Cube Dev Case Studies Cuboh's Transformation: From Latency to Efficiency with Cube

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	Cuboh's Transformation: From Latency to Efficiency with Cube Cube Dev

Cuboh's Transformation: From Latency to Efficiency with Cube

Cube Dev

Technology Category	Application Infrastructure & Middleware - Database Management & Storage Infrastructure as a Service (IaaS) - Cloud Databases
Applicable Industries	Buildings Construction & Infrastructure
Use Cases	Time Sensitive Networking
Services	System Integration
Challenge	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. Read More
About Customer	Cuboh is a seed-stage restaurant-tech company based in British Columbia, Canada. The company offers a SaaS that integrates delivery apps with point-of-sales systems, consolidating them into a single tablet. This technology also provides centralized reporting and menu management. Cuboh serves traditional restaurants, ghost kitchens, and chains, saving them from the complexities of delivery processing. The platform has processed over $1B in transactional volume, equating to over 50 million orders. Despite being a relatively small company with 51-100 employees, Cuboh has a significant market fit and demand. Read More
Solution	Cuboh discovered Cube in early 2022 and recognized its potential to address their challenges. Cube's heavy caching capabilities for date-based query structures were particularly appealing. Two team members facilitated Cube’s implementation on top of Cuboh’s IT infrastructure as a microservice that runs on its own AWS EC2 instance, all within less than a year. Cube’s compatibility with relational databases, its instant RESTful API, and its advanced, two-level caching system met Cuboh’s technical requirements. This allowed them to provide efficient reporting to their customers while reducing costs. Cube’s pre-aggregation structure resulted in fewer query metrics directly hitting the database, reducing the time required to generate real-time and historical reports from tens of seconds to less than 2. The team also found that endpoint querying and structuring reports were much easier with Cube, eliminating the need for writing additional back-end code to support specific endpoints. Read More Log in to view content
Contents

Technology Category

Application Infrastructure & Middleware - Database Management & Storage

Infrastructure as a Service (IaaS) - Cloud Databases

Applicable Industries

Buildings

Construction & Infrastructure

Use Cases

Time Sensitive Networking

Services

System Integration

Challenge

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.

About Customer

Cuboh is a seed-stage restaurant-tech company based in British Columbia, Canada. The company offers a SaaS that integrates delivery apps with point-of-sales systems, consolidating them into a single tablet. This technology also provides centralized reporting and menu management. Cuboh serves traditional restaurants, ghost kitchens, and chains, saving them from the complexities of delivery processing. The platform has processed over $1B in transactional volume, equating to over 50 million orders. Despite being a relatively small company with 51-100 employees, Cuboh has a significant market fit and demand.

Solution

Cuboh discovered Cube in early 2022 and recognized its potential to address their challenges. Cube's heavy caching capabilities for date-based query structures were particularly appealing. Two team members facilitated Cube’s implementation on top of Cuboh’s IT infrastructure as a microservice that runs on its own AWS EC2 instance, all within less than a year. Cube’s compatibility with relational databases, its instant RESTful API, and its advanced, two-level caching system met Cuboh’s technical requirements. This allowed them to provide efficient reporting to their customers while reducing costs. Cube’s pre-aggregation structure resulted in fewer query metrics directly hitting the database, reducing the time required to generate real-time and historical reports from tens of seconds to less than 2. The team also found that endpoint querying and structuring reports were much easier with Cube, eliminating the need for writing additional back-end code to support specific endpoints.

Impact #1	The implementation of Cube has significantly improved Cuboh's operational efficiency. The latency issues that plagued the company's large customer reporting requests have been resolved, leading to faster and more efficient reporting. The compatibility of Cube with relational databases and its instant RESTful API has streamlined Cuboh's operations. The advanced, two-level caching system has also reduced the load on the database, further enhancing performance. Additionally, the ease of endpoint querying and structuring reports with Cube has simplified the process, eliminating the need for additional back-end code. This has not only improved operational efficiency but also reduced costs.

Impact #1

The implementation of Cube has significantly improved Cuboh's operational efficiency. The latency issues that plagued the company's large customer reporting requests have been resolved, leading to faster and more efficient reporting. The compatibility of Cube with relational databases and its instant RESTful API has streamlined Cuboh's operations. The advanced, two-level caching system has also reduced the load on the database, further enhancing performance. Additionally, the ease of endpoint querying and structuring reports with Cube has simplified the process, eliminating the need for additional back-end code. This has not only improved operational efficiency but also reduced costs.

Benefit #1	Reduced time to generate real-time and historical reports from tens of seconds to less than 2 seconds
Benefit #2	Processed over $1B in transactional volume, equating to over 50 million orders
Benefit #3	Saved 14 weeks of engineering time

Benefit #1

Reduced time to generate real-time and historical reports from tens of seconds to less than 2 seconds

Benefit #2

Processed over $1B in transactional volume, equating to over 50 million orders

Benefit #3

Saved 14 weeks of engineering time

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Overview

Cuboh's Transformation: From Latency to Efficiency with Cube

Operational Impact

Quantitative Benefit