Nomad Goods, a company that started as a Kickstarter project in 2012, had grown significantly over the years, expanding their product line and business strategy. By 2018, they offered over thirty products and distributed through retailers such as Best Buy, Urban Outfitters, and Staples. However, with the expansion of sales, product line, warehouses, and sales channels, inventory management became a major challenge. The lack of a single source of truth for their inventory led to daily miscommunications and dissatisfaction from customers. Operational alignment meetings often took precedence over discussions about new product launches and promotional plans. Nomad’s Co-Founder & COO, Brian Hahn, spent most of his day over spreadsheets, estimating inventory in transit, at their 3PL, and in their internal warehouse. This process involved logging into multiple platforms, exporting data, and analyzing it in Excel.

Aaron Alter, a composer and technical consultant, faced the challenge of creating music for modern performances and recordings while maintaining his full-time career. He needed to establish a creative presence in the music industry, despite having limited time due to his demanding consulting job. The traditional method of composing music, which involved writing everything by hand, was time-consuming and inefficient. This made it difficult for him to expand his second career as a composer and produce new music regularly.

Airbag, a band from Argentina, faced the challenge of recording and producing music that captures high-quality audio while being flexible and easy to use. With a busy live concert schedule and global ambitions, the band needed a tool that could capture their sound in both live and studio settings, edit and mix their tracks, apply effects and MIDI instruments, and quickly publish songs online for their rapidly growing fan base. The band's bassist, Gastón Sardelli, was largely responsible for their recording sessions, editing and mixing, and developing a sound that was unique and personal. The challenge was to find a tool that was intuitive for a musician and could capture a very clean sound.

Gainesville, Georgia, located in the foothills of the Blue Ridge Mountains, is a region where public transit is essential yet inconvenient. The area's bus stops are not centrally located, leading to long walks and low ridership. This situation is particularly challenging for residents who are not as mobile due to age or physical ability. Furthermore, a significant number of residents do not own a vehicle, making them heavily reliant on public transit. The existing fixed routes were underperforming, and the lack of a modern, convenient system was a significant issue for the community.

The COVID-19 pandemic has resulted in a massive backlog of X-rays for radiologists to sift through, due to the closure of non-essential businesses and people staying at home. The traditional detection system for radiologists relied heavily on their own eyes, which could lead to human error. This situation posed a significant challenge in terms of time efficiency and accuracy in the detection and classification of anomalies in mammography. The need for a solution that could help radiologists improve the accuracy of their work, support better clinical decision-making, and improve financial outcomes for hospitals and clinics was evident.

Ender Diagnostics, a Swiss developer and manufacturer of rapid molecular diagnostic kits for the detection of SARS-CoV-2, faced a significant challenge with its quality management system (QMS). Founded in 2020, the company expanded rapidly due to the high demand for COVID-19 testing. However, Ender's manual, paper-based QMS was unable to keep up with this growth. It lacked audit-ready traceability and control and did not support remote and home working. The majority of Ender's workforce consisted of scientists and graduates, with only small pockets of specific quality management experience. The company needed a system that would not distract from scientific work with time-consuming administrative tasks. The challenge was to implement a document and training management system that could handle the company's rapid expansion and remote working needs.

The healthcare sector is often burdened with administrative tasks that can limit the capacity of staff and resources. Traditional methods of patient engagement, such as appointment notifications and waiting list validations, can be time-consuming and inefficient. Additionally, the reliance on physical mail for appointment letters can lead to delays and lost information. The challenge was to find a solution that could reduce the administrative burden on staff, increase capacity, and improve patient engagement, while also integrating seamlessly with existing healthcare systems.

Yource, a leader in customer contact and business process outsourcing, was on a path of complete digital transformation. The company was looking for a development platform that could provide a secure environment and enable faster development time to build applications for its internal processes. As a technology-first company, Yource had been a low-code user and a WaveMaker client since 2019, successfully implementing critical internal applications with low-code. However, the company needed a solution that offered flexibility in terms of customizing code, and the ability to deliver simple to complex applications quickly. This was crucial to give developers sufficient freedom and space within the tool to get the best of both worlds.

Coinpaprika, a leading cryptocurrency market data platform, was facing challenges with their existing data management system. They were using InfluxDB for their time-series data and MySQL for transactional data. However, as their data volume grew, they encountered several issues with InfluxDB. The team found it difficult to extract useful metrics from the system, and extending the timeframe for queries often led to server overload. They also experienced problems with response times due to merging data blocks. The open-source version of InfluxDB lacked built-in replication and scalability, which were critical for Coinpaprika's infrastructure. Coinpaprika needed a solution that could handle their increasing data volume, provide useful metrics, and offer improved performance and scalability.

Modern enterprises are investing heavily in security products due to the increasing cyber risks and their impact on businesses. A typical large enterprise today uses more than a dozen security technologies, which emit data in various shapes and sizes, making it difficult to make sense of the data. Security Information and Event Management (SIEM) systems, designed for immutable time series event data, struggle with the mutable nature of security data. For instance, the state of an alert could change from 'open' to 'closed', and SIEMs cannot update this change. The solution is to re-insert the updated data and query the most recent data, which is challenging on append-only systems like SIEMs. Additionally, SIEM companies have stopped innovating and investing in solving basic problems such as data normalization. Dassana, a security data lake, aims to address these challenges.

