Nicolas Brousse, a Cloud Technology Leader, became Director of Operations Engineering at Adobe (NASDAQ: ADBE) after the acquisition of TubeMogul (NASDAQ: TUBE). As TubeMogul's sixth employee and first operations hire, Nicolas has built and grown Adobe/TubeMogul's infrastructure over the past ten years from several machines to over eight thousand servers that handle ±350 billions requests per day for clients like Allstate, Chrysler, Heineken and Hotels.com.
Adept at adapting quickly to ongoing business needs and constraints, Nicolas leads a global team of site reliability engineers, cloud engineers, software engineers, security engineers, and database architects that build, manage, and monitor Adobe Advertising Cloud's infrastructure 24/7 and adhere to "DevOps" methodology. Nicolas is a frequent speaker at top U.S. technology conferences and regularly gives advice to other operations engineers. Prior to relocating to the U.S. to join TubeMogul, Nicolas worked in technology for two decades, managing heavy traffic and large user databases for companies like MultiMania, Lycos and Kewego. Nicolas lives in Danville, CA and is an avid fisherman and aspiring cowboy.
- Built from the ground up and lead a global team of 60 operations engineers (FTE, vendors worker, contingent workers)
- Global Team with staff in 4 different timezone (Ukraine, China, India, US) to ensure 24/7 support (Follow The Sun)
- Support a ±250 global product and engineering team
- Built and support a ±8,000 assets infrastructure with 6 datacenter locations in US, Europe, and APAC.
- Built a multi-cloud solution with cloud bursting capabilities to support product scale and latency requirements
- Design and deployed a solution to deliver services in Mainland China with a POP in Beijing and direct connectivity to HKG Data Center
- Responsible for infrastructure P&L with goal on TI cost as percent of Gross Profit
- Define strategy and tactical plan to ensure SOC2/ISO/SOX compliance
Technologies: Linux, Puppet, Python, Ruby, PHP, Java, Go, Jenkins, Graphite, Ganglia, Grafana, Nagios, Sensu, AWS, HAproxy, OpenStack, Zookeeper, Kafka, Couchbase, MySQL, ElasticSearch/ELK, Splunk, HBase, Hadoop, Ubuntu, Debian, Docker, Container, Kubernetes, KVM, TCP/IP, Open vSwitch, etc.
Product and engineering teams’ speed of producing high-quality results is critical to ensuring enterprise competitiveness. Additionally, one can observe an increase in IT systems complexity driven by the adoption of service-oriented architecture, micro-services, and serverless. Therefore, many large enterprises benefit from a mono-repository for source code management because of the improved team cognition that results from eroding barriers between teams and from influencing enhanced teamwork quality. This paper, first, reviews the characteristics of a multi-repositories structure, a monorepository structure, and a hybrid model. Second, it discusses why some manage source code in a multi-repositories structure, either by choice or because of the organic evolution of large enterprises. Third, it reviews how mono-repositories in large teams, beyond the technical arguments, can drive high efficiency and enhanced product quality through improved team cognition.
Over the past few years I had the unique opportunity to see a start-up, TubeMogul, going through hyper-growth, an IPO, and an acquisition by a fortune 500, Adobe. In this journey, I was exposed to a lot of technical challenges, and I work on systems at an astonishing scale, i.e., over 350 billions real-time bidding request a day. It allowed me to build some strong personal opinions on the role of an SRE and how they can help transform an organization. This post cover self-healing design, forecasting algorythm, anomaly detection, risk classification, and provide real use cases from Adobe SRE teams.
Adobe Experience Cloud is a collection of best-in-class solutions for marketing, analytics, advertising, and commerce. All integrated on a cloud platform for a single experience system of record. The Adobe Experience Cloud's SRE team works hand-in-hand with the Product and Engineering teams to build dependable services. In this presentation you will learn how the team leverage Adobe's artificial intelligence and machine learning engine to build predictive auto-scaling and self-healing services.