DB Schenker wanted to offer WMS solutions with a faster time-to-market. Their ambitious plan included rapid rollouts in a complex global landscape of several hundred warehouses spanning multiple regions. To meet their business objectives, they needed a low-cost system that included standard WMS solutions for small and simple processes to enable quick onboarding. The system also needed the flexibility to support multiple verticals, value-added services and scalability. They also required support for multiple languages across countries.

Tastefully Simple experienced strong sales growth requiring higher efficiency through the DC to keep pace. They were in need of process modernization and a scalable solution. Priorities included better capacity utilization, picking efficiencies for both pick to light and batch, improved kitting, product and lot # tracking, and optimal cartonization operations. In addition, it was important that a new WMS system could easily integrate with their existing IT environment.

Atlantic Dominion wanted to increase customer service capabilities to keep up with the needs of their growing retail base. Their plan was to improve order visibility and increase efficiency in warehouse and distribution operations. The company needed better capacity utilization and picking efficiencies in their piece pick and cartonization operations. In addition, they needed a sales tax solution to manage the high degree of complexities and challenges in dealing with state specific duties/tax for cigarette stamps generation and related reverse logistics.

Greenhouse’s internal log-management system was difficult to maintain and wasn’t able to scale with the company’s rapid growth. Greenhouse was looking for a way to get the insight it needed from its log data to keep its software services optimally running, and to troubleshoot issues as soon as possible to minimize impact to its end users.

Monitoring to support products such as mobile social games and service quality. As the organization has diversified and expanded, log viewing and rights can no longer be managed at the organizational level, but must be made stricter and assigned by product staff. At the same time, a large scale transition to the cloud made it necessary to ensure that the monitoring system, such as the log management and scalability, analysis functions, etc., be compatible with the cloud environment.

Founded in 2009, PagerDuty has grown rapidly and is now a leader in digital operations management. More than 19,000 customers across the globe rely on PagerDuty to deliver a better digital experience by identifying issues and opportunities in real time and bringing together the right people to fix problems faster and prevent them in the future. As an innovative tech company that processes billions of events to support its customer use cases, PagerDuty relies on real-time data to understand performance—from the platform’s core infrastructure to several hundred microservices running in AWS to efficiently manage customer inquiries and support operations. Data intelligence is essential for PagerDuty to deliver on its SLA commitments for a high-quality customer experience. With the desire to gain operational insights, PagerDuty chose a traditional log monitoring solution. The vendor proved unreliable in supporting PagerDuty’s significant data ingestion volume, which introduced gaps in historical data. The inability to dynamically scale with PagerDuty’s business needs created an untenable situation. PagerDuty no longer had confidence in the quality and accuracy of its query search results. Ultimately, the company concluded that the vendor wasn’t serving its needs.

Hudl needed a way to collect and centralize massive amounts of data generated by its modern applications, cloud platforms, servers, network devices, and endpoints to rapidly identify and remedy performance issues. The company realized that its vast data requirements and the fast-moving, distributed nature of its services called for a move from a monolithic application and development architecture to a microservices architecture. Hudl was conducting an average of 10 deployments a day, which has since risen to roughly 25 deployments a day. The team needed to know quickly the cause of any availability or performance issues and how to rapidly remedy the situation. Hudl’s customers generate an enormous amount of customer analytics data, with events created every time someone plays a video or loads a new screen, either online or within the app. With every event, new data is generated to capture what the user is doing at the time, as well as which aspects of the new feature are popular – and which are not. Hudl’s development and operations teams needed a better way to collect massive amounts of data from applications, cloud platforms, servers, network devices, and endpoints.

DevOps and security teams needed to unify telemetry to maximize their efficiency and visibility. Through years of rapid growth, SoSafe’s infrastructure for its IT environment and SaaS product had grown significantly and included a mix of cloud and on-premises systems that were all producing high volumes of telemetry data for a range of users and needs. The DevOps and site reliability engineers (SRE) teams were generating content to monitor the SaaS platform and infrastructure to understand how things were performing while the security operations center (SOC) team was using a range of tools to monitor the security and integrity of the company’s complex environment — all of which was producing content that had to be separately tracked and analyzed. Collectively, monitoring the company’s growing data volumes across a sprawl of individual tools was time-consuming and difficult for the DevOps and security teams to work efficiently and obtain the desired insights.

Dave, a fintech company known for its user-friendly banking services, faced significant challenges as it experienced hypergrowth. The company needed to invest heavily in infrastructure and gain clear visibility into their systems to manage the flood of events generated by their consumer-based system. Stock monitoring solutions in GCP were insufficient to meet Dave’s DevOps and security needs, especially as their customer base grew. They needed to capture all user activities, API calls, and logs to harness insights while iterating on products and keeping systems and user data secure.

