Cube Dev

COTA, a healthcare company founded in 2011, specializes in combining oncology expertise with advanced technology and analytics to organize real-world medical treatment data for cancer research and care. They have access to millions of electronic oncology patient records, a data volume unmatched in the oncology healthcare industry. One of their products, the Real World Analytics (RWA) solution, helps clinicians and researchers make sense of fragmented and often incomplete electronic health records (EHR) data. However, COTA faced challenges with their existing off-the-shelf solutions like Qlik and Tableau, which required heavy customization and specialty configuration knowledge. They sought a more developer-friendly ecosystem that could handle their vast data and provide a single source of truth.

Ternary, a FinOps platform provider for Google Cloud (GCP) customers, was facing the challenge of managing and analyzing the large volume of cost-related data generated by its rapidly growing customer base. The platform, which aids cloud engineers, IT finance, and business teams in optimizing public cloud costs, had to deal with the complexities of providing a SaaS platform at scale. The challenge was to break down costs by projects and other dimensions across a time series for users with many values in a given dimension. The company was frequently running into issues with Cube’s response limit of 50,000 rows, which could result in incomplete datasets and inaccurate total cost calculations. The challenge was to present complete, accurate data to users, enabling them to perform multidimensional analysis of vast volumes of cost data.

sublimd, an award-winning medical software platform based in Switzerland, was facing a significant challenge in early 2019. They had received a request for a new module, sublimd Analytics, from a client. At that time, they had an open-source business intelligence server solution in their product. However, they were struggling with preconfiguring their analytics dashboards and delivering them ready-to-use to their non-technical customers. They needed a solution that would allow them to have full control over the customer configuration, which would be tracked in a version-control system. The challenge was to find a solution that would fit their technology stack, which included Node.js, MySQL, and Redis, and allow for a simpler deployment process.

Gadsme, an advertising platform designed for the gaming and e-sports industries, was faced with the challenge of efficiently exposing data insights to their clients. As an AdTech company, Gadsme collects millions of events every day, all of which are stored in BigQuery. The company was in need of a tool that could help them manage and expose this vast amount of data in a meaningful and efficient way. They were also looking for a solution that could help them maintain complete control of analytics costs. The challenge was further compounded by the need to keep innovating and bringing new brand experiences to the gaming and e-sports industries.

Cuboh, a restaurant-tech company, integrates delivery apps with point-of-sales systems and consolidates them into a single tablet. Despite processing over $1B in transactional volume, the company faced a significant challenge. They hadn't built their data structure to handle scale, leading to inefficiencies in querying their database for millions of rows. This resulted in latency in large customer reporting requests. The team sought to create an appropriate underlying dataset to streamline their ledger-based reporting. They initially opted for a Kafka-based streaming platform, but soon realized they needed a more robust solution to handle their large datasets. The ideal solution needed to be compatible with an underlying relational database, produce low latency requests, handle RESTful API requests, provide near-real-time reporting, be self-managed, and offer caching for date-based query structures.

Cloud Academy, a San Francisco-based SaaS startup, was in search of a modern analytics platform to enhance its existing enterprise analytics offering. The company needed a solution that could deliver a seamless, highly available embedded analytics experience based on CI/CD best practices, while also allowing for planned outages for necessary infrastructure maintenance. They also needed to leverage their existing data warehouse and maintain high flexibility in their data modeling to serve both internal and external end users. Prior to Cube, Cloud Academy used a major BI platform for their enterprise embedded analytics UI, and internal stakeholders used the BI platform directly. However, they needed a solution that would allow for greater flexibility in data modeling, security context orchestration, and caching.

Jobber, a leading provider of business management software, supports over 50 industries and has serviced over 15 million households in more than 47 countries. A crucial tool for Jobber's customers is dashboards that provide a snapshot of their businesses, helping them schedule their day, optimize routing, keep track of invoices, accept payments, and more. However, as Jobber's business scaled and accumulated close to a decade of data, the dashboard performance began to slow down. The team tried to address these performance issues through caching, query optimization, and database tuning, but they realized that more needed to be done. The challenge was to find a solution that could handle the large amount of data and still deliver high-performance dashboards.

