In case you missed it, this blog post is a recap of Cassandra Lunch #22, covering deployment and administration tools for Cassandra. We will discuss a number of tools for the installation, configuration, monitoring, and administration of Cassandra clusters.

Introduction

Datastax Enterprise Graph (DSE Graph) is a NoSQL database tool that is optimized for storing objects and their relationships. It is a distributed database, just like Cassandra, and has many of the same advantages. It can scale to handle massive amounts of data, is optimized for high-speed transactions, and built for high reliability, just like Cassandra. DataStax enterprise graph specifically, also comes with access to other DataStax enterprise features, like DSE Search capabilities and security features.

In Cassandra Lunch #20, we discuss Cassandra read and write paths which is how Cassandra stores and retrieves data at high speeds. We won’t cover how Cassandra replicates data because that is its own subject, but we will take a look at these four sub-topics: Write Path, Update / Delete, Maintenance Path, and Read Path.

Today I’m going to walk you through how to deploy Kubernetes on AWS using Terraform. In Part 1 of this series, I mentioned that I had a tough time getting my terraform demonstration to work because of my auto-scaling configuration. That resulted in endless instances being spun up on my AWS account and prevented me from creating more due to the limit. This time around I was able to get my demo working successfully! The procedure and setup are different from my first attempt however I had an in-depth overview of Kubernetes in the first webinar so I will include them in the same series.

Introduction

What is model deployment?

Model deployment is the process that we take to put our trained models to work. It involves moving our model to somewhere with the resources to do serious processing. That place also needs the ability to receive or retrieve data to be processed. We place that trained model within an architecture that delivers data to the model for processing. It then retrieves and delivers or stores the results so that they can be used or seen by users. Similar choices need to be made about whether the model gets retrained, updated, or replaced during operation.