Bitbank operates its cryptocurrency exchange system on AWS. They used an OSS distributed search and analytics engine for log monitoring, but faced issues with accurate error detection and analysis for applications. This raised concerns about IT control for maintaining security and financial auditing. The existing system was lacking in performance and security, and the AWS usage fees were increasing. Additionally, error detection in application logs was inefficient, requiring human intervention to determine details.

As a digital bank serving 31 countries across the globe, the financial services company has a wide range of software-based products that support customer offerings, such as personal banking, home loans, wealth management, and small-to-medium business services. With software delivery as a backbone of the business, the digital bank was continuously striving to mature its DevOps processes to better serve customer needs and address market opportunities. To achieve this, one essential call-to-action was to pursue a data-driven approach for strategic planning and decision making. For the DevOps team, that meant having data to understand issues as they arise and the insights to derive optimum solutions. DevOps also needed data that would empower them to move at a fast velocity to deliver software to customers. Having good observability and telemetry data on the digital environment was an essential foundational component to enabling fast and intelligent development decisions.

As part of its ongoing commitment to innovation, a leading global airline company embarked on a major initiative that—when fully completed–would entail moving hundreds of applications to the cloud. However, essential to this initiative was the need for the company’s nascent cloud platforms to first attain compliance with the highly demanding PCI Data Security Standard. Failing to achieve this milestone would endanger the company’s entire digital transformation efforts. The airline company always seeks new methods for leveraging technology to support its drive for innovation and efficiencies. These objectives were instrumental in the company’s executive mandate to adopt a cloud-first strategy for its systems and applications. For example, the airline made major investments in Amazon Web Services (AWS), Microsoft Azure, Office 365, and SharePoint. Kubernetes also features prominently in the company’s portfolio. To date, the airline has deployed approximately five, major solutions to the cloud, underpinning critical functions, such as baggage tracking and carry-on monitoring. While that’s an impressive number, there are still hundreds of other applications to migrate. Regardless of the exact cloud vs. on-premise blend of the airline’s systems and applications, one overarching fact remains: the company is obligated to adhere to its rigorous PCI DSS regulations at all times. This reality means that the airline must constantly scrutinize its entire operating landscape to uncover any security risks to its cardholder data that could jeopardize the company’s cloud computing business strategy.

As the world’s fastest-growing neo-banking platform that works with various banks, Open faced challenges in providing data access to its various departments. Providing access to their underlying data by building an admin panel for various teams was both challenging and time-consuming. “We wanted an out-of-the-box solution that could analyze our logs and provide insightful information to our teams to take action based on that.” Setting up a log monitoring tool from the ground up using open source software may seem simple, but, maintaining and scaling requires time and investments. “Our log data ingestion increases month-on-month considering the number of transactions, and the interaction we get on our platform is increasing and we wanted a log monitoring tool that would automatically scale with us, without much intervention. After trying out various open source log monitoring tools, we decided to go with Sumo Logic, which was a perfect fit for our various needs,” says Ajeesh Achuthan, co-founder & CTO at Open.

LendingTree is focused on maintaining high uptime levels by minimizing outages and disruptions to provide better service to its over eight million customers. The company also seeks new avenues for innovation and cost savings. The Kubernetes project was identified as a potential solution to boost reliability by spreading cloud computing workloads across multiple vendors. However, LendingTree needed a unified approach to understand the state of their distributed environment, become aware of issues regardless of their origin, and quickly resolve those problems.

As a rapidly evolving enterprise wielding a highly complex technology portfolio, it was crucial that Epsilon find ways to bolster its internal controls and safeguards. This went beyond the obvious requirements to protect its clients’ vital customer information. It also meant demonstrating ongoing compliance with key industry certifications that included Service Organization Control 2 (SOC 2) and Payment Card Industry (PCI). Initially, Epsilon engaged several legacy log-collecting products that were primarily geared towards security analytics. There were drawbacks to this approach, such as limited capabilities for operational or business viewpoints, scalability headaches, and excessive administrative overhead. All of these factors motivated Epsilon’s technical staff to seek a more robust machine data solution that would be able to keep pace with the company’s needs.

To implement a proactive, business-centric strategy, Infor required a scalable log monitoring and management solution. To help its customers achieve business-wide digital transformation, Infor needed the right data and holistic insights to understand how development releases impact experience. With software delivery as a backbone of the business, the company needed fast access to data so support operations could quickly analyze issues as they arise and provide customers with rapid incident response and resolution. To implement this proactive, business-centric strategy, Infor required a scalable log monitoring and management solution. “Having good observability and trending data on our development, infrastructure, and operational environment is a vital component to enable fast and intelligent business decisions. This requires a strategic partner for data aggregation and analysis, and for that, we turned to Sumo Logic,” said Iwan Eising, Team Lead of Service Reliability Architecture at Infor.

