One of the biggest, most time-consuming parts of data science is analysis and experimentation. One of the most popular tools to do so in a graphical, interactive environment is Jupyter.
Combining Jupyter with Apache Spark (through PySpark) merges two extremely powerful tools. AWS EMR lets you set up all of these tools with just a few clicks. In this tutorial I’ll walk through creating a cluster of machines running Spark with a Jupyter notebook sitting on top of it all.
Continue reading Jupyter Notebooks with PySpark on AWS EMR
You’ll definitely want to read this if you’re using AWS Kinesis with Apache Spark to stream data, it’s been extremely valuable:
Recently I ran into a problem while working with Amazon EC2 servers. Servers without dedicated elastic IP addresses would get a different IP address every time they were started up! This proved to be a challenge when trying to SSH in to the servers.
How can I have a dynamic domain name that always points to my EC2 server?
Amazon’s Route53 came to mind. Route53, however, does not have a simple way to point a subdomain directly to an EC2 instance. You can set up load balancers between Route53 and your instance, but that’s a hassle. You can also set up an elaborate private network with port forwarding – yuck.
I wanted a simple way to set a Route53 subdomain’s
A record to point to an EC2 instance’s public IP address, on startup.
Enter go-route53-dyn-dns. This is a simple Go project that solves this problem. It is a small binary that reads a JSON configuration file and updates Route53 with an EC2 instance’s public IP address.
Included in the GitHub
README.md file is how to set everything up.
The project is here: go-route53-dyn-dns.