Big Data in AWS:
Spend Less While Storing and Analyzing More

Whether you’re building new products with machine learning and AI or just processing your customer data, ensuring timely access to relevant data insight has become table stakes for today’s companies. However, designing systems for scale can come with growing pains.  Whatever solutions make up your stack (S3, EC2, Redshift, Athena, Elasticsearch, a managed ELK vendor, or something else), they have similar challenges: complexity, high costs, and poor predictability. 

In this 30 minute webinar, Pete Cheslock, VP of Product at CHAOSSEARCH, and Erik Peterson, Founder of CloudZero, discuss how to tackle the growing pains that come along with building cloud-based businesses that rely on processing and/or storing large amounts of data. 

• Amazon Big Data cost saving best practices & techniques
• How to decide when the cost of running your own ELK stack is prohibitive and when you should turn to a third party. 
• Common AWS architecture and configuration changes to make to reduce the cost of Big Data on AWS.
• How to implement cost monitoring to detect unexpected costs.
• When to leverage serverless infrastructure to reduce your data costs.
• Data analytics techniques and tips