Geodata Optimizes Log-Processing Costs by Using Amazon EC2 A1 Instances

Geodata, a market leader in Norway for geographical data and technology, is the Norwegian distributor for Esri, a global leader in geographical information systems (GIS) software. Geodata has been an Amazon Web Services (AWS) customer for a decade and often evaluates new Amazon Elastic Compute Cloud (Amazon EC2) instance types to identify innovative, cost-effective ways to process its workloads. The opportunity to experiment and optimize workloads was what initially attracted Geodata to explore Amazon EC2 A1 instances. “We routinely try new AWS instance types as soon as they’re available,” says Ray Skoog, system developer for Geodata. “The low barrier of entry makes it easy for us to try them and satisfy our curiosity.”

Amazon EC2 A1 instances are powered by Arm-based AWS Graviton processors, which deliver up to 45 percent cost savings for scale-out workloads. “I’m thrilled that AWS is offering Arm-based processors. They hold their own against other processor types that we use and run our workloads with no problems,” says Skoog. A1 instances are the first Arm-based instances from a major cloud vendor and provide all the benefits of the cloud, including on-demand capacity, high security, and scalability. “A1 instance types work well with our streaming log-data application. Processing in the cloud works to our advantage because if the log data increases in velocity or volume, we can add instances to match our need.” 

Since A1 instances work well with streaming data, Geodata can use them to process log-server activity for near-real-time and long-term purposes. The company employs A1 instances using Amazon EC2 Spot Instances, which take advantage of unused Amazon EC2 capacity in the AWS Cloud and are available at up to a 90 percent discount compared to On-Demand Instance prices. Geodata has experienced 10 percent cost savings after switching its logging workloads from Amazon EC2 R4 to A1 instances. Skoog says, “Amazon EC2 A1 instances improved our log-processing stability and, through the use of Spot Instances, were more cost-effective.”

Skoog found that shifting Geodata’s log processing, system monitoring, and data-streaming workloads from R4 to A1 instances was both convenient and easy. Geodata uses the Amazon Linux 2 operating system and a series of Python scripts to prepare a custom Amazon Machine Image (AMI), which launches servers for custom processing, job management, and data processing. "It was zero effort to switch to Arm processing," Skoog says. He appreciates how AWS fosters innovation and experimentation. “The AWS Cloud is simply the most flexible and convenient. Cost savings are not the only reason to be on the cloud. It’s also important to have options that allow us to quickly and easily try new things.”

To learn more, visit aws.amazon.com/ec2/instance-types/a1.