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Futureplay, a fast-growing mobile games studio, was struggling with scattered data and no single source of truth. They were using a variety of tools, resulting in multiple sources of truth. This made it challenging to understand how players were interacting with content and to make data-informed decisions. For instance, to validate revenue streams, they had to confirm if the number of installs charged by the user acquisition networks matched the number of players using the game. The manual process of bringing data together from multiple tools and consolidating definitions meant that most employees weren’t able to fully leverage data to improve the user experience and optimize their own budgets and effort. Futureplay needed a solution that would help more users across the company access, understand, and trust data.

GoCardless, a rapidly growing payment platform for businesses, was facing challenges in accessing accurate and reliable data. The company was using a combination of traditional internally developed business intelligence tools and third-party SQL clients that required manual queries. Non-technical business owners had to submit tickets to the analytics team, who wrote code to extract insights. This process was time-consuming and inefficient. The finance team spent four days at the end of each month running revenue numbers, which delayed critical visibility into revenue trends and frustrated executives. The product team also lacked easy and rapid access to product usage data that could help drive a roadmap for accelerated company growth.

SoundCommerce, a data platform for retail brands, was facing challenges in accessing and taking action on the data they needed to efficiently and effectively deliver above and beyond the competition. They were under pressure to offer the best customer experience, which required understanding customers and supply chain. Their previous solution put too much administrative burden on their technical team for creating, maintaining, and customizing a shared and responsive data model, and a growing library of reports and visualizations. The prior stack lacked the power and features that consumer brands and retailers need to make fast decisions at scale. In addition to product challenges, the SoundCommerce product team worried about the risk of potential vendor lock-in down the road — both for their commercial product and for their customers’ data.

Sunrun, a leading provider of residential solar power, was facing challenges in managing their growing volumes of data across installation operations, installed systems, customer operations, and sales. The company was using a legacy data stack that required IT and data team support for almost every internal data request. This reliance on IT and the data team drained time and resources with ad-hoc requests, changing requirements, and backlogs of reporting requests. Moreover, the data pipelines and infrastructure weren’t scaling to meet either data growth or increased demand for data access. The data team struggled to respond to changing data sets or new sources of data as quickly as the business demanded, and Sunrun's legacy Oracle data warehouse was not equipped to scale across growing analytics demands or unlock predictive insights with ease.

Vivino, the world's largest online wine marketplace, was facing challenges with data management. The company was using a plethora of spreadsheets owned by different stakeholders in different time zones, leading to inconsistent and inaccurate data. This was particularly problematic for the eCommerce side of the business, where data was being used to influence strategy and decision-making as the team scaled globally. In addition, technical users who ran their own database queries needed help making sense of conflicting results from queries run without consistent data definitions across the organization.

Moderna, a research-driven organization, has always relied heavily on data for its operations. However, the company faced challenges in accessing actionable insights from its data. The majority of employees relied primarily on Excel for data analysis, with some researchers utilizing Spotfire Desktop. These tools required significant manual work and set a high barrier to entry. This manual process led to data silos across the organization, limited opportunity to further explore data, and created issues of consistency resulting from various and conflicting versions of the same report. The company needed a solution that would improve self-service and exploration, maintain data quality and consistency, and ensure the new tool would be cost-effective and integrate with the tools Moderna already had in place.

Businesses require critical equipment to be running at peak efficiencies and utilization in order to realize return on capital investments. In the digitally connected world, businesses face higher operational risks due to unexpected failures and quite often have limited insights into the root cause of problems. Common industry challenges are: Costs of unproductive time, Lower asset utilization, Risks of operating outside of manufacturer specifications, Regulatory compliance risk.

Snowe, a home goods retailer, was facing challenges with managing an overwhelming amount of data from disconnected sources and too many tools. The company was using Excel, Google Sheets, and MS Access for data reporting, but these manual mechanisms could not scale with the company’s growing needs. The team was bogged down with laborious, time-intensive processes. Other solutions on the market were either too expensive or required advanced SQL knowledge. Snowe needed a cross-functional platform that could extract meaningful insights from their data while providing companywide utility and transparency, without breaking the bank.

PeopleDoc, a leading provider of SaaS HR platforms, needed a solution that could meet its complex requirements while also complying with the legal framework set out by GDPR. The company aimed to optimize communication and streamline administrative processes between HR departments and employees. However, they lacked a system that could handle complicated data modelling, provide access to real-time accurate data, ensure highly confidential HR information was secure and offer an intuitive user experience.

TIER Mobility, a Berlin-based startup offering e-scooter ride-sharing services, was facing challenges with its initial tech stack. The company's rapid growth and expansion into new cities required a scalable business intelligence solution. The existing system, which used Alooma for data loading and open-source BI solutions for the modeling layer, was inefficient and lacked the power to provide deeper insights into the performance of the fleet. The BI team was spending most of their time writing and updating SQL queries, which was not sustainable for a fast-growing business.

