arquitetura_flux

barra-leitura

bootcamp-igti-machine-learning

bootcampdesafio01

Desafio 01 do Bootcamp Rocketseat

bootcampfase02modulo01

Bootcamp - Fase 02 - Modulo 01

cei-crawler

Crawler para pegar dados do CEI 🤑💰💸

context-api

cs-go-pipeline

Data pipeline, designed to scrap Counter Strike GO data from hltv.org using scrapy, Docker Compose, Airflow, Amazon S3 and EMR with PySpark to process the data and write it in a Data Lake.

data-engineering-capstone-project

data-engineering-notes

data-lake-with-spark

In this project, we'll apply what we've learned on Spark and data lakes to build an ETL pipeline for a data lake hosted on S3. To complete the project, we will need to load data from S3, process the data into analytics tables using Spark, and load them back into S3. We'll deploy this Spark process on a cluster using AWS.

data-modeling-with---apache-cassandra

In this project, I applied what I've learned on data modeling with Apache Cassandra and completed an ETL pipeline using Python. To complete the project, I needed to model my data by creating tables in Apache Cassandra to run queries. I was provided with part of the ETL pipeline that transfers data from a set of CSV files within a directory to create a streamlined CSV file to model and insert data into Apache Cassandra tables.

data-modeling-with-postgres---etl

In this project, I applied what I've learned on data modeling with Postgres and built an ETL pipeline using Python. To complete the project, I needed to define fact and dimension tables for a star schema for a particular analytic focus, and wrote an ETL pipeline that transfers data from files in two local directories into these tables in Postgres using Python and SQL.

data-pipelines-with-airflow

A music streaming company, Sparkify, has decided that it is time to introduce more automation and monitoring to their data warehouse ETL pipelines and come to the conclusion that the best tool to achieve this is Apache Airflow. They have decided to bring you into the project and expect you to create high grade data pipelines that are dynamic and built from reusable tasks, can be monitored, and allow easy backfills. They have also noted that the data quality plays a big part when analyses are executed on top the data warehouse and want to run tests against their datasets after the ETL steps have been executed to catch any discrepancies in the datasets.

data-warehouse-s3-redshift

In this project, I applied what I've learned on data warehouses and AWS to build an ETL pipeline for a database hosted on Redshift. To complete the project, I needed to load data from S3 to staging tables on Redshift and execute SQL statements that create the analytics tables from these staging tables.

databricks-ds-path

desafio---conceitos-node.js

desafio-07-gofinances-web

desafio-08-fundamentos-react-native

desafio-1-2020

desafio-conceitos-do-react-native

desafio-conceitos-do-reactjs

desafio-database-upload

desafio-final-imers-o-dados-alura---filipe-macedo

Desafio final para o imersão dados Alura

desafio-fundamentos-node.js

docker-etl

fase02inicandogobarber

Inicio do gobarber

fase03-primeiro-proj-react

fase03-primeiro-proj-react-native

fase03continuandogobarber

Fase 03 do bootcamp continuação da API do Gobarber