Apiiro, a cloud-native application security platform, was faced with the challenge of providing complete visibility of all application components to security and development teams. This was crucial in order to map the application attack surface and proactively remediate critical risks before releasing to the cloud. The complexity of security issues was increasing as software development accelerated and businesses used agile models to deploy more apps at a faster rate. Traditional application security tools were proving ineffective in improving a company's security posture due to the intricate combination of multiple factors that contribute to risks. Furthermore, Apiiro needed to ensure near-perfect uptimes to deliver resilience and grow its business on Google Marketplace.

Arab Bank, a leading financial institution in the Middle East, recognized the need to innovate and modernize its services to maintain its market position and attract new customers. Despite its strong presence in the market, the bank faced challenges in responding quickly to the growing consumer market and the small and midsize enterprise (SME) sector. The Middle East and North Africa (MENA) region, where Arab Bank operates, has a largely unbanked population, with a significant portion interested in new digital tech capabilities. The bank also had to deal with legacy systems and applications, which made it difficult to release new features and functionalities frequently. Furthermore, Arab Bank had to navigate regulatory constraints such as data residency rules in the countries where it operates.

Body & Fit, a leading sports nutrition and dietary product business, was facing challenges with its existing ecommerce platform. The company, which processes almost two million orders a year and offers more than 3,000 items, was planning a strategy of rapid growth, aiming to double its presence from seven to twelve countries. However, scalability was a challenge on its existing ecommerce platform, and weak integration between components made it difficult to track customer journeys across touchpoints. The company needed a solution that could deliver excellent customer experience, potential for global expansion, continuous optimization, and personalized content. Furthermore, Body & Fit wanted to create a unified customer journey by combining data from multiple sources, including its ERP, ecommerce solution, event-stream processing, frontend interfaces, online marketing, customer service solution, and analytics.

Bonnier Publications, a Copenhagen-based media company, was facing the challenge of transitioning from a print to a digital multi-format business model in an ever-changing media environment. The global magazine industry was shrinking every year, and Bonnier knew that this digital transformation was critical for its survival and growth. The company set a six-month deadline to achieve this goal, with the challenge being a complete overhaul of its customer data and the way it engaged with customers online to maximize ecommerce conversion rates. The initial target was to gain 25,000 new subscriptions. Bonnier's success largely depended on its reader base, with B2C business accounting for 75% of its revenue. Therefore, it was crucial for the company to understand its readers and their preferences.

Book Creator, a self-publishing tool, aimed to shift its focus towards serving the K-12 market and enhance student engagement by unlocking their creativity. The company wanted to provide a platform where students and teachers could collaboratively create and publish ebooks and learning materials. However, with a small team of 13, they faced the challenge of reaching millions of students and handling high-variability workloads. Additionally, they needed to comply with strict data access management requirements including GDPR in the European Union and FERPA/COPPA in the U.S. The company also wanted to rapidly develop new features while reducing the complexities of storing data and scaling access to it.

Boxed, a leading digital wholesaler, was founded in 2013 with the aim of making bulk shopping easy, convenient, and accessible for consumers. As the COVID-19 pandemic hit the U.S., Boxed experienced a significant increase in traffic and demand on its platform. The shift towards online grocery shopping, which had been gradual, suddenly accelerated, and Boxed needed to scale up its infrastructure and data processing to keep up with the flood of orders that nearly doubled during the pandemic. With the large increase in concurrent users on the site, Boxed’s database was hit with thousands of read/write operations per second, specifically for operations like creating new user accounts, adding/removing items to cart, and checking out. These actions required writes to its database, and Boxed had to scale up the resources available to its database to handle this increase in operations per second.

Airbus Intelligence, a unit of Airbus Defence and Space, provides comprehensive satellite imagery and data to over 1,200 customers in more than 100 countries. Over the years, Airbus Intelligence has expanded its client base from governmental security to include entities that benefit from high-quality imagery such as agricultural companies. However, the company faced a challenge in meeting the growing expectations of its customers due to the limitations of its existing solution. The traditional process involved capturing images from satellites, processing them, and downloading them into the company’s library. Customers would then select the images they wanted from a catalogue and receive them several hours later. As Airbus added more satellites and technology became more sophisticated, the company was able to provide better quality data and imagery at increasingly large volumes. However, the existing solution could not speed up the production and delivery of its image and data products.

