CI/CD for data projects: Why manual deployments are not good enough скачать в хорошем качестве

CI/CD for data projects: Why manual deployments are not good enough

During this webinar, Pierre Borckmans, a full stack engineer from Data Minded who is currently working with Conveyor: 🛠️ Discusses the typical lifecycle of a data project, from ideation to production ⚡ Tells you about how CI/CD can bring you confidence and peace of mind in that process 🍡 Shows you how Conveyor makes all of this easy and reproducible across all your projects 👁️‍🗨️Sneak peek on the blog: https://hubs.li/Q01pjRKV0 if you are building data projects of any kind (ETL, Spark, Dbt, ML, ...) and want to: 👉 Reliably build, test and deploy your projects 👉 Have a flexible yet robust way to standardize your CI/CD pipelines for data projects 👉 Do so, while following best practices Then this webinar is just for you! ⚡ Want to know more about Conveyor? It is a managed data engineering platform. Using Conveyor, enterprises can accelerate data product development. 👉 Setup your Conveyor cluster in your AWS account:    • Setup a Conveyor cluster in your AWS account   👉 Learn more at: https://www.dataminded.com/conveyor 👉 Request a Demo or, ask questions: https://www.dataminded.com/conveyor-c... 👉Read our blogs: https://blog.dataminded.com Conveyor helps you build data products! Starting from a blank sheet can be hard. That's why Conveyor supports you with templates, offers best practices, and promotes a cloud-native way of building data products. Without the hassle. Build your first real data product in hours or days instead of months. Conveyor deploys your data products and watches over them Building and deploying code is a single-line command and takes less than 2 minutes. You can easily create new environments in Conveyor to test your code in isolation before you promote it to production. Once deployed, Conveyor takes care of scheduling, logging, monitoring, and alerting. Conveyor is a fully managed, serverless, service that runs in your VPC and scales up and down magically. No need to think about cluster sizing, VM configurations, or software patches. Just focus on building the data product.