LeadSquared provides a cutting-edge, end-to-end CRM and marketing automation platform to more than 300,000 users across 1,500 brands globally. With a successful SaaS offering that delights its customers, the company has been experiencing rapid growth. A business that began with one office in India has now expanded to multiple offices across continents. For the SaaS offering to keep pace with swift customer adoption, the engineering team needed to quickly increase the Aurora databases in the company’s Amazon Web Services (AWS) environment. The engineers had to individually analyze each database log to assess how the product was performing, which was a complex and time-consuming endeavor. As the number of database clusters grew, it became very hard to monitor and analyze if database issues were negatively impacting the SaaS product performance and, in turn, the customer experience. LeadSquared wanted a scalable and automated way to centrally monitor performance across all its database clusters.

Headquartered in Malta, Catena Media is a high-quality online lead generation company for iGaming and financial services, leading the way with record-breaking retention rates of over 5 million monthly users. With more than 400 employees managing 2,600 customer brands across the globe, obtaining real-time security insights across the company’s ecosystem was a top priority. With a lean security staff, attempting to optimize security operations with the incumbent open-sourced security incident and event management (SIEM) solution was introducing many challenges. The solution ran on-premises and had stability and scalability issues. While the open-sourced solution had an active community, there was limited dedicated support to aid Catena Media with product issues. Ultimately, it required a lot of time and effort to manage, which was precious time the security staff needed to spend on other, priority efforts.

Transitioning from a traditional datacenter to the cloud, carsales was looking for a SaaS-based solution that would provide instant visibility into the health of applications in order to detect potential issues before they happen and ultimately reduce the impact to users. carsales needed a solution that avoided managing additional infrastructure or software, as their previous ELK stack had become a burden. They also required a system that could track application performance, visualize automated test results, and monitor machine and application health across their 150+ services.

The data required to deliver a great user experience kept growing, as did costs and complexity, making it difficult to meet the demands of customers. Bixby’s deep-learning AI model understands the user’s voice and delivers more accurate results when user data accumulates. However, as Bixby required more data per user, the amount of data to analyze and manage increased, escalating data management costs to the development team. While the previously used cloud-based analysis system, Google Stackdriver and Google Cloud Operations supported simple search and debugging functions, the analysis team still had to go through additional processes and data format changes. There was room for improvement in producing meaningful results and enhancing customer experience, including debugging, data distribution, performance, and issue resolution.

A global investment firm with five offices worldwide faced significant cybersecurity challenges. The firm's five-person security team, led by a chief information security officer (CISO), was tasked with protecting financial data. The senior security engineer, responsible for security operations, investigations, and threat hunting, needed an automated tool to help prioritize alerts and provide end-to-end network visibility. The existing homegrown solution was time-consuming, inefficient, and unable to meet the firm's needs. The team sought a solution that would offer more visibility into their network, improve efficiency, and provide the context needed to make confident and speedy decisions to stay ahead of attackers.

Tailored Brands, a leading omni-channel retail company, faced challenges in obtaining real-time data to analyze and support operational efficiency across its 1,000 locations and 11,000 employees. The company initially used Kibana on-premises to visualize its Elasticsearch data cluster, but managing Kibana became complex and resource-intensive. The solution required significant development time to create customized dashboards, and conducting log analysis during production issues was not a turnkey process. The company lacked the bandwidth to invest in the necessary UI resources to scale Kibana to meet current and future demands.

Weverse, a global fandom platform, faced challenges with platform silos and managing large fluctuations in traffic volume. The company needed a solution to handle explosive data growth and reduce the high costs associated with managing debug logs and operating up to 75 virtual machines. Additionally, Weverse required a more efficient and automated log management system to prevent data loss and improve service quality.

In response to the unprecedented success of the Pokémon Go app, The Pokémon Company International (Pokémon) determined that it was time to bring the game’s development in-house. A major part of this venture was migrating much of the company’s technology stack to Amazon Web Services (AWS), and then establishing it as a core platform to support many other usages. For this project to succeed, it was also vital that the company fashion a top-tier security operations center (SOC).

