Learn by doing hand-on projects -> https://www.katacoda.com/sntsua
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Updated
Mar 23, 2023
DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations.
Learn by doing hand-on projects -> https://www.katacoda.com/sntsua
The primary objective of the project is to build a Bash Command Line tool that performs useful data preparation tasks such as cleaning, truncating, and sorting data.
Redpanda Console is a developer-friendly UI for managing your Kafka/Redpanda workloads. Console gives you a simple, interactive approach for gaining visibility into your topics, masking data, managing consumer groups, and exploring real-time data with time-travel debugging.
Airflow DAGs for the Manifold (TUL Website) application
Metadata management in Go
This project serves as a showcase to demonstrate the implementation of data streaming pipeline. It highlights the integration of key technologies like Apache Kafka for data streaming, Apache Spark for real-time data processing. The project aims to provide a comprehensive example of building and deploying real-time data processing applications.
Azure Team Data Science Process project template
DataOpsを活用したオープンデータ利活用に関する報告書
Airflow Data Processing Pipeline for TUL Catalog on Blacklight Data
IntelliJ plugin for editing DataKitchen Platform recipes.
🚀 instant jupyter notebook autolaunch with $HOME mount using docker 🐳