BBVA, a leading bank in Spain, was facing the challenge of digital transformation in the highly regulated banking industry. The bank was under pressure to compete with digital-native fintech companies while maintaining the trust of its 78.9 million customers. The banking industry, being an attractive target for cybercriminals, had traditionally kept their IT infrastructures on-premises to maintain control over data security and privacy. However, BBVA recognized the need to embrace cloud computing to gain agility and anticipate future banking needs. The bank also aimed to reduce its carbon footprint and become an environmentally sustainable business. The challenge was to navigate this transformation while maintaining stringent data security and privacy standards.

AgroStar, an ecommerce platform selling farm supplies, was facing challenges in expanding its services to small farmers in India. The company aimed to provide a full-service platform that combines agronomy, data science, machine learning, and analytics to boost crop yields and improve income for these farmers. However, the lack of access to new, higher yield seeds and improved soil analyses for small farmers, who had to rely on traditional methods, was a significant hurdle. Additionally, the dissemination of innovative information from universities to small, grassroots farmers was slow and inefficient. The company also faced the challenge of providing timely advice in five languages on various aspects of farming, including seed optimization, crop rotation, soil nutrition, and pest control.

Airwallex, a global payments platform, was facing challenges in expanding into new markets and complying with stringent regulations such as the Payment Card Industry Data Security Standard (PCI DSS). The company's existing cloud service was unable to support its growth plans and ensure compliance with these regulations. As a startup, Airwallex also needed to focus on the application layer of its offerings rather than dedicating time and resources to infrastructure. Furthermore, the company required high security, reliability, low latency, and manageable costs to build and retain trust with its customers. The company also needed to ensure low latency to provide data consistency and offer low rates to customers without applying a high spread to account for rate fluctuations due to the fast-moving nature of financial markets.

A2A, an Italian utilities provider, faced the challenge of scaling its business to meet the needs of over 2.5 million customers while also striving to meet the United Nations’ 2030 sustainability goals. The company aimed to double its client base by 2030, in line with the liberalization of the Italian energy market. However, the increasing customer numbers, shifting expectations, and the uptake of new technology, such as smart electricity meters, made it difficult to predict infrastructure requirements. A2A's data warehouse would refresh every 24 hours, which was not sufficient for real-time customer service. The company needed a solution that could provide real-time data to respond to customer needs immediately, especially in critical situations such as power cuts.

ATB Financial, a financial institution with $55.1 billion in assets, was seeking to enhance its IT environment and improve client experiences. Despite having a loyal client base, ATB faced growing competition from larger banks and FinTech companies. To stay competitive, ATB launched a 'Work Reimagined' initiative, powered by Google Workspace, to improve collaboration and client service. However, the bank wanted to further modernize its operations by migrating from SAP ERP Central Component (ECC) to SAP S/4HANA for improved performance and more dynamic data models. Additionally, ATB planned to move from SAP Bank Analyzer to SAP Finance and Risk Data Platform (FRDP) to consolidate applications on a HANA-based data platform. The challenge was to manage the increasing data storage and compute power needs, which were difficult to handle on-premises.

Boa Vista Serviços, a leading analytical-intelligence and credit-bureau player in Brazil, was facing challenges with its previous technology infrastructure. The company's physical servers located in three data centers were hindering its speed and ability to innovate. Limited scalability of resources and processes meant that they could not train many analytical models simultaneously or implement modern machine-learning techniques. One of its products was on the verge of halting sales because the company's processing capacity had reached its peak, directly impacting business expansion. The company needed to embrace digital transformation by migrating all its information from physical servers to the cloud. They also needed to switch from a traditional organizational model to a squad-based model to focus more efficiently on various technology and business aspects and challenges.

Americanas sa, a Brazilian retail giant, faced a significant challenge in delivering the best customer experience across its 1,700+ physical stores and ecommerce platforms. The company, which emerged from the merger of Lojas Americanas and B2W Digital, had a diverse audience spread across Brazil, with over 90 million registered customers and 46 million unique users. Data analytics was crucial to understanding and improving the shopping journey of this diverse audience. However, the company's data environment, initially hosted on physical servers, was unable to keep pace with its expansion rate. The infrastructure lacked the maintainability and scalability needed to manage the growing data volume. The business required more efficiency to test hypotheses, validate concepts, and assist in decision-making processes. In 2017, the company decided to migrate its data environment to the cloud to improve scalability, governance, security, and speed.

ADEO, a global leader in home improvement and DIY, was facing a significant challenge with its 15-year-old on-premises data warehouse. The company was struggling to manage the vast amount of data it was collecting from its customers' journeys on its e-commerce websites or marketplaces. The legacy infrastructure was unable to keep pace with the data influx, which threatened to impact customer retention. ADEO recognized the need for a data-driven strategy to align with its digital transformation and improve decision-making processes. However, the transition to a cloud-based system was a daunting task, given the age and complexity of the existing on-premises system. ADEO also wanted to adopt a platform strategy to facilitate partnerships and collaborations, but the legacy infrastructure was a hindrance.

