This article provides a checklist of steps and considerations when deploying a data engineering project to production, covering infrastructure setup, testing, monitoring and more. Following this checklist will help ensure a smooth deployment and transition to production systems.
This Docker crash course for data scientists covers Docker fundamentals like architecture, images, containers, storage, networking. It then explores using Docker for data science workflows including environments, model training/deployment, notebooks. Finally it discusses best practices for optimization, orchestration, security, and monitoring.
Platform engineering distinguishes itself through a systematic approach towards designing, building, and maintaining platforms, providing a solid foundation for multiple applications and services.
In this article, we delve into an overview of OLTP and OLAP, explore their key differences, use cases, and offer insights into when one should be chosen over the other.
The data lakehouse architecture is designed to provide the scalability and flexibility of a data lake while also providing the reliability and governance of a data warehouse.
