Nike's online business is a significant and growing part of its revenue portfolio. In 2011, Nike.com and its associated eCommerce applications brought in about $450 Million in revenue in the United States and about $200 Million plus in Europe. However, prior to the company’s bringing in New Relic in 2011, incident notification, performance triage and management were significant challenges for the nike.com eCommerce team. There was very little insight into production application server health. And although the team was using tools, they didn’t provide the detailed insight the team needed to ensure performance of Nike’s rapidly growing eCommerce platform. As Kevin Bartholomew, Nike Web Support Production Manager says, “Our main challenge was insufficient insight into our eCommerce applications — no single pane of glass we could look at to see what was going on under the covers. One tool told us the network was down. Another told us the site was slow. We didn’t have anything to see where the problems were, and definitely not what was causing potential issues.”

Cinchcast, a company that provides a cloud-based audio management platform, was experiencing rapid growth as more individuals, businesses, and organizations discovered how easy it was to create and deliver audio content using their solution. On a monthly basis, Cinchcast’s media property, BlogTalkRadio, was hosting more than 35,000 hours of original content, attracting over 13 million unique visitors, and powering 15 million streams and 175 million ad impressions. The team at Cinchcast knew that site performance was critical to participant engagement and saw it as a critical success factor for their continued rapid growth. They needed more insight and visibility into application performance to understand which components in the application were taking significantly more time than others and how fast the website loaded from an end-user perspective. Using multiple tools from multiple vendors was time-consuming and did not offer a seamless monitoring experience.

Bleacher Report, a digital sports media outlet, has over 26 million readers who rely on the site for news about their favorite sports and teams. During peak times like the Super Bowl, World Series, March Madness, etc., fans flood the site for up-to-the-minute information and dialogue. High performing mobile access is especially critical. March is a particularly busy month in terms of traffic to the site: NCAA March Madness is right on top of the NFL free agency and the NBA trading deadline. During March Madness, the site processes 50,000 requests per minute (RPM) for the core service. With a self-imposed uptime goal of 99.9% and striving for 99.99% uptime, the B/R engineering team needed a real-time, all-the-time, application monitoring tool.

Fanzter, a private software company, was under pressure to maintain high performance for its products, introduce new features, and increase application stability. The company's products, which include Coolspotters.com, CoolPapers, Streaks, and Summizer, served over 20 million users in 2011, serving more than 32 million page views each month. High performance for traditional and mobile access was essential to their success. In the early days of Coolspotters, the team analyzed Rails application logs daily to determine the areas with slowest performance. They also tried to correlate the slow query logs from the database with the actions that triggered them. These methods were time consuming and far from foolproof. Fanzter needed a better way to analyze performance, to find and fix performance problems.

ProSoft Technology, a company specializing in communication solutions for industrial automation and control applications, was looking to develop a new cloud-based communication platform for the Industrial Internet of Things (IIoT) market. The company wanted to provide its customers with a secure, flexible, and easy-to-use platform that would allow them to remotely monitor and manage their industrial systems. However, ProSoft faced several challenges in developing this platform. They needed a solution that would allow them to quickly and easily develop and deliver new protocols, processes, and technologies that meet the needs of their industrial customers. They also needed a solution that would provide them with the flexibility to add additional services as their customers' needs evolve.

TrustBills, a German-based auction platform for trade receivables, faced several challenges in its operations. The company had to comply with stringent EU data privacy and compliance regulations, which required them to run their own Kubernetes cluster instead of using a managed service. This was further complicated by the need to protect and secure customer data in a security-conscious market. TrustBills spent 10 months testing open source container storage solutions but failed to identify a solution with sufficient levels of scalability, resilience, and security. The company needed a solution that would allow them to efficiently allocate resources, provide performance guarantees for each service, and ensure that if one service was compromised, not all other services would be affected.

Beco, a company that delivers real-time space analytics to connect workforces to physical spaces, faced challenges in processing large amounts of data collected from connected devices. They needed to keep important databases like Kafka, Cassandra, and PostgreSQL highly available when using containers. The default setting of DC/OS pins stateful services to a single host, which posed a risk to high availability. The company needed a solution that could handle stateful services in a reliable, scalable fashion.

Cloud Provider, a cloud hosting, infrastructure, and services company, was facing challenges in offering scalable solutions to its customers. The company's customers, who are mostly not tech-savvy, were finding it difficult to migrate from a shared hosting environment to a virtual machine. The company wanted to offer a solution that would allow its customers to scale their operations without having to manage anything. The main challenge was ensuring the scalability of the platform, especially for stateful apps like WordPress. For these apps to function properly, it was necessary that the data volume is available to multiple physical hosts at the same time. This was a requirement that many storage vendors did not offer.