Conrad Electronic, a B2B sourcing platform, was looking for a way to provide its marketplace suppliers with valuable data insights on their products. The company wanted to maintain its high-quality service and ensure that marketplace providers could easily manage their products. The COVID-19 pandemic had caused demand peaks and the company needed a solution that could help suppliers respond to customers’ needs quickly. The company wanted a solution that could provide visibility into key metrics on their product portfolios.

Charlotte Tilbury, a rapidly-growing global luxury beauty and skincare brand, was seeking to advance its data analytics processes while scaling up its ecommerce platform to meet growing customer needs across channels. The company needed a unified approach to analytics and reporting that would provide access to real-time, critical data insights across the business, from ecommerce to supply chain and finance.

ACME Truck Line, a leader in the truck transport of equipment, materials, and supplies throughout the United States, was facing challenges with its invoice processing standards. The company was asked by a major oil and gas industry customer to shift from paper to electronic invoicing. However, the traditionally paper-based truck transport industry had varying invoice processing standards from customer to customer. Before deploying the Actian solution, ACME had to enter all of its invoicing data twice: once in its own accounting system and once in its customer’s e-invoice system. This 'double-entry' practice doubled the risk of errors, delays and rework in the invoicing process. It also had an adverse impact on the company’s customer onboarding process, making it unnecessarily cumbersome and complex and hindering support of high-volume customers.

Bucap, a leading Italian company providing outsourced document archiving and management services, needed a highly flexible and scalable system to allow quick and easy customization of the applications to respond to business requirements. The company handles huge quantities of data which must be managed and stored in systems compliant with the requirements of high availability, accessibility and security. Bucap also needed the system to be scalable, reliable, highly available and easy to maintain, in order to be able to support all the business processes of storage, indexing, retrieving, and displaying of the documents at the service level agreement committed with the customers.

Egide Informatique, a Paris-based company providing a comprehensive solution for real estate management, was in search of a reliable, high-performing database that would not require maintenance by a database administrator (DBA). Their software encompasses the commercial, residential, and social housing and addresses the needs of property administrators, co-ownership trustees, and property managers through its modular set of features. They have a wide-ranging base of 500 customers who can have anywhere from one to 200 workstations each, with some not even having an IT system in place. The challenge was to find a database solution that could cater to this diverse customer base, ensuring high performance and reliability without the need for constant maintenance.

The Office of the Revenue Commissioners in Ireland, responsible for the assessment and collection of taxes and duties, required high availability and high performance for its mission-critical computer systems. It had adopted the Ingres database and the OpenROAD application development toolset in 1993 and had constantly upgraded to the latest versions to ensure a stable, manageable, and economical platform. However, the organization faced challenges in improving transaction processing as well as better batch and checkpoint performance. It was essential to move mission-critical systems to Actian Ingres database without any impact on live services. The organization had many databases in multiple Ingres installations, all of which support mission-critical applications either in production or at various stages of development. It was essential to have minimal downtime during the upgrade and to ensure that all applications, especially its large-scale batch applications, continued to run smoothly.

The Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) of the University of Oxford is involved in extensive bioresearch and healthcare studies. They extract and analyze data related to the causes, prevention, and treatment of chronic illnesses such as cancer, heart disease, and strokes. However, they faced a significant challenge when they realized that their existing database platform could not cope with the large data volumes involved in their research. The legacy platform struggled with complex queries, especially when they involved several thousand fields. The analytics could take days, which was not acceptable for the fast-paced research environment. They needed a solution that could handle high-speed analytics and deliver results within extremely fast timescales.

Sabre Holdings, a world leader in the travel marketplace, was looking for an open system solution to provide real-time inventory for travelocity.com and several other websites. The challenge was processing Origination and Destination, also known as Airline Inventory, which requires massive transactional throughput. Relational technology was not a good fit for this task. Sabre was looking to migrate from an expensive and proprietary IBM Mainframe System, TPF, to an open system solution using an object-oriented design approach to run its SabreSonic Inventory System.

Reliance Mutual, a UK-based life and pensions company, underwent a significant shift in its business model in 2003. Instead of writing new policies, the company started buying poorly performing existing life and pensions policies from other insurers. This aggressive growth strategy relied on the company's ability to aggregate these disparate policies within a single, highly efficient administration system that could turn the previously unprofitable into a robust income generator. The transition posed a two-fold challenge for Reliance Mutual’s IT department – to build and maintain a database capable of managing tremendous volumes of data, including migrating and integrating blocks of newly acquired policies – and to do so with a staff that had been dramatically reduced as part of a strategic review.

Sheetz, a rapidly growing convenience store chain in the U.S., was facing the challenge of managing and analyzing increasing volumes of data from multiple sources. The company needed to optimize costs, maintain high quality, and ensure a positive customer experience. As data volumes continued to grow, Sheetz recognized the need to switch from a more expensive platform to a more cost-effective and efficient one that could handle the increasing data and provide actionable insights. The company was also looking to expand its data analysis from one year to two years, which equates to approximately three billion rows of data. Furthermore, Sheetz was anticipating its data to double over the next two to three years.

