Version 1 of Technical Best Practices of Azure Databricks based on real world Customer and Technical SME inputs
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Updated
Oct 6, 2023
Version 1 of Technical Best Practices of Azure Databricks based on real world Customer and Technical SME inputs
Collection of Sample Databricks Spark Notebooks ( mostly for Azure Databricks )
Project Y is a straightforward Landing Zones automated deployment tool dedicated to data processing.
A data pipeline project build on databricks and azure to demostrate lifecycle of a cloud data project.
Developed an end-to-end machine learning solution for predicting employee churn using Azure Databricks, leveraging Spark for data processing, MLflow for managing the ML workflow, and deploying the model using Databricks model serving.
An Internship Project Body Fat Estimator Deployed on Azure Cloud Platform
Using SAS to authenticate and access to ADLS Gen 2 from Azure Databricks
This project builds an End-to-End Azure Data Engineering Pipeline, performing ETL and Analytics Reporting on the AdventureWorks2022LT Database.
Practice with Azure Synapse Analytics/Databricks Pipeline
Sentiment Analysis for Tweets
This Repo provide code showcasing how to connect Pyspark (Azure DataBricks) to EventHub
This project exemplifies a robust Azure streaming data solution tailored for fitness data analysis, leveraging Azure's powerful ecosystem to deliver actionable insights and drive informed decisions in health and wellness management.
Making ODBC connection from Databricks (Azure Databricks) to Azure SQL Database with Azure AD User Access Token.
Notebook sample of Exploratory Data Analysis (EDA) for Prudential Life Insurance Sample Data
Ingested Tokyo Olympic data into Azure Data Lake using Azure Data Factory. Enhanced data quality with Apache Spark on Azure Databricks. Optimized SQL queries on Synapse Analytics, reducing execution time. Developed engaging Power BI dashboards, boosting user engagement creating KPI's with DAX.
Collection of data on Formula One Racing
Springboard Open Ended Capstone
This project gets data from Spotify API , ingests into kafka for streaming and processes it through spark streaming. All this is done on Azure.
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