Fractal Industries, a software company that applies artificial intelligence to solve complex, real-world problems at scale, faced a significant challenge in managing its stateful services. The company's platform, Fractal OS, needed to run in multiple environments, including their own multi-tenant SaaS infrastructure, a customer's data center, or their VPC in Amazon, Google, or Azure. However, most container orchestration platforms are built for stateless services, which are easy to scale elastically. Existing persistent storage and data management solutions didn't work across clouds and on-premises data centers, a hard requirement for Fractal's platform. Furthermore, running multiple data services at production scale required significant expertise in each service to provide high availability, backups, and disaster recovery.

MightWeb, a hosting company, needed to instantly scale to meet customer demand, which was challenging for the many stateful services they offer like WordPress, MySQL, MongoDB, Magento, and WooCommerce. Most persistent storage options for containers don’t provide both high performance storage for applications like MySQL, and multi-writer shared volumes for WordPress, both hard requirements for typical hosting customers. They needed a solution that could provide high availability, the ability to take snapshots, and the ability to scale.

Lix, a study platform for university students, was looking for a way to improve their platform's responsiveness and speed. They wanted to use containers to build a high-quality platform, but they faced challenges in running and managing data services like Elasticsearch, Cassandra, and Postgres in containers. Initially, they manually managed node labels for these services, which was time-consuming and prone to errors. Moreover, when a machine failed, they lacked the ability to temporarily move the Cassandra pod to another machine while the cluster recovered.

ReachForce, a company in the marketing automation space, was facing several challenges with their AWS data center. The data center, comprised of four environments (Dev, QA, Performance Lab, and Production), consisted of about 215 EC2 instances. The company had re-implemented their SaaS offering in a micro-services architecture, using a separate AWS instance for each microservice. However, this led to the need to maintain over 200 Linux OS instances, and their average CPU utilization was significantly below best industry practices. Additionally, their AWS EBS storage was severely under-utilized, with only 25% of purchased capacity being used. At the same time, EC2 instances were over-provisioned by 100%, increasing the cost of operating the ReachForce SaaS platform.

Aurea Software, the software engineering arm of ESW Capital, needed to create a single, multi-tenant Kubernetes platform capable of handling database workloads for 80 different SaaS companies. The company faced several challenges in achieving this goal. Traditional software-defined storage systems like Ceph and GlusterFS did not integrate well with Kubernetes or scale to the required levels. Furthermore, they were limited to only 40 EBS volumes per EC2 instance when using Amazon EBS for container storage, requiring them to overprovision VMs by 5x. The company also had to deal with the challenge of running their Kubernetes clusters multi-tenant, which meant that they had 80 different companies running on the same cluster, all simultaneously. This posed a significant challenge in isolating resources within the Kubernetes cluster, so that they do not interfere with each other.

Naitways, an IT service provider based in Paris, France, was facing challenges in scaling to meet customer demand, especially for the many stateful services they offer like WordPress, Drupal, Joomla, MySQL, & Redis. Most persistent storage options for containers didn’t provide both high performance storage for applications like MySQL, and multi-writer shared volumes for WordPress, both hard requirements for typical hosting customers. They needed a solution that could provide scalable, easy-to-use container-as-a-service offering and hosted web applications.

Inhibrx, a company that develops multivalent costimulatory agonists, checkpoint inhibitors, and therapeutics to invert the tumor microenvironment toward local immune activation, was facing several challenges. They did not have a formal tracking system, which resulted in plasmid maps being distributed across multiple scientists’ computers. This made it difficult to find the right plasmid map and often involved walking through the lab to find the right scientist. Additionally, spreadsheets were used to track requests and information about plasmids, which required extensive manual search and limited user compliance. Experimental notes were taken using a shared paper lab notebook, making it difficult to find experimental details and extract insights.

Adicet Bio, a company that employs gamma delta T-cells to target solid tumor cancer, faced several challenges in managing their data. Their data was scattered across paper notebooks, employee computers, and public drives, making it difficult to access and secure. Their previous data management platform struggled to provide varying levels of access permissions to data for the stratified teams across their various sites. As Adicet rapidly scaled their staff size and the complexity of their research and development process, they needed to be able to organize and standardize their data capture and record keeping.

Agenus, a global biopharmaceutical company, faced several challenges due to the lack of unified systems and a globally distributed R&D team. The absence of a centralized system made it difficult for scientists to find and share information effectively. The global distribution of the R&D team further impeded team collaboration. The complexity of R&D workflows exacerbated difficulties in tracking samples and experiments.

Zymergen, an industrial biotechnology company, was facing challenges with its custom software systems. The company had many siloed pieces of custom software, making it difficult for scientists to enter and extract data. The strain optimization and fermentation & production teams couldn’t share complete experimental context due to the disparate systems. Moreover, Zymergen’s systems couldn’t scale with their rapidly growing company. The company needed a solution that could integrate their custom tools and provide a central platform for data entry and extraction.

Arcturus Therapeutics, a leader in RNA medicines, faced several challenges in their research process. The lack of standardized molecular design tools hindered their ability to collaborate effectively. Their legacy Electronic Lab Notebook (ELN) was cumbersome and led to low usage among scientists. It took an average of 10 days for experimental data to be entered into the system, if at all. Additionally, the absence of a standard data store and sequence repository scattered data and prevented the capture of institutional knowledge.