Siemens Industry Automation was tasked with providing a scalable solution for the automation of batch processes in the chemical, pharmaceutical, and food & beverage industry. The solution needed to ensure high product quality, production performance over time, complete traceability of production, quick response to market conditions, and efficient use of production equipment. The challenge was to find a database technology that would optimally match the requirements of the SIMATIC® BATCH program design, which provides an object-oriented view on models, procedures, and recipes for comprehensive batch processes. The system needed to be stable, performant, and capable of handling large database files up to double-digit Gigabytes in size. It also needed to provide fast and reliable real-time access to prevent any significant delay, failure, or shutdown that could potentially affect product quantity and quality.

The Scottish Qualifications Authority (SQA) was faced with the challenge of rapidly expanding its database functionality and capacity to accommodate the increasing number of subjects and students. The agency needed to maximize the system’s development agility to offer new functionality in response to growing user expectations. The agency was also expanding to new markets in China, the Caribbean, and beyond, which required robust performance and open source flexibility.

Softwear BV, a SaaS solution provider for retailers, was facing challenges with its legacy database system. The poor performance and functionality of the system were unable to meet the analytical needs of its customers. As the database tables grew from millions of records to potentially billions, the transactional-based database solution could not keep up with the analytic demands from the customers. Furthermore, Softwear wanted to streamline the applications and reduce the amount of hardware needed in its data centers. The company also aimed to offer the analytic reporting layer as a SaaS-based on-demand online solution.

Sony Creative Software was facing a challenge with their Blu-ray authoring software, Blu-print. The software was built using object-relational mapping (ORM) technology, which was not scaling as required. The production of a Blu-ray disc can involve as many as 200,000 objects, demanding a fast and reliable database capable of handling an equal number of complex object relationships. Additionally, the emerging Blu-ray specification was in flux, and every change to the spec required a corresponding adjustment to the data model and database schema. Whenever a change was required to the data model, a time-consuming process ensued, requiring the export of all data to XML, updating and migrating the schema, and then reimporting the data.

STRATEC is one of the most innovative medical engineering companies in Germany. The development team was looking for a small but powerful object-oriented database that was available in “embedded mode” and would not require any database administration tasks from end users. The company develops and manufactures fully automated analyzer systems for clinical diagnostics and biotechnology. Main applications are blood group analysis, serological tests for infectious diseases, and immunoassays. About 90% of STRATEC’s products are licensed and sold through OEM customers, who market the systems under their own brands, including the chemical reagents.

The Rohatyn Group (TRG), a hedge fund based in New York, needed a solution to manage risk in the highly volatile market of hedge funds. Information about positions, pricing, and risk is critical to investment decision-making. For tactical decision-making, TRG provided analysts immediate access to the information they needed in a format that they could use through a self-service data access environment. However, the combination of market volatility and a desire to do more strategic analysis drove the need to understand how their positions had performed over time. While the existing solution provided the interactive analysis TRG was looking for, it did not have the historical data. They wanted the user tools to remain the same and the query responses to be interactive, but they needed to do the analysis on more than 1000 times the data.

Telecom Bretagne, a prestigious graduate engineering school in France, was tasked with developing a French validator and demonstrator system for the UsiXML project, which is based on the European Maritime Surveillance project. The challenge was to develop a database technology that could provide both the objects of the simulation and the operational information captured during the experimental or effective mission. The system needed to integrate various autonomous surveillance applications, tactile surfaces, and sensor systems, including maritime search and rescue, traffic monitoring, fishing inspections, and maritime border surveillance.

TNT Express Services was in need of a reliable partner who could not only provide robust technology for building business applications, but also a qualified team of IT professionals who could support them by developing, running, and maintaining their applications. The company was looking for a solution that could help them track high-value items shipped by two of their divisions: Technology Express, which distributed IT and computing equipment, and Retail Express, now Fashion Group, which distributed high-end clothing from manufacturers to High Street outlets. Over time, the need for the system expanded to cover all items shipped by TNT Express.

Verite Group was developing a packet reconstruction and network intelligence application, Netscope, which was expected to handle diverse deployment environments and large, complex data streams. The original designs for Netscope called for a relational approach, with the IBM DB2 PureXML to drive the application. However, during testing, it became apparent that the computational cost of retrieving and translating XML from the database into in-memory objects was prohibitive, to the point where enterprise scaling would be limited. Turning to an object-oriented approach, Verite looked to the Hibernate framework and ran simulations with both MySqL and PostGres powering the system. While the object approach proved the right path, the Hibernate setup put a drain on CPU and disk usage, to the point where it impeded performance at even modest network data volumes.

The Bank’s in-house analytics solution, Netezza, had reached its end-of-life cycle and was not going to be supported by IBM or its channel partners. The Bank needed to create one data repository for all positions across all asset classes, enabling ad hoc analysis of positions and their sensitivity to market factors. The Bank also wanted greater visibility into its risk exposure. It knew that presently, managing client risk and exposure was at 20th-century levels. For example, risk and opportunity value was analyzed via batch data dumps once a day. The Bank needed greater insights, delivered in sub-minute intervals, multiple times a day. It also had additional criteria that had to be met, including improved price/performance levels, ease of development and maintenance, durability, and a palatable TCO.