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Java JavaScript PostgreSQL RESTful API Software Architecture
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$15 USD / tunti
Maan MOROCCO lippu
youssoufia, morocco
$15 USD / tunti
Kello on tällä hetkellä 2:23 ip. täällä
Liittynyt syyskuuta 7, 2020
2 Suosittelee

Badr-Eddine E.

@elbatouriB

5,0 (11 arvostelua)
4,4
4,4
$15 USD / tunti
Maan MOROCCO lippu
youssoufia, morocco
$15 USD / tunti
100 %
Suoritetut työt
92 %
Budjetin mukaisesti
92 %
Aikataulussa
33 %
Uudelleenpalkkausaste

Software engineer || SDE @AWS.

Software engineer || AWS SDE Intern. => Programming languages: Java - GoLang - Python - Typescript - Javascript. => Front-end: React - Next - Angular - Tailwind - Chakra UI - Material UI - HTML - CSS - SCSS => Databases: NoSQL( MongoDB - Redis - Elasticsearch ) - SQL - Hibernate - Mongoose - Prisma => Back-end => Spring - JEE - NodeJS - NestJS - ExpressJs. => Microservices => Spring Cloud - Grpc - Kafka => DevOps : Jenkins - Kubernetes - Docker => Cloud Providers : AWS - GCP - Digital ocean => Networking : TCP/UDP, VoIP, WebRTTC, HTTP, RPC, Sockets. My services are: -Building full stack web applications(PWA and SPA included). -Building microservices architecture-based applications. -Building REST APIs and web services. -Building scrapping bots -Building data pipelines -Building Client/Server based application
Freelancer Java Developers Morocco