17LIVE, a live streaming company founded in Taiwan in 2015, has rapidly expanded its presence worldwide, offering a diverse range of content to more than 50 million registered users from 133 countries. However, as the company grew, it faced challenges in maintaining the quality of its streaming services. Initially, 17LIVE deployed its live streaming application on a public cloud platform that didn’t have a data center in Taiwan. As its business in Taiwan grew, the company realized that it needed to enhance the scalability capability of its app infrastructure and lower the network latency of its streaming services. The company also wanted to optimize its operations and the content performance on its live video streaming platform.

ADEO, the third largest player in the global home improvement market, faced a significant challenge in the face of increasing competition from online retailers. In 2017, ADEO's CEO launched a global strategy for the digital transformation of the company, aiming to develop online activity and provide new customer solutions through the creation of digital applications. However, with its on-premises data center, it was taking up to four days to provision a complete environment to build a new application. This delay was hindering the company's ability to quickly respond to market changes and customer needs. The IT team sought a solution that would allow them to provision resources faster and enable developers to work more independently.

ADEO Services, a technical support provider for ADEO, the world’s third-largest home improvement company, faced a significant challenge in maintaining cloud compliance and optimizing its infrastructure. The company was implementing a new data platform within the Global Tech and Data Platform for ADEO in 2018, using a site reliability engineering (SRE) approach with Google Cloud operations suite. However, for certain services, it required custom options and the ability to automatically monitor services in real time, detecting noncompliance errors as they occurred. The operations team needed a tool that could address inventory, accountability, and real-time monitoring. While ADEO was already using Cloud Asset Inventory, the team wanted to have up-to-date overviews across all its services and be able to cross-reference data errors. They also wanted real-time compliance monitoring to ensure systems were compliant at the point of development.

ADEO Services, a company with a mission to inspire homeowners and help them create their dream home, faced a challenge in managing its vast data platform. The platform was established in 2018 as part of a digital transformation project and was designed to collect, store, and deliver capabilities that enable all of ADEO’s companies to search, consult, and use data easily. The data platform team adopted a site reliability engineering (SRE) model to administer the platform, focusing on keeping services running and users happy while identifying opportunities to automate repetitive work. However, operating in a systematic way, at scale, while staying secure and compliant with company policies, proved to be a challenge. The team needed a solution that would allow them to monitor services using self-managed solutions and improve the experience of users with automated SLO monitoring.

Akeneo, a global leader in product experience management, was facing a significant challenge. The company had developed a comprehensive Product Information Management (PIM) solution that centralized product data, making it easier to enrich and update. However, the company was running into issues by 2016. While clients were pleased with the product, they had to either host the Akeneo PIM themselves or work with local partners to host it and get it running. This was a barrier for many potential customers who lacked the technical expertise. The existing on-premises infrastructure wasn't built for a web-based product, so Akeneo had to look to the cloud. However, they needed a cloud provider that could provide high-quality service globally and wouldn't be seen as a competitor by its retail customers.

American Eagle Outfitters, a leading US clothing manufacturer and retailer, was facing the challenge of staying relevant to changing customer preferences. The company needed to innovate with investments in pricing, promotion, assortment, technology, and in-store experiences. However, executives needed confidence in the ROI of these activities to justify the associated spending. The company was also dealing with an increasing amount of data from various sources including web, mobile, and third-party data combined with transaction, inventory, weather, mobility, and other market data. This presented new opportunities to understand and shape customer behavior but also required a more efficient approach to data warehousing to increase speed and scalability.

Bending Spoons, a Milan-based app developer, was faced with the challenge of standing out in a saturated marketplace where hundreds of apps often offer similar functionalities. The company needed to make data-driven decisions to identify the best apps to develop and optimize them for user experience. Their business model, built around auto-renewable subscriptions and in-app purchases, required a deep understanding of user behavior. However, to make these data-driven choices and grow the business, Bending Spoons needed to analyze large volumes of data very quickly. They were looking for a powerful data storage and analysis system that didn't require specialist technical support. Additionally, as a start-up, they wanted to grow quickly without investing too much in operations and infrastructure, but also needed the capacity to expand rapidly once the business grew.

