Scheduling Big Data Workloads and Data Pipelines in the Cloud with pyDag

Run Jupyter Notebooks (and store data) on Google Cloud Platform.

GCP_Data_Enginner

An educational project to build an end-to-end pipline for near real-time and batch processing of data further used for visualisation and a machine learning model.

Data Workflows with GCP Dataproc, Apache Airflow and Apache Spark

A Scala Spark based project to experiment with map-reduce algorithms on big data graph shaped

Collection of personal resources on Google Cloud

Yelp ETL Pipeline in Apache Spark on Google Cloud Dataproc

Deploying production ready environment for Spark cluster

Projeto do Curso "Criando um Ecossistema Hadoop Totalmente Gerenciado com Google Cloud Dataproc" do Bootcamp Data Engineer da Digital Innovation One

Creating gcloud dataproc cluster with this github action

Determination of which words occur in a dataset of textbooks along with each word's occurrence count identification with the help of Google Cloud Platform based Dataproc cluster formation.

Código fuente: Análisis de Vuelos basado en trabajo de Valliappa Lakshmanan.

A Java based project aims to extract news articles from large .sgm file, process them and load them into MongoDB Database. It includes an Apache Spark job for word frequency analysis directly from .sgm files, and a sentiment analysis implementation using a Bag-of-Words model in Java.

Training a classification model as a Dataproc Job and using Kafka/PubSub connector for real-time prediction using pre-trained models

Kaggle - Outbrain Click Prediction (Oct-2016 - Jan-2017)

PySpark Job that runs in Dataproc cluster, loads data from Cloud Storage to BigQuery table.

Criando um ecossitema Hadoop totalmente gerenciado com Google Cloud Platform: O desafio consiste em efetuar um processamento de dados utilizando o produto Dataproc do GCP. Esse processamento irá efetuar a contagem das palavras de um livro e informar quantas vezes cada palavra aparece no mesmo.