About Rockset (acquired by OpenAI)

Rockset is a real-time indexing database that enables continuous ingestion of vector, text, geospatial, and JSON data, allowing for efficient hybrid search and analytics. It provides businesses with millisecond SQL query performance and eliminates the need for ETL processes, significantly reducing operational costs and development time.

```xml <problem> Traditional database systems struggle to efficiently index and query diverse data types like vector embeddings, text, geospatial data, and JSON, hindering the development of real-time search and analytics applications. Extract, transform, load (ETL) processes add complexity and latency, making it difficult to derive insights from rapidly changing data. Existing solutions often require extensive manual tuning, sharding, and index management, increasing operational overhead. </problem> <solution> Rockset is a real-time indexing database designed for converged indexing of vector, text, geospatial, and JSON data, enabling efficient hybrid search and real-time analytics at scale. It eliminates the need for traditional ETL processes by continuously ingesting data from sources like Kafka, MongoDB, DynamoDB, and S3, with built-in connectors for OpenAI and others. Rockset's architecture provides millisecond SQL query performance, allowing developers to build real-time features with standard SQL for search, filtering, aggregations, joins, and vector search. The platform's compute-storage separation enables independent scaling of query performance and storage capacity, optimizing cloud resource utilization and cost. </solution> <features> - Converged Indexing: Indexes vector, text, geospatial, and JSON data in a single database. - Real-time Ingestion: Continuously ingests data from streaming sources and databases with field-level upserts. - Millisecond SQL: Enables fast search, filtering, aggregations, joins, and vector search using standard SQL. - Data APIs: Provides APIs for fast SQL search, aggregations, joins, and vector search. - Compute-Storage Separation: Scales query performance and storage independently. - Schemaless Ingestion: Offers flexibility with schemaless ingestion. - Built-in Connectors: Integrates with Kafka, MongoDB, DynamoDB, S3, and OpenAI. </features> <target_audience> Rockset targets developers and data scientists building search, real-time analytics, and AI applications, including recommendations, personalization, gaming analytics, logistics tracking, anomaly detection, and real-time reporting. </target_audience> ```

What does Rockset (acquired by OpenAI) do?

Where is Rockset (acquired by OpenAI) located?

Rockset (acquired by OpenAI) is based in San Mateo, Philippines.

When was Rockset (acquired by OpenAI) founded?

Rockset (acquired by OpenAI) was founded in 2016.

How much funding has Rockset (acquired by OpenAI) raised?

Rockset (acquired by OpenAI) has raised 44000000.

Location

San Mateo, Philippines

Founded

2016

Funding

44000000

Employees

15 employees

Major Investors

Icon Ventures

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Rockset (acquired by OpenAI)

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Executive Summary

rockset.com 10K+

Crunchbase

Founded 2016 – San Mateo, Philippines

Funding

Estimated Funding

$20M+

Major Investors

Icon Ventures

Team (15+)

No team information available.

Company Description

Problem

Traditional database systems struggle to efficiently index and query diverse data types like vector embeddings, text, geospatial data, and JSON, hindering the development of real-time search and analytics applications. Extract, transform, load (ETL) processes add complexity and latency, making it difficult to derive insights from rapidly changing data. Existing solutions often require extensive manual tuning, sharding, and index management, increasing operational overhead.

Solution

Rockset is a real-time indexing database designed for converged indexing of vector, text, geospatial, and JSON data, enabling efficient hybrid search and real-time analytics at scale. It eliminates the need for traditional ETL processes by continuously ingesting data from sources like Kafka, MongoDB, DynamoDB, and S3, with built-in connectors for OpenAI and others. Rockset's architecture provides millisecond SQL query performance, allowing developers to build real-time features with standard SQL for search, filtering, aggregations, joins, and vector search. The platform's compute-storage separation enables independent scaling of query performance and storage capacity, optimizing cloud resource utilization and cost.

Features

Converged Indexing: Indexes vector, text, geospatial, and JSON data in a single database.

Real-time Ingestion: Continuously ingests data from streaming sources and databases with field-level upserts.

Millisecond SQL: Enables fast search, filtering, aggregations, joins, and vector search using standard SQL.

Data APIs: Provides APIs for fast SQL search, aggregations, joins, and vector search.

Compute-Storage Separation: Scales query performance and storage independently.

Schemaless Ingestion: Offers flexibility with schemaless ingestion.

Built-in Connectors: Integrates with Kafka, MongoDB, DynamoDB, S3, and OpenAI.

Target Audience

Rockset targets developers and data scientists building search, real-time analytics, and AI applications, including recommendations, personalization, gaming analytics, logistics tracking, anomaly detection, and real-time reporting.