Customer Transformations

with the MINTDATA Real-time Engine

IoT Platform at Scale

With over 140 million subscribers, Verizon was faced with three challenges in bringing their consumer IoT platform to market:

• How to create a cloud system that would scale to the size of their user base.
• How to interoperate with their existing network and broadband technologies.
• How to build a system that would be flexible enough to deploy consumer IoT services defined in the future.

To overcome this challenge, Verizon's IoT service was built on the MintData platform, helping Verizon scale to tens of millions of homes.

Human + Artificial = Intelligence

Faced with increasing demands on categorizing and classifying the content of ads in real-time, Yahoo Japan faced a unique challenge:

• How can the tasks be done in near real-time without increasing the size of their moderation workforce?
• How can the system guarantee both accuracy and precision of the resulting work?
• How can new employees be onboarded quickly, across a diverse range of skill sets and backgrounds?

A system that combined artificial intelligence with the power of the human mind was built, such that Yahoo Japan could categorize and classify information without increasing the size of their workforce.

Every Millisecond Matters

With stiff competition from E-Trade, a Goldman Sachs entity faced a key challenge: how to offer advanced trading features without increasing latency on their trading platform.

The specific challenges included:

• How to react to over 1 million messages per second
• How to re-balance customer portfolios in real-time
• How to satisfy regulatory concerns for order execution

Foundational research was performed and an HFT (high frequency trading) core was built to address the scalability and regulatory challenges. This research then led to the creation of the MintData real-time engine.

File Systems, Distributed

Facing stiff competition in the consumer file storage market, Hightail needed to modernize their infrastructure to reduce cost and rapidly introduce new features. In doing this, Hightail needed to solve key technical problems at scale:

• How to build a robust, distributed file system for 40 million concurrent users.
• How to facilitate rapid feature delivery without downtime in a live system.
• How to deliver search, audit, sharing, security and other features at a rapid pace.

To address the above challenges, a number of key distributed systems were built to scale horizontally while providing key functionality to deploy new features in a live system.

Make sense of data with a spreadsheet from the future.