DeepL, a language translation service, was looking to enhance its analytics capabilities in a privacy-friendly manner in 2020. The company wanted to self-host a solution that could handle large amounts of data and provide quick query times. They evaluated several options, including the Hadoop world, but found it too maintenance-intensive and time-consuming to set up. DeepL also wanted to automate the process of changing table schemas when frontend developers created new events, which would have otherwise overwhelmed the team. The company needed a system that could handle complex events and queries to understand user interactions, something that traditional tools like Google Analytics couldn't provide. Additionally, DeepL wanted to maintain full control over the data while keeping user privacy in mind.

DENIC eG, the administrator and operator of the German namespace on the Internet, was facing challenges in improving the user experience of the internet community due to limitations in data analytics. The data relevant for their analytics was distributed among relational databases, server log data, and various other sources. These sources were already used for monitoring and system improvements, but their analytical features were limited and cross-evaluations across a wide range of sources were costly or not feasible. The initial steps of developing the data science platform involved using a database based on a relational DBMS. The data from different sources was consolidated by Python agents in containers on Kubernetes and the results were written to target tables in the database. This approach resulted in a considerable number of target tables and containers, which were difficult to administer and became somewhat overcomplicated. Furthermore, relational databases were only suitable for larger amounts of data to a limited extent, as the processing time of a query could take several minutes to hours.

HIFI, a company providing financial and business insights to music creators, was facing challenges with its data management system. The company ingests a significant amount of royalty data, with a single HIFI Enterprise account having over half a gigabyte of associated royalty data representing over 25 million rows of streaming and other transaction data. This data needs to load into the user interface as soon as a customer logs in, and there can be multiple customers logging in simultaneously. Previously, it could take up to 30 seconds to load the data, and sometimes it would not load at all due to timeouts. HIFI was using Google Cloud's BigQuery (BQ) to store royalty data, but the pricing structure of BQ was a major challenge. It discouraged data usage and contradicted HIFI's data-driven values. Google's solution to purchase BQ slots ahead of time was not feasible for HIFI as a startup, as usage patterns could change dramatically week to week.

Instabug, an SDK that provides a suite of products for monitoring and debugging performance issues throughout the mobile app development lifecycle, faced significant challenges with performance metrics. These metrics heavily relied on frequent and vast events, posing a challenge in receiving and efficiently storing these events. Additionally, the raw format of performance events was not useful for users, requiring heavy business logic for querying and data visualization. Instabug's backend is large scale, with APIs averaging approximately 2 million requests per minute and terabytes of data going in and out of their services daily. When building their Application Performance Monitoring (APM), they realized it would be their largest scale product in terms of data. They were storing approximately 3 billion events per day at a rate of approximately 2 million events per minute. They also had to serve complex data visualizations that depended heavily on filtering large amounts of data and calculating complex aggregations quickly for user experience. Initially, they designed APM like their other products, but faced performance issues with Elasticsearch, especially for reads, and writes were also not fast enough to handle their load.

Opensee, a financial technology company, was founded by a team of financial industry and technology experts who were frustrated by the lack of simple big data analytics solutions that could efficiently handle their vast amounts of data. Financial institutions have always stored large amounts of data for decision-making processes and regulatory reasons. However, since the financial crisis, regulators worldwide have significantly increased reporting requirements, insisting on longer historical ranges and deeper granularity. This has led to an exponential increase in data, forcing financial institutions to review and upgrade their infrastructure. Unfortunately, many of the storage solutions, such as data lakes built on a Hadoop stack, were too slow for at-scale analytics. Other solutions like in-memory computing solutions and query accelerators presented issues with scalability, high hardware costs, and loss of granularity. Financial institutions were thus forced into a series of compromises.

WorkForce Software, a global provider of integrated employee experience and workforce management solutions, faced a significant challenge during the COVID-19 pandemic. Despite their rapid growth during this period, the company's go-to-market (GTM) strategy was traditional and leads-based, which was not creating the desired customer experience. The company had much of their content gated to gather leads and email addresses, which were then flooded with unsolicited content. This approach was not only intrusive but also ineffective in creating a positive customer experience. Furthermore, the company lacked the necessary data to properly target their customers and understand their position in the buyer journey. Their overall approach was too generic, and they lacked the tools to reach the right decision-makers.

Chinook Seedery, a company dedicated to providing the best sunflower seed experience, was facing challenges in managing its food manufacturing business. The company was struggling to maintain consistent profits, increase sales, and keep customers satisfied. The founder, Mark Pettyjohn, had a deep understanding of how to create great sunflower seed snacks, but lacked the necessary knowledge to manage the business side of the operation. The company was in desperate need of real-time, accurate data to base crucial business decisions. Additionally, the company was dealing with issues related to late or incomplete orders, imprecise invoices, and penalties paid to distributors for late orders. These issues were negatively impacting Chinook’s cash flow and customer trust.

Sweet Jubilee Gourmet, a boutique chocolate product manufacturer, was facing challenges in managing its rapid expansion and meeting the requirements of new national retail customers. The company was struggling with lot traceability, a crucial factor for succeeding in Safe Quality Food (SQF) audits. Their traditional pencil-and-paper method was proving to be time-consuming, error-prone, and inadequate for the speed and accuracy required in a recall situation. Additionally, Sweet Jubilee was having difficulty tracking their existing inventory levels and planning for their production runs. With hundreds of products and ingredients, they were experiencing stock outs, where a key ingredient or packaging component was unavailable on the production day. This was hindering their ability to plan for large order runs for larger customers and ensure timely and consistent delivery.