bayudwiyansatria Goto Github PK

followers: 23.0 following: 18.0 repos: 63.0 gists: 16.0

Name: Bayu Dwiyan Satria

Type: User

Bio: Software Engineer | DevOps Engineer

Twitter: bayudsatria

Location: Manado, Indonesia

Blog: https://www.bayudwiyansatria.com

Welcome to Bayu Dwiyan Satria's profile!

Stack Technology

Tools, languages, and other things that I like to work with.

Java	Python	Go	NodeJS	Docker	Kubernetes	Nginx
RHEL	Debian	Prometheus	Grafana	Elasticsearch	Logstash	Kibana
AWS	GCP	Azure	Ansible	MySQL	PostgreSQL	MongoDB

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Bayu Dwiyan Satria's Projects

apache-hadoop

Apache Hadoop Environment and Configuration Files For Single (Stand Alone) Or Clusters

apache-spark

Apache Spark Environment and Configuration Files For Single (Stand Alone) Or Clusters

bayudwiyansatria-libs-java

Java lib parent for dependencies and plugins management

busybox

BusyBox combines common UNIX utilities into a single containerization

full-pasca-sarjana

Full Project Dependencies of Master Degree Library

ibm-platform-lsf

IBM Platform LSF Environment For Computing

kubernetes-client-java

Official Java client library for kubernetes

Apache Hadoop. Apache Hadoop is a collection of open-source software utilities that facilitate using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model. Originally designed for computer clusters built from commodity hardware—still the common use—it has also found use on clusters of higher-end hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework.

library-java-apache-spark

Apache Spark Libraries. Apache Spark has as its architectural foundation the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. The Dataframe API was released as an abstraction on top of the RDD, followed by the Dataset API. In Spark 1.x, the RDD was the primary application programming interface (API), but as of Spark 2.x use of the Dataset API is encouraged even though the RDD API is not deprecated. The RDD technology still underlies the Dataset API.