Bolt Threads faced several challenges in their quest to develop new, high-quality biomaterials. Their project data was spread across more than ten systems, creating information bottlenecks and making it difficult to find and aggregate data from upstream experiments to inform downstream production processes. Their existing information management system was not flexible enough to adapt to Bolt’s ever-changing, materials-specific workflows. Legacy tools required time-intensive, manual data capture, and made collaboration logistically challenging – all of which slowed progress and hindered speed of innovation.

Rubius Therapeutics faced several challenges in their cell engineering processes. Their manual record-keeping systems were disorganized and cluttered, making it difficult for teams to collaborate effectively. The company's legacy request management protocols were inefficient, requiring scientists and managers to sift through scattered records on cell cultures and quality control assays. Furthermore, their engineering processes were out of sync and hard to track due to decentralized information storage systems and unreliable reporting on project progress.

Incyte’s antibody discovery group was starting from the ground-up and knew it would be pivotal to deploy a world-class informatics system as soon as possible. With a growing team and fluid processes, the antibody discovery group needed a flexible system, or else their data would be unreliable and difficult to track. Being able to work with external collaborators (including international collaborators) was a must.

Synlogic, a company developing microbe-based therapeutics, faced several challenges in their operations. Their complex workflows were being sketched out step-by-step on paper, which hindered collaboration and reproducibility. Without a workflow system linked to a registration system, it was difficult for Synlogic to trace the lineages of their candidates. Additionally, placing requests, uploading results from instruments, and collating data across experiments were cumbersome and unreliable without unified, intelligent systems.

Zymergen, an industrial biotechnology company, faced several challenges as it scaled rapidly. The scientists at the company were having difficulty collaborating and sharing information due to the lack of an efficient system. The growing R&D teams needed an informatics solution that could quickly adapt to organizational changes. Moreover, Zymergen has extensive custom software and instrumentation, so they needed a platform that could integrate easily with their internal tools.

Obsidian Therapeutics was facing several challenges with their previous Electronic Lab Notebook (ELN). The system was clunky and didn't allow for linkages to registered samples, which hindered adoption and experimental detail. Without a formal registration system, Obsidian scientists couldn't draw connections between results data and upstream entities. Their legacy sample tracking system was unintuitive and saw low scientist usage, leading to data loss and compliance concerns.

Syngenta, a global leader in agricultural science and technology, faced several challenges. One of the main challenges was balancing customization and standardization. Syngenta wanted standardized data for cross-company analysis, but each department had its own preferences and needs for recording and recalling data. Another challenge was collaboration across the globe. Handoffs between different geographies, languages, time zones, and areas of expertise while using paper records led to bottlenecks. Lastly, managing many regulatory requirements was a challenge. Different countries maintain complex, shifting regulatory requirements around product safety that all need to be met.

Cue Biopharma, a company that designs novel biologics to modify the immune system to fight diseases, was transitioning from a research-centric organization to a clinical-stage organization. This transition involved bringing early development activities in-house to achieve greater control over the types and quality of work done by their teams. However, they faced challenges with non-standardized data capture, barriers to communication and handoffs between teams, and the need for a digital infrastructure to support their growth. They were using shared spreadsheets to manage information, which posed risks such as unintended edits and duplicates. As the company tripled in size, it became increasingly difficult for key pieces of information to efficiently flow through the organization.

Anagenex, a drug discovery company, is combining DNA encoded library (DEL) technology with machine learning (ML) to accelerate the traditionally time- and labor-intensive portion of drug discovery. However, they faced several challenges. Treating DNA sequencing readouts as an intermediary step to understanding the molecule of interest is an indirect use case, creating an additional layer of complexity. Each experiment can introduce batch effects that mask the biologically relevant effects and confuse the machine learning process. Anagenex needed a robust solution that could start tracking from the very first experiments and scale quickly as the company grows.

Pairwise, a company that uses CRISPR and gene editing to develop new varieties of fruits and vegetables, faced several challenges. The nature of plant genetics meant that each plant had its own phenotypic and genotypic characteristics, requiring individual tracking. This resulted in sample sets often containing hundreds of entities, each with its own optimization needs and custom steps. This is significantly larger than a typical biopharma sample set. Additionally, plant-based workflows required more complex infrastructure, more team handoffs, and longer timelines compared to cell-based workflows. Pairwise needed a centralized, easily-accessible way to organize and communicate experiment data to every team, from discovery to development. Program leaders also needed to turn thousands of data points into insights to drive decision making. They needed pre-computed reports aggregating metrics for successful plants each week to move forward with.

Inzen Therapeutics, a biopharma startup, was facing challenges in integrating wet lab and informatics teams. The scientists were using different terminologies to describe the same processes and data, which was slowing down data integration and discovery. They were unable to track samples through the entire experiment lifecycle, which hindered their understanding of the cell of interest. As a small startup with limited resources, they needed a solution that was intuitive to use and easy to maintain.