RamSoft, a leading SaaS HealthTech company, was in search of a modern analytics platform to enhance its new analytics offering, Root. The company had been using a major business intelligence tool, but it lacked the customization capabilities required to provide a truly native end-user experience. The tool offered limited customization options, particularly in terms of user experience workflows and report generation. Additionally, RamSoft needed a solution that would allow them to control the features available to the end user. They found that many existing solutions in the market were packed with numerous irrelevant features, which often overwhelmed new users. The team required a solution that would allow them to remove unnecessary features and control user access to various functionalities. Given the complexity of their domain, they also needed a platform that could streamline the analytics behind the tool, allowing them to focus on the UX/UI and front-end.

ShopBack, a leading shopping rewards and discovery platform in Asia-Pacific, faced a significant challenge in analyzing transactions on its sites. The company needed to assess various transactional aspects, including purchase value and sales volume. In January 2020, ShopBack embarked on a new project that required extensive dashboard reporting of aggregated data for both internal and external users. One of the options considered was building an in-house application and storing the data in a graph database. However, the data was highly relational and needed to be pre-aggregated into OLAP cubes for analytics. The company also faced performance issues, with p95 query loading times taking as long as 50 seconds, leading to a poor user experience.

FeedMe, a Malaysia-based company providing a Point of Sale (POS) system for the food and beverage industry across South East Asia, was facing challenges with its reporting system. The company wanted to offer a robust reporting system that allowed users to generate their own custom reports. However, their previous solution, which was based on SQL queries and JavaScript to process data across MongoDB, CouchDB, and BigQuery, was becoming increasingly difficult to maintain and scale. FeedMe uses CouchDB in their client POS systems that can work either online or offline. The data from CouchDB is then fed into the BigQuery data lake for reporting. FeedMe’s backend systems are written in JavaScript, so they wanted to use JavaScript to process data from MongoDB and BigQuery. The challenge was to find a solution that could simplify their data processing and help them develop a data schema standard.

Cyndx, a company that serves some of the largest financial services companies worldwide, was looking to expand its product and develop functionality to explore data analytics that would allow its end users to dig deeper than their existing platform. They had evaluated several commercial business intelligence (BI) products in the past, but most of the solutions required a lot of custom work for frontend/design and integrating with their AI. They needed a solution that could seamlessly integrate with their existing AI-driven algorithms and data from over 12 million companies and more than 1 million acquisitions, capital raises, investments, and investors data in their database. The challenge was to find a solution that could provide predictive analytics and help its clients identify target lists in a fraction of the time of traditional workflows.

Simon Data, a SaaS company based in New York, operates a data platform that allows clients to manage their marketing data. The platform is powered by a complex, multi-tenant environment with datasets of varying lineages, schemas, and business purposes. Some datasets are common across client accounts, while others are client-specific. This setup, while beneficial for customers, posed a significant challenge for engineers trying to drive analytical insights across the system. The platform had several analytics products built into it, but the company wanted to consolidate them on a standard foundation and streamline the development and deployment processes. They aimed to build a framework that could be used for future analytics product development on any part of their core platform. The goals for the system included a seamless development experience, support for querying arbitrary data, fluency in managing various schemas, and an ability to rapidly prototype and develop the user experience. They also wanted to transparently present the queries and transformations used to produce results to facilitate QA by multiple stakeholders.

Qualibrate, a SaaS company based in Amsterdam, was seeking an effective way to provide its users with the flexibility of advanced reporting and simplicity in generating dashboards. While there were several great products available in the market such as Kibana and Grafana, Qualibrate wanted to offer a seamless experience within their own platform. The challenge was to find a solution that would integrate well with their current platform which uses Vue.js. After considering various options, they found Cube from Statsbot.co to be a perfect fit for their use case. However, there was a significant caveat - Cube did not have an implementation for Vue.js. This led Qualibrate to consider working with the community to improve the MongoDB connector and integrate Cube into their workflow.