OctopuFS library helps managing cloud storage, ADLSgen2 specifically. It allows you to operate on files (moving, copying, setting ACLs) in very efficient manner. Designed to work on databricks, but should work on any other platform as well.
Fluentd output plugin for Azure Datalake Storage Gen2 (append support)
Data Engineering Project on Supply Chain ETL. Creating a dynamic ADF pipeline to ingest both Full Load and Incremental Load data from SQL Server and then transform these datasets based on medallion architecture using Databricks.
Explore the Tokyo Olympics data journey! We ingested a GitHub CSV into Azure via Data Factory, stored it in Data Lake Storage Gen2, performed transformations in Databricks, conducted advanced analytics in Azure Synapse, and visualized insights in Synapse or Power BI.
This sample demonstrates how to create a Linux Virtual Machine in a virtual network that privately accesses a blob storage account using an Azure Private Endpoint.
Using SAS to authenticate and access to ADLS Gen 2 from Azure Databricks
Deploy apache spark in client mode on Kubernetes cluster, integrate with Jupyter notebook through Jupyterhub server.
POC projects working on Cloud Platforms
This is an End to End Azure Data Engineering project copying data from Rest API to Azure cloud.
ETL project with Spark and Airflow
COVID19-ADF is a project that leverages Azure services to collect, analyze, and visualize COVID-19 data. With seamless data integration and advanced analytics, it provides valuable insights into the pandemic's impact, enabling informed decision-making in the fight against COVID-19.
Implementation of most useful services of Azure Data Platform.
An end-to-end Netflix data engineering pipeline built on Microsoft Azure. This project ingests raw Netflix data, applies PySpark transformations , enforces data quality with Delta Live Tables, and orchestrates workflows via Azure Data Factory and Databricks.
Data files for azure cloud data engineering project
"Explore Formula 1 data analytics with this project. Leveraging the Ergast API, it utilizes Databricks Spark for ingestion, transformation, and analysis. ADLS acts as the storage layer, while Power BI visualizes the ADLS presentation layer. Uncover insights in the world of Formula 1 through powerful data analytics."
Explore the Paris Olympics data journey! We ingested a GitHub CSV into Azure via Data Factory, stored it in Data Lake Storage Gen2, performed transformations in Databricks, conducted analytics in Azure Synapse, and visualized insights in Synapse.
Add a description, image, and links to the adlsgen2 topic page so that developers can more easily learn about it.
To associate your repository with the adlsgen2 topic, visit your repo's landing page and select "manage topics."