Developed while following the dbt Developer Path, using dbt Cloud
interface and Snowflake as the Database manager and cloud resource provider.
Version control, testing, documentation, macros, incremental models,
jobs refactoring and use of packages are some of the things reflected here.
Go to the repo for more information, code and configuration files.
My version of the app built in
the Hands-On Essentials: Data Application Builders Workshop,
with the purpose of using Snowflake as a back end, Streamlit as front end and Fruityvice API to get information about the fruits.
This includes: Streamlit (Python), SnowSQL, REST APIs and CLIs among others.
Built a real-time data ingestion pipeline between AWS S3 and Snowflake
using Snowpipe and SQS. Designed an automated flow to load CSV and JSON
files from S3 into Snowflake tables with minimal latency, leveraging
storage integration and IAM roles for secure cross-platform connectivity.
An overview of a COVID-19 disease dataset about
satistics of vaccinations and deaths using SQL best
practices to import it into a DataBase and clean it with
the help of pgAdmin 4 open source tool and PostgreSQL.
Relational Database Management System.
This project applied core data science tools to predict customer purchasing behavior.
I used Python, Pandas for data manipulation, Scikit-learn for model building and evaluation,
and Jupyter Notebook as the development environment.
This project is about managing a Nashville Housing dataset obtained from Kaggle
using SQL in order to get meaningful insight out of it. Data is Trasformed and optimised for further Analysis.
A powerful and interactive Dashboard built using Tableau Public Visualization tool to showcase the house Sales in King County, Washington from May, 2014 to May, 2015.
Datasets from Kaggle that are used to visualize and interpret the AirBnB house prices by region and time
of the year 2016 in Seattle, Washington. Excel is used to transform the datasets into one to load to Tableau Public.
Cleaning a Dataset using Pyhton language
and Pandas library in Jupiter Notebook, using best practices and following guidelines to get a clean dataset as the output. This Absenteeism Excercise will be the way to showcase data wrangling.
Obtaining data using iTunes API and scraping methods using Pandas in Python is a reliable way of gettting a meaningful dataset.
Getting constant information about a page using Beautiful Soup library in Python. Saving your own data to a file for further analysis is the basis for having really
good data to analyze.
A Power BI report using data extracted from the European Data webpage. Data from a JSON file is imported
into Power Query and cleaned using the advantages of this tool inside the Power BI Desktop app, which is used to visualize this interactive Dashboard.
A Sales Report in Power BI using data extracted from the Adventure Works Database, by Microsoft. Data imported from csv and Excel files
into Power Query and then visualized using the Power BI Desktop app. The result is a powerful Dashboard.
