A NEW CLASS OF DATA
Defining the Hyperscale Dataset
"Hyperscale" is more than just size. It describes a specific class of data, defined by its unique nature and use case, that overwhelms and breaks data architectures, making them ineffective, or forces you to manipulate the data, lowering its accuracy, usefulness, and value.
By Scale: Billions of Records
A single, monolithic dataset containing billions, if not trillions, of individual records, occupying terabytes to petabytes of storage at rest. This is not a collection of small tables; it's a vast, unified whole. Unreduced Hyperscale Datasets are often larger than the rest of the Big Data in the Data Lake combined.
By Nature: Immutable & Append-Only
This data is almost exclusively machine-generated (logs, telemetry, sensor readings, transaction events). It is written once and never modified, following a WORM (Write Once, Read Many) model. Concepts like CRUD and ACID are irrelevant and add unnecessary overhead.
By Use: The Raw Source of Truth
It is the "Data-Centric" ideal—the highest-resolution, raw source of truth that all downstream applications, from AI models to security analytics and business intelligence, should query directly and on-the-fly.
All Data Becomes Structured at Rest
One of the most critical aspects of hyperscale data is how even "unstructured" content is transformed into a highly structured format for machine consumption. This predictability is key to enabling new, high-performance architectures.
Text and Documents
Raw text from documents, chats, or articles are rarely stored as-is for large-scale analysis. It is broken down into structured components. For search, it becomes a search index (like an inverted index) mapping keywords to document locations. For AI and semantic understanding, it is transformed into numerical vectors that capture its meaning, ready for use in models.
Images, Video, and Audio
Similarly, rich media is converted into structured data for analysis. Images and video frames are processed into vectors representing their content, objects, and scenes. Audio is transformed into spectrograms or feature vectors. In all cases, the unstructured source becomes a structured, machine-readable dataset at rest. This structured, immutable nature is the universal constant of hyperscale data.