Tokio Marine HCC (TMHCC) faced the challenge of delivering the best user experience while managing data residency and portability during their digital transformation. With a growing global footprint, they needed to control costs and ensure full visibility across their complex distributed systems. The decision was made to build out microservices from their legacy monolithic application, containerize their applications, and host them in Amazon Elastic Kubernetes Service (EKS). They also moved from SQL Server to PostgreSQL and from Windows to Linux servers to mitigate future licensing costs. A critical part of their migration strategy was finding a solution that would provide full visibility across the entirety of their applications, supporting AWS CloudWatch, CloudTrail, and API gateway logs and metrics, while operating within their security guidelines for AWS management across different regions and groups.

SEGA Europe sought techniques to strengthen how it safeguarded the personal details supplied by millions of loyal fans. A key aspect of this exercise was consolidating security-related machine data from the company’s hybrid cloud into a single source of truth, while also establishing repeatable processes for onboarding yet-to-be-acquired game studios. Although the initial SIEM ventures appeared to be promising, the company soon encountered a number of daunting drawbacks including: deploying these solutions required making weighty expenditures for dedicated hardware and related infrastructure, SEGA Europe’s site-specific requirements necessitated significant outlays for customization, and the SIEM applications were overly brittle, and needed costly professional services to address errors and performance issues. It became apparent that the situation was untenable, and that failing to resolve these shortfalls could jeopardize SEGA Europe’s relentless commitment to the safety and security of its customers. In response, the company began an aggressive search to identify a replacement that could also serve as a ‘single source of truth’ for its nascent security operations center (SOC).

Too many monitoring vendors left Laurel with numerous blindspots and no way to know if they were delivering on customer promises. Laurel is committed to helping their customers automate time-keeping and billing processes. Most lawyers use Microsoft products exclusively, so they primarily interact with Laurel’s system through the Windows desktop application. Nine outside vendors monitored Laurel’s systems, making the reliability of their desktop application and per-customer infrastructure architecture challenging. Even with this battery of solutions, there were a lot of reliability blindspots — they didn’t know if they were delivering on customer expectations to manage time efficiently. With only alerts for CPU and memory on their containers, many poorly enriched alerts were ignored. Their on-call rotation was disorganized and included people who weren’t in the company anymore. Laurel’s product and engineering teams took a hard look at their current workflows — and found them lacking. They had to fix their general approach and understanding of infrastructure monitoring and, in the process, improve the well-being of their engineers through better clarity and less stress. Laurel ran a request for proposal (RFP) with nine vendors, searching for a monitoring solution to provide broader visibility. Laurel’s product and engineering teams also decided to take a service level objective (SLO) approach to reliability and set out to find solutions that aligned with this.

Running an inefficient SIEM solution left Knauf’s SOC team unable to scale at the same pace as company growth. As a business in operation since 1932, Knauf’s IT infrastructure had expanded through the years, becoming a large, legacy on-premises environment with decentralized SCADA systems, production tools, and many regional locations. Knauf was running McAfee Enterprise Security Manager (ESM) on-premises for their SIEM solution to gain real-time security monitoring of the extended environment. But the McAfee solution was unreliable. Dawid Krochmal, SOC Manager at Knauf, explained that “McAfee ESM was highly inefficient. An analyst wouldn’t just go for a coffee; he could go to lunch during the time it took for a query to run. Just to learn that after an hour, the query had an error, and he had to start again and wait another hour.”

The fitness company sought security information and event management (SIEM) options to protect its operations along with the personal data of more than one million members around the world. By securely monitoring the threats across its entire infrastructure, the company had the potential to dramatically shorten the amount of time necessary to detect and correct vulnerabilities. The company initially deployed Sumo Logic to make the most of its ever-enlarging machine data collection. The rollout proceeded smoothly and was quickly ingesting significant volumes of log files per day. However, even though the initial Sumo Logic implementation was of great utility to the company’s operational staff, it soon became apparent that significant shortfalls still remained in how the company’s security operations interacted with the organization’s computing resources. Instead of utilizing a centralized view that aggregated all security-related details across the company’s technology portfolio, the security group was obligated to manually connect to each resource to ascertain what was happening across their environment. For those assets that were capable of instant notification, alerts were delivered via a non-integrated set of emails or text messages.

Scaling to meet the requirements of their expanding customer base was a mammoth challenge. The increasing data residency demands of their constantly growing clientele were proving too much to handle for the legacy model of operations and deployment SAP Fieldglass had in place. This drove the decision to transition from SAP-operated on-premises data centers to a multi-cloud approach with hyperscalers to accommodate sharp changes in data volume and requirements–moving from virtual machine (VM) architecture to Kubernetes, from a legacy IT mindset to DevOps-oriented Agile delivery. With this drastic shift, SAP Fieldglass sought an observability solution that could support their transition to ensure reliability, performance, and security both internally and for millions of end-users.