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Portfoliokohteet

One of my clients that organizes gaming competitions had an idea where there are pretalks before the matches where players make fun of each other and are hyped about the next game. But the client was a sponsor-based business, so he either used his solution or his sponsor's solution; he needed something like discord that could broadcast live to their Facebook/Instagram/youtube pages and also their application. The application was built using ElectronJS, angular, Typescript, and socket.IO for the front ends and Spring, WebRTC, TCP/UDP, Kafka, Kubernetes, Docker, MongoDB, and Keycloack for the back end. Also, the application was a microservices-based application.
Discord clone.
One of my clients that organizes gaming competitions had an idea where there are pretalks before the matches where players make fun of each other and are hyped about the next game. But the client was a sponsor-based business, so he either used his solution or his sponsor's solution; he needed something like discord that could broadcast live to their Facebook/Instagram/youtube pages and also their application. The application was built using ElectronJS, angular, Typescript, and socket.IO for the front ends and Spring, WebRTC, TCP/UDP, Kafka, Kubernetes, Docker, MongoDB, and Keycloack for the back end. Also, the application was a microservices-based application.
Discord clone.
A slack clone application whit the same functionalities plugins can be built using either python or javascript. but the dev should always consume the spring boot API for making new functionalities based on existing functionalities the application is microservices-based.
Slack clone, whit angular and spring.
A Netflix clone with the functionality of streaming videos from the servers dedicated to storage using webRTC for streaming. User and film information is stored in firebase, the business logic layer, and it's built whit spring boot. The application uses spring cloud and Kubernetes to guarantee cloud-native application features. And the application is microservice-based.
Netflix clone using angular,spring boot, firebase and webRTC
A Netflix clone with the functionality of streaming videos from the servers dedicated to storage using webRTC for streaming. User and film information is stored in firebase, the business logic layer, and it's built whit spring boot. The application uses spring cloud and Kubernetes to guarantee cloud-native application features. And the application is microservice-based.
Netflix clone using angular,spring boot, firebase and webRTC
This application was a microservices-based application since I was forced to use some libraries in python that does both some NLP treatment and also web scraping. This application works as a Search engine that searches e-commerce websites based on the user text input and the configuration for the filter(bestseller, location, etc), when the result is scrapped they should be cached(based on how many times they appear as result. so the search operation will be faster (rather than researching using scrapping again) the caching cluster adapts to the size of its container. Since the application was built using different technologies in order for web services to communicate whit each other and the application to be qualified for a cloud-native application the use of docker and Kubernetes along whit spring cloud was a must, and the application has modern features like infinite scrolling (each time the user reaches the end of page new product will appear), and it has the openID connect.
TechGadget recommendation.
This application was a microservices-based application since I was forced to use some libraries in python that does both some NLP treatment and also web scraping. This application works as a Search engine that searches e-commerce websites based on the user text input and the configuration for the filter(bestseller, location, etc), when the result is scrapped they should be cached(based on how many times they appear as result. so the search operation will be faster (rather than researching using scrapping again) the caching cluster adapts to the size of its container. Since the application was built using different technologies in order for web services to communicate whit each other and the application to be qualified for a cloud-native application the use of docker and Kubernetes along whit spring cloud was a must, and the application has modern features like infinite scrolling (each time the user reaches the end of page new product will appear), and it has the openID connect.
TechGadget recommendation.
This application was a microservices-based application since I was forced to use some libraries in python that does both some NLP treatment and also web scraping. This application works as a Search engine that searches e-commerce websites based on the user text input and the configuration for the filter(bestseller, location, etc), when the result is scrapped they should be cached(based on how many times they appear as result. so the search operation will be faster (rather than researching using scrapping again) the caching cluster adapts to the size of its container. Since the application was built using different technologies in order for web services to communicate whit each other and the application to be qualified for a cloud-native application the use of docker and Kubernetes along whit spring cloud was a must, and the application has modern features like infinite scrolling (each time the user reaches the end of page new product will appear), and it has the openID connect.
TechGadget recommendation.
This application was a microservices-based application since I was forced to use some libraries in python that does both some NLP treatment and also web scraping. This application works as a Search engine that searches e-commerce websites based on the user text input and the configuration for the filter(bestseller, location, etc), when the result is scrapped they should be cached(based on how many times they appear as result. so the search operation will be faster (rather than researching using scrapping again) the caching cluster adapts to the size of its container. Since the application was built using different technologies in order for web services to communicate whit each other and the application to be qualified for a cloud-native application the use of docker and Kubernetes along whit spring cloud was a must, and the application has modern features like infinite scrolling (each time the user reaches the end of page new product will appear), and it has the openID connect.
TechGadget recommendation.
This application was a microservices-based application since I was forced to use some libraries in python that does both some NLP treatment and also web scraping. This application works as a Search engine that searches e-commerce websites based on the user text input and the configuration for the filter(bestseller, location, etc), when the result is scrapped they should be cached(based on how many times they appear as result. so the search operation will be faster (rather than researching using scrapping again) the caching cluster adapts to the size of its container. Since the application was built using different technologies in order for web services to communicate whit each other and the application to be qualified for a cloud-native application the use of docker and Kubernetes along whit spring cloud was a must, and the application has modern features like infinite scrolling (each time the user reaches the end of page new product will appear), and it has the openID connect.
TechGadget recommendation.
This application was a microservices-based application since I was forced to use some libraries in python that does both some NLP treatment and also web scraping. This application works as a Search engine that searches e-commerce websites based on the user text input and the configuration for the filter(bestseller, location, etc), when the result is scrapped they should be cached(based on how many times they appear as result. so the search operation will be faster (rather than researching using scrapping again) the caching cluster adapts to the size of its container. Since the application was built using different technologies in order for web services to communicate whit each other and the application to be qualified for a cloud-native application the use of docker and Kubernetes along whit spring cloud was a must, and the application has modern features like infinite scrolling (each time the user reaches the end of page new product will appear), and it has the openID connect.
TechGadget recommendation.
This application was a microservices-based application since I was forced to use some libraries in python that does both some NLP treatment and also web scraping. This application works as a Search engine that searches e-commerce websites based on the user text input and the configuration for the filter(bestseller, location, etc), when the result is scrapped they should be cached(based on how many times they appear as result. so the search operation will be faster (rather than researching using scrapping again) the caching cluster adapts to the size of its container. Since the application was built using different technologies in order for web services to communicate whit each other and the application to be qualified for a cloud-native application the use of docker and Kubernetes along whit spring cloud was a must, and the application has modern features like infinite scrolling (each time the user reaches the end of page new product will appear), and it has the openID connect.
TechGadget recommendation.
In this project, I build a pipeline that extracts/formats/analyze/visualizes log data, the log data can be written directly on the same server, and for other servers that are outside the network, a Kafka cluster should be used to communicate data between the server that have spark streaming cluster on. And finally, all the data streamed to spark will be pre-processed in spark, and then spark streaming will forward it to an Elasticsearch cluster then the data will be visualized whit the help of Kibana.
Log analysing pipeline.
In this project, I build a pipeline that extracts/formats/analyze/visualizes log data, the log data can be written directly on the same server, and for other servers that are outside the network, a Kafka cluster should be used to communicate data between the server that have spark streaming cluster on. And finally, all the data streamed to spark will be pre-processed in spark, and then spark streaming will forward it to an Elasticsearch cluster then the data will be visualized whit the help of Kibana.
Log analysing pipeline.
In this project, I build a pipeline that extracts/formats/analyze/visualizes log data, the log data can be written directly on the same server, and for other servers that are outside the network, a Kafka cluster should be used to communicate data between the server that have spark streaming cluster on. And finally, all the data streamed to spark will be pre-processed in spark, and then spark streaming will forward it to an Elasticsearch cluster then the data will be visualized whit the help of Kibana.
Log analysing pipeline.
In this project, I build a pipeline that extracts/formats/analyze/visualizes log data, the log data can be written directly on the same server, and for other servers that are outside the network, a Kafka cluster should be used to communicate data between the server that have spark streaming cluster on. And finally, all the data streamed to spark will be pre-processed in spark, and then spark streaming will forward it to an Elasticsearch cluster then the data will be visualized whit the help of Kibana.
Log analysing pipeline.
One of my clients asked to provide an application whit the same WhatsApp functionality, but a possibility to add new features easily, so the application should have different front-ends that look the same one using react and one using angular. The backend was built using spring and TCP sockets plus Redis for caching messages to each user. the app architecture was microservices-based. other nonrealtime data ( last time connected etc) are built using KAFKA since there is a possibility that each user data (like last time connected) will be consumed by different clients.
Whatsapp clone.
One of my clients asked to provide an application whit the same WhatsApp functionality, but a possibility to add new features easily, so the application should have different front-ends that look the same one using react and one using angular. The backend was built using spring and TCP sockets plus Redis for caching messages to each user. the app architecture was microservices-based. other nonrealtime data ( last time connected etc) are built using KAFKA since there is a possibility that each user data (like last time connected) will be consumed by different clients.
Whatsapp clone.

