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Listen to most AWS proponents and you'll likely hear how easy it is to use the technology – just enter your credit card information, spin up a VM and get started in the public cloud. While that may be true for test-and-development environments, AWS customers establishing enterprise-grade AWS environments don't always agree with the ease-of-use claims.
Nearly all new services launched here this week aim to simplify an enterprise's work in Amazon Web Services (AWS) – from application development through management, including Amazon QuickSight and AWS Inspector, which help IT teams quickly set up business intelligence products and determine security and compliance holes, respectively.
"We really hope that we're making it simpler for you to build the applications you want to build," said Werner Vogels, CTO at AWS during his keynote.
And this doesn't even bring into account existing services from AWS that have the word "simple" right in the name, including Amazon Simple Storage Service (S3), Amazon, Simple Notification System, Amazon SimpleDB and Amazon Simple Email Service.
But is AWS really all that "simple?"
Some services, such as DynamoDB or Elastic Block Storage (EBS), offer companies a fairly uncomplicated path to cloud-based storage and databases, but other services, including Elastic Compute Cloud (EC2) can require more work.
"EC2 instances are easy to launch and we use a ton of them," said Mike Daly, CTO and co-founder of Spokeo, a Pasadena, Calif.-based people search website. "But of the AWS services, EC2 simply requires the most work since you are installing software on servers."
On the other hand, SaaS and database as a service products such as Kinesis and DynamoDB are easier to use "because they save us from having to handle installation and maintenance – the 'undifferentiated' work," he added. Spokeo uses a range of AWS products, including EC2, Relational Database Service, DynamoDB, S3, Redshift and Route 53, to name a few. Daly is also interested in Amazon Elasticsearch service, but said it's still very new.
Cost structure queries
It's not the difficulty of getting started with AWS that concerns one IT consulting director in charge of data analytics for a large media company. It's getting a handle on the pricing structure for some AWS big data services.
"It will absolutely be easy [to work in AWS], especially for proof-of-concept projects," he said, adding that using AWS could also allow his data scientists to circumvent IT completely to get projects off the ground more quickly. But the cost structures for such tools aren't as clear cut.
"I need to determine the budget for these new projects and don't want overspend or limit my team to a certain number of queries to control costs," he said.
He hasn't pinpointed which AWS analytics service would best meet the company's needs, but is interested in Amazon Machine Learning for predictive analysis and big data projects.
AWS Lambda saves the day
Other attendees noted that AWS Lambda and new capabilities which became available this week, such as Virtual Private Cloud support and scheduled functions, will make their work lives easier, and can help speed up development of "serverless" systems .
"I'm interested in Lambda," said Mike Lazarus, chief scientist at ID Analytics, a consumer risk management software provider located in San Diego.
ID Analytics develops complex machine learning systems that run on thousands of variables and terabytes of data to produce highly tuned estimates of risk, such as identity fraud detection.
"AWS Lambda enables a new and interesting opportunity to simplify the implementation such systems in a novel and scalable way," Lazarus said.
He also applauded Amazon for its increased focus on security this year and noted he'd like to see AWS offer an end-to-end security tool to round out its products in the near future. But seeing big-name corporations publicly discuss their reliance on AWS here this week is a step in the right direction, he added.
Michelle Boisvert is an executive site editor in the Data Center and Virtualization group. Contact her at email@example.com.
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