spark-as-service-using-embedded-server
This application comes as Spark2.1-as-Service-Provider using an embedded, Reactive-Streams-based, fully asynchronous HTTP server
robobenklein.github.io
Something on the internet,
zoharyips.github.io
This is my personal blog, simple but comprehensive.
twitter_pyspark_streaming
pyspark streaming example with flask dashboard
movie-recommendation
Web Data Extraction and Analysis project
Data-Science-Lit
All the Data Analysis exploration projects will be present here either as jupyter :notebook: or :snake: code.
ds-cheatsheets
List of Data Science Cheatsheets to rule the world
Keypoints-Detection-of-an-image
Program to detect keypoints in an image according to the following steps, which are also the first three steps of Scale-Invariant Feature Transform (SIFT). 1. Generate four octaves. Each octave is composed of five images blurred using Gaussian kernels. For each octave, the bandwidth parameters σ (five different scales) of the Gaussian kernels are shown in Tab. 1. 2. Compute Difference of Gaussian (DoG) for all four octaves. 3. Detect key points which are located at the maxima or minima of the DoG images.