Cheat sheet of important Docker commands by Yash Salvi via @TDataScience

He tried to list most of the commands, which you’ll find useful many times using docker. The commands follow logical order of pulling image, running container, deleting. Any core commands, which may be missed are more than welcomed in the comments!

Very useful.

How to Run Airflow Locally With Docker by Giorgos Myrianthous via @TDataScience

 A step by step guide for running Airflow with Docker on your local machine.

This is very clear and easy to follow. Well worth a bookmark.

Presto-powered S3 data warehouse on Kubernetes by @joshua_robinson via @Medium

Joshua Robinson offers up a tutorial on how to set up a Presto data warehouse using Docker that could query data on a FlashBlade S3 object store, and a follow-up tutorial that explains how to move everything, including the Hive Metastore, to run in Kubernetes.

This is very useful to read and might help you to achieve something quicker than you have planned.

WEBINAR: Managing the Machine Learning Lifecycle What's New with MLflow 6 June 2019

Sponsored News from Data Science Central

Managing the Machine Learning LifecycleWhat's New with MLflowThursday, June 6, 2019 | 10 am PST Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To solve for these challenges, last June, we unveiled MLflow, an open source platform to manage the complete machine learning lifecycle. Most recently at Spark + AI Summit in San Francisco, we announced the General Availability of Managed MLflow and the upcoming release of MLflow 1.0. In this webinar, we will review new and existing MLflow capabilities that allow you to: Keep track of experiments runs and results across frameworks. Execute projects remotely on to a Databricks cluster, and quickly reproduce your runs. Quickly productionize models using Databricks production jobs, Docker containers, Azure ML, or Amazon SageMaker Featured Speakers Clemens Mewald, Director of Product Management at Databricks Hosted by: Cyrielle Simeone, Product Marketing Manager, Databricks SAVE MY SPOT

Introducing R-Brain: A New Data Science Platform by @idigdata via @kdnuggets

R-Brain is a next generation platform for data science built on top of Jupyterlab with Docker, which supports not only R, but also Python, SQL, has integrated intellisense, debugging, packaging, and publishing capabilities.

Great article and it sounds like a great platform. I'm hoping to have a go with it next week if I can find the time to play.

WEBINAR: Modern infrastructure with NoSQL and containers - 31 August 2016

 

Modern infrastructure with NoSQL and containers

DATE: Wednesday, August 31, 2016

TIME: 1:00PM EDT | 10:00AM PDT

The best way to deploy and scale a NoSQL database is with elastic infrastructure, with containers being one of the most efficient platforms – deployed on-premise or in the cloud. In this webinar, we’ll discuss how development and operations teams can benefit from running NoSQL databases on containers, as well as the options for doing so.

We will discuss how running NoSQL on containers:

• Improves business agility
• Increases developer productivity
• Simplifies deployment and scaling

And, we will highlight options for running a NoSQL database with:

• Docker Swarm, Docker Machine, and Docker Compose
• Docker on Kubernetes or Apache Mesos (DC/OS)
• Docker on AWS, Azure, Google Cloud

FEATURED SPEAKER:

Shane K Johnson

Manager of Product Marketing, Couchbase

Register here

WEBINAR: Using Databases and Containers: From Development to Deployment - 24 February 2016

 


DATE: Wednesday, February 24, 2016

TIME: 1:00PM ET

In this talk we review what Docker is and why it's important to Developers, Admins and DevOps when they are using a NoSQL Database such as Aerospike, the high performance NoSQL Database. Persistence is a critical element for a successful multi-Container strategy.

We also cover the following topics:

• Using Docker to Orchestrate a multi container application (Flask + Aerospike)
• Injecting HAProxy and other production requirements as we deploy to production
• Scaling the Web and Aerospike clusters to grow to meet demand

This presentation led by Alvin Richards, VP of Product at Aerospike includes an interactive demo showcasing the core Docker components (Machine, Engine, Swarm and Compose) along with Aerospike’s integration. We hope you will see how much simpler Docker can make building and deploying multi-node Aerospike based applications.

Register here