How Zendesk Enables Greater Developer Productivity with AWS and Datadog
Company Size
1,000+
Region
- America
Country
- United States
Product
- Datadog’s unified monitoring solution
- Datadog’s Application Performance Monitoring (APM)
- Datadog’s Trace Search and Analytics
Tech Stack
- AWS
- Kubernetes
- Datadog
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Digital Expertise
- Productivity Improvements
Technology Category
- Analytics & Modeling - Real Time Analytics
- Platform as a Service (PaaS) - Connectivity Platforms
Applicable Industries
- Software
Applicable Functions
- Business Operation
- Product Research & Development
Use Cases
- Factory Operations Visibility & Intelligence
- Predictive Maintenance
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
Zendesk is a San Francisco-based customer service software company that provides support and sales products designed to help companies improve their customer experiences. More than 170,000 businesses across many industries leverage Zendesk to build stronger relationships with their customers around the world. Zendesk is committed to delivering an always-on, high-performing platform to meet the demands of their diverse user base and deliver great customer service. To keep up with their customers’ evolving needs, Zendesk’s developers need the freedom to build new features quickly.
The Challenge
Zendesk was transitioning to a highly dynamic, container-based environment and needed a robust monitoring solution that integrated with AWS and Kubernetes. Their existing monitoring tools created silos between teams and required manual correlation of metrics, traces, and logs, which made it difficult to resolve issues. To keep up with their customers’ evolving needs, Zendesk’s developers need the freedom to build new features quickly. Historically, Zendesk had used a monolithic, on-premises architecture for its production workloads, while its nonproduction workloads ran on Amazon Web Services (AWS). This setup created a lot of friction for their developers and made it difficult to scale.
The Solution
Zendesk decided to transition its production workloads to a container-based environment that ran on AWS, giving developers the modularity they needed to work on new features and accelerate development. They chose Kubernetes as their container orchestration tool, which worked well in their new cloud-based production environment on AWS. As Zendesk shifted its production workloads to AWS, they also shifted to using Datadog as their sole monitoring solution. By switching to Datadog, Zendesk was able to view their environment as a whole, instead of piecing together information from multiple monitoring solutions. Datadog easily integrates with Kubernetes clusters and gives teams the visibility they need.
Operational Impact
Quantitative Benefit
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