Arvostelut

Muutokset tallennettu
Näytetään 1 - 5 / 11 arvostelua
Suodata arvosteluja: 5,0
$800,00 USD
An amazing person with a brilliant intellect. You want to build your complete stack application using any Javascript stack. or perhaps Java, Spring Boot, or Python? You request it, and he will deliver it in any possible technology. He delivered the finest possible outcomes and took an initiative to improve the platform. Technologies used in the project: React. MongoDB. Express. Node.js. Socket.io. Docker, and more. This individual is trustworthy for your project; I can vouch for him.
JavaScript Node.js Express JS Backend Development
+1 enemmän
Käyttäjän avatar
Maan  lippu Adnan A. @adnanahs
6 kuukautta sitten
5,0
$210,00 CAD
Badr again did an amazing job for us. Whenever we have a project that no-one else can fix, Badr is able to resolve our issue for us. He's our "go-to" guy
Java JavaScript .NET Software Architecture
+1 enemmän
Käyttäjän avatar
Maan  lippu Jerry H. @jhobbs2
10 kuukautta sitten
5,0
$50,00 USD
Badr is amazing! Will no longer count how much time did he help me! So professional
PHP Java JavaScript Python Software Architecture
+1 enemmän
A
Maan  lippu Aya I. @ayariyadh9
11 kuukautta sitten
5,0
€80,00 EUR
I enjoyed working with him very much, I loved his work. Great Job.
Python Cryptocurrency
L
Maan  lippu Lukas S. @lukasstaudter
vuosi sitten
5,0
$150,00 USD
Badr is a fast turn-around skillful dev and I enjoyed working with him.
Java JavaScript OAuth
Käyttäjän avatar
Maan  lippu Tony L. @tonylew
vuosi sitten

Kokemus

Software development engineer

Amazon web services
helmik. 2023 - Voimassa
- Working on a distributed system migration service @AWS.

Software engineer

OCP maintenance solutions
kesäk. 2022 - syysk. 2022 (3 kuukautta, 1 )
Full stack devlopper

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