
Synnada
Building low-touch, self-contained ML systems that drive mission-critical actions in streaming data environments.
Date | Investors | Amount | Round |
---|---|---|---|
- | investor | €0.0 | round |
investor investor investor investor investor | €0.0 | round | |
N/A | - | ||
Total Funding | 000k |
USD | 2022 | 2023 |
---|---|---|
Revenues | 0000 | 0000 |
% growth | - | 106 % |
EBITDA | 0000 | 0000 |
Profit | 0000 | 0000 |
EV | 0000 | 0000 |
EV / revenue | 00.0x | 00.0x |
EV / EBITDA | 00.0x | 00.0x |
R&D budget | 0000 | 0000 |
Source: Dealroom estimates
Related Content
Synnada.ai is a startup that operates in the data infrastructure sector, specifically focusing on AI-native data applications. The company is a significant contributor to Apache Arrow DataFusion, a next-generation data infrastructure platform. Synnada.ai's primary offering is a unified data processing solution that is built using the Rust programming language. This solution leverages the Apache Arrow in-memory format to deliver rapid query performance and supports both SQL and Dataframe APIs.
Synnada.ai's data processing solution is highly adaptable, with compatibility for various data formats including CSV, Parquet, JSON, and Avro. This adaptability, coupled with a supportive community, makes it a robust platform for data processing.
The company's business model revolves around integrating with clients' existing data stacks, including warehouses, lakehouses, orchestration, and metrics layers. It enables clients to build real-time data pipelines within minutes using standard SQL and convert them into live end-to-end applications. This is achieved through Synnada's stream-first data processing technology, which allows seamless work with both at-rest and in-motion data sets.
Synnada.ai's target market includes businesses and organizations that handle large-scale data workloads and require efficient real-time data processing. The company's revenue model likely involves charging clients for access to its data processing platform and for any additional services or features.
Keywords: AI-native data applications, Apache Arrow DataFusion, unified data processing, SQL and Dataframe APIs, CSV, Parquet, JSON, Avro compatibility, real-time data pipelines, stream-first data processing.