Real-time ADLs and Fall Detection implement TensorFlow
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Updated
Nov 7, 2018 - Python
Real-time ADLs and Fall Detection implement TensorFlow
Activity Assistant provides a platform for logging, evaluating and predicting Activities of Daily Living for Home Assistant.
Contains data preprocessing and visualization methods for ADL datasets.
Fluentd output plugin for Azure Datalake Storage Gen2 (append support)
HDInsight provider for Airflow
Tutorial on setup of online and offline feature stores in Azure Machine Learning (Azure ML), using command line interface (Az CLI)
File Source to read data from distributed file system
Projeto para Extração de dados de um SQL Server e Carga em um Data Lake Storage Gen2 da Azure (ADLS) usando Airflow Astro CLI e spark para movimentação dos dados entre as camadas tipo medalhão com delta lake.
End-to-end Azure Lakehouse + Operational Analytics demo with IaC (Bicep/ARM), secure data landing (ADLS, Fabric, Synapse), and can’t-mess-it-up automation (Cosmos upserts + DevOps). Scales with skills, satisfies auditors, and proves automation can be boringly reliable (that’s the point). Runs cleanly every time ... even under interview pressure.
Terraform module for terraform-azure-adls-gen2
This project demonstrates a complete, real-world implementation of a modern data engineering pipeline using the Azure cloud ecosystem. Built around the AdventureWorks dataset, the pipeline follows the Medallion Architecture (Bronze → Silver → Gold), enabling structured data refinement from raw ingestion to business-ready insights.
Built an end-to-end data pipeline using Azure, Snowflake, and Power BI based on the Medallion Architecture (Bronze–Silver–Gold). Implemented automated ingestion, data cleaning, and business-ready transformations to deliver customer sales insights and churn analysis via interactive dashboards.
An end-to-end ETL pipeline that extracts data from an Azure SQL Server database, transforms the data using Databricks, and loads the transformed dataset into Azure Data Lake Storage (ADLS).
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