An end-to-end GoodReads Data Pipeline for Building Data Lake, Data Warehouse and Analytics Platform.

Terraform module to provision an Elastic MapReduce (EMR) cluster on AWS

BERT, AWS RDS, AWS Forecast, EMR Spark Cluster, Hive, Serverless, Google Assistant + Raspberry Pi, Infrared, Google Cloud Platform Natural Language, Anomaly detection, Tensorflow, Mathematics

Classwork projects and home works done through Udacity data engineering nano degree

Reference Architectures for Datalakes on AWS

Bits of code I use during live demos

The goal of this project is to offer an AWS EMR template using Spot Fleet and On-Demand Instances that you can use quickly. Just focus on writing pyspark code.

Apache Spark TPC-DS benchmark setup with EMR launch setup

A boilerplate for spark projects with docker support for local development and scripts for emr support.

A Cassandra Architecture for GDELT Database 🌍

Uses EMR clusters to export dynamoDB tables to S3 and generates import steps

Create a data pipeline on AWS to execute batch processing in a Spark cluster provisioned by Amazon EMR. ETL using managed airflow: extracts data from S3, transform data using spark, load transformed data back to S3.

This project demonstrates the use of Amazon Elastic Map Reduce (EMR) for processing large datasets using Apache Spark. It includes a Spark script for ETL (Extract, Transform, Load) operations, AWS command line instructions for setting up and managing the EMR cluster, and a dataset for testing and demonstration purposes.

A large-scale data framework that will enable us to store and analyze financial market data and drive future predictions for investment.

An end-to-end data pipeline for building Data Lake and supporting report using Apache Spark.

This is an ETL application on AWS with general open sales and customer data that you can find here: https://github.com/camposvinicius/data/blob/main/AdventureWorks.zip, it's a zipped file with some .csvs inside that we will apply transformations.

This project is to prove the efficiency of distributed computing and distributed database. The machine learning multiple classification algorithms in spark were used to predict the Air Quality Index in California.

Projects related to Udacity Data Engineering Nanodegree including Data Modeling, Infrastructure setup on AWS cloud, Data Warehousing and Data Lake development on Amazon EMR and Redshift, developing Data Pipelines using Apache Airflow.

Hosting data lake with bid-ask data in S3 using Spark and Airflow