How Zendesk Enables Greater Developer Productivity with AWS and Datadog
Customer Company Size
Large Corporate
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
- Productivity Improvements
- Digital Expertise
- Customer Satisfaction
Technology Category
- Platform as a Service (PaaS) - Connectivity Platforms
Applicable Industries
- Software
Applicable Functions
- Product Research & Development
- Business Operation
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
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Infosys achieves a 5–7 percent effort reduction across projects
Infosys, a global leader in consulting, technology, and outsourcing solutions, was facing significant challenges in application development and maintenance due to its distributed teams, changing business priorities and the need to stay in alignment with customer needs. The company used a mix of open source, home-grown and third-party applications to support application development projects. However, challenges resulting from distributed teams using manual processes increased as the company grew. It became more and more important for Infosys to execute its projects efficiently, so they could improve quality, reduce defects and minimize delays.
Case Study
Arctic Wolf Envelops Teamworks with 24x7 Cybersecurity Protection and Comprehensive Visibility
Teamworks, a leading athlete engagement platform, faced rising cyberthreats and needed enhanced visibility into its network, servers, and laptops. With software developers connecting from all over the world, the company sought to improve its security posture and position itself for future growth. The company had a secure platform but recognized the need for a more proactive solution to identify gaps within its technology infrastructure. Data exfiltration and malicious access were top concerns, prompting the need for a comprehensive security upgrade.
Case Study
Sawback IT and Datto Save Client From a Costly Mistake
Ballistic Echo, a software development house, faced a critical challenge when human error led to the deletion of thousands of lines of unique code. This incident occurred before the code was pushed to source control, resulting in significant loss of time, revenue, and work. The previous file-level backup solution they used was slow and inefficient, making it nearly impossible to manually recreate the lost work. The need for a more reliable and efficient business continuity solution became evident to avoid such disasters in the future.
Case Study
Opal Helps Customers Shine Thanks to Datto
SP Flooring & Design Center faced a ransomware attack that encrypted and locked their files. The attack was initiated through a compromised service account set up by an outside vendor. The ransomware infection was isolated quickly, but there was a concern about the extent of the data at risk. The company had backups in place but was unsure of how much information was compromised. The situation required immediate action to prevent further damage and restore the affected data.
Case Study
Zapier Aggregates Multiple Analytics in a Single Dashboard with the New Relic Platform
Zapier, a company that enables non-technical users to push data between hundreds of web applications, was facing a challenge in automating and provisioning servers for optimal performance. The company's environment consisted of 50 Linux servers on the Amazon Elastic Compute Cloud (EC2), a Django application split across several servers, and a backend consisting of a dynamic number of celery task workers fed by messages published to a RabbitMQ cluster. They also maintained a number of internal web services on nginx in front of Gunicorn and Node.js processes. Redis handled simple key and value stores, with logging handled by Graylog2 and ElasticSearch. However, they realized that no level of automation would be sufficient without an effective monitoring solution in place. They needed a tool that could provide immediate alerts when something was breaking and could be easily implemented into their environment.
Case Study
Pipeline Insight Case Study: YARCDATA
YarcData faced challenges in determining the conversion rates of prospects into customers through various marketing efforts and identifying the source of its leads. They wanted to know the percentage of opportunities in the sales pipeline that came from different marketing events, web downloads, or self-sourced sales opportunities. Additionally, they needed the ability to drill down into the data to guide where to allocate more marketing dollars based on the success of previous efforts. Previously, YarcData relied heavily on spreadsheets and Salesforce.com reports, which made it difficult to extract the exact information they needed. This reliance on spreadsheets represented about 70% of their data presentation.