Billie, a German fintech startup, offers online invoice factoring services to its customers, helping them to advance payments for their outstanding invoices. As an intermediary in handling payments, the company faced the challenge of ensuring utmost reliability and security. Unlike its competitors who operate as subsidiaries of large, established firms, Billie operates independently and had to obtain its own operating license from the German financial authorities. The company needed to build a stable, scalable platform from scratch that could adapt to market conditions and gain the trust of their clients. The challenge was to create a platform that was not only secure but also capable of scaling quickly and efficiently.

boodmo, India’s largest online marketplace for automotive components, faced a significant challenge in its early days. When the company first launched in 2015, there was a scarcity of customer data available. To make a lasting impact, boodmo had to gather and analyze most of this information itself. The company was using Google Analytics and Google Ads to gather data about its web activity and advertising, but it also needed to include more detailed information about its orders. Furthermore, boodmo wanted to build on its relationship with its customers and expand further. To do so effectively, the company had to carry out a comprehensive analysis of its customers as well as identify profitable marketing channels. However, pulling together different strands of data from multiple sources was not straightforward.

Harver, a pre-employment assessment software company, was facing the challenge of scaling to meet the demands of its growing enterprise customers. Initially, the company relied on labor-intensive statistical analysis in R or Excel to surface hiring metrics to their customers. However, as Harver continued to grow and add more enterprise customers, it had to automate and scale processes in order to provide their growing customer base the insights and customization they expected. The company needed an analytics solution that could also support its multi-tiered product offering, integrate with other tools such as Zendesk, and support multi-tenancy. In addition to the technical functionality, it was also critical that an embedded solution could appear seamless and natural within the impeccably designed Harver platform.

Acordo Certo, a Brazilian financial services company, was facing significant challenges with its existing cloud services provider. The company's mission is to simplify debt tracking, negotiation, payment, and notification processes for defaulting consumers. However, the company's platform was experiencing performance issues, particularly with large-sized queries. The existing solution was costly, inefficient, and unable to deliver the desired results. The company needed a robust solution that could handle the vast amount of data generated by its nearly one million new subscriptions per month and dozens of billions of registrations. The company also needed to process this data to learn about consumer profiles and maintain customized, assertive communications with each of them.

Adloox, an ad-verification and insights platform, was facing challenges in delivering timely and in-depth insights to its clients due to its existing infrastructure. The company was dealing with a complex digital landscape where fraudulent traffic, lack of transparency in delivery and reporting, and the ability to track real ROI were among the top challenges. Adloox's platform was designed to address these issues by ensuring ads are viewable, impactful, and delivered in a brand-safe environment. However, the company was processing huge amounts of data, with more than 7 billion fraudulent bid requests avoided every day. Its existing infrastructure, built on dedicated servers, was not scalable enough to handle the increasing data load and traffic peaks. The company was constantly over-provisioning and monitoring its infrastructure to make adjustments, which was time-consuming and costly.

Affable, a Singapore-based startup, has built an AI-based influencer marketing platform that tracks over 1 million micro-influencers across various social media platforms. The company's challenge was to process vast amounts of data and images to recommend key micro-influencers to clients for promoting their products and services. The business was processing 100 million events through its data pipelines and using machine learning models to serve up to 20 million image requests per day. The agility and responsiveness were critical to the success of the business. However, the company was struggling with the fast delivery of accurate, actionable influencer data to meet client demands. The company initially started running its platform on a traditional cloud service, but soon saw an opportunity to use machine learning and big data analysis to create even more value for its clients.

AfterShip, a Hong Kong-based company providing e-commerce shipment, label and rate calculation, and self-service return services to 300,000 merchants worldwide, was facing a significant challenge. The company was experiencing rapid growth, tracking about 30 million packages per month and doubling its revenue, package transaction numbers, and team size every year for the past three years. However, it was struggling to maintain this growth trajectory while automating key infrastructure processes, implementing a continuous deployment model, and controlling costs. The company was also keen on maintaining a global presence and high-quality service. AfterShip had initially delivered its applications and services from an incumbent public cloud service, but it was not meeting their needs for scalability and cost-effectiveness.

AntVoice, a French startup, was faced with the challenge of improving the effectiveness of online advertising. Traditional ad selection methods, which analyze customers’ recent shopping history and target similar products, often resulted in customers seeing ads for products they’ve already bought. This not only wasted money on redundant ads but also risked tarnishing the brand's reputation. AntVoice aimed to solve this problem with its “predictive targeting” AI, which required a robust infrastructure to handle large volumes of data and perform complex computations at high speeds. However, as the company grew, it became clear that their existing infrastructure was not sufficient to support the heavy-duty requirements of their final product.