Denormalized

About Denormalized

Denormalized is a real-time data platform that utilizes DataFusion's in-memory query engine and SlateDB for efficient single-node processing of large data streams, eliminating the complexity of distributed systems. It enables developers to easily ingest and analyze terabytes of data with minimal operational overhead and fast deployment through a simple Python SDK or Rust crate.

```xml <problem> Real-time data processing often requires complex distributed systems, leading to increased operational overhead and infrastructure costs. Managing and deploying these systems can be challenging, hindering developers from quickly analyzing large data streams. </problem> <solution> Denormalized offers a real-time data platform that simplifies data processing by leveraging DataFusion's in-memory query engine and SlateDB for efficient single-node operation. This approach eliminates the need for distributed systems, allowing developers to ingest and analyze terabytes of data with minimal operational complexity. By scaling up to modern hardware, Denormalized provides blazing-fast performance and cost-effective data processing. The platform is designed for easy deployment and management, enabling developers to focus on data analysis rather than infrastructure management. </solution> <features> - Utilizes DataFusion's in-memory query engine based on Apache Arrow for efficient vectorized computation. - Employs SlateDB to offload state management over cloud object stores, enabling processing of terabytes of data on a single node. - Supports job checkpointing to the cloud for enhanced reliability and seamless continuation of processing. - Available as both a Python SDK and Rust crate for flexible integration. - Designed for single-node processing, simplifying deployment and management with zero operational overhead. </features> <target_audience> The primary audience includes data engineers, data scientists, and developers who need to process and analyze real-time data streams efficiently without the complexities of distributed systems. </target_audience> ```

What does Denormalized do?

Denormalized is a real-time data platform that utilizes DataFusion's in-memory query engine and SlateDB for efficient single-node processing of large data streams, eliminating the complexity of distributed systems. It enables developers to easily ingest and analyze terabytes of data with minimal operational overhead and fast deployment through a simple Python SDK or Rust crate.

Where is Denormalized located?

Denormalized is based in San Francisco, United States.

When was Denormalized founded?

Denormalized was founded in 2022.

How much funding has Denormalized raised?

Denormalized has raised 500000.

Location
San Francisco, United States
Founded
2022
Funding
500000
Employees
2 employees
Major Investors
Y Combinator
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Denormalized

Score: 100/100
AI-Generated Company Overview (experimental) – could contain errors

Executive Summary

Denormalized is a real-time data platform that utilizes DataFusion's in-memory query engine and SlateDB for efficient single-node processing of large data streams, eliminating the complexity of distributed systems. It enables developers to easily ingest and analyze terabytes of data with minimal operational overhead and fast deployment through a simple Python SDK or Rust crate.

denormalized.io200+
cb
Crunchbase
Founded 2022 – San Francisco, United States

Funding

$

Estimated Funding

$500K+

Major Investors

Y Combinator

Team (<5)

No team information available. Click "Fetch founders" to run a focused founder search.

Company Description

Problem

Real-time data processing often requires complex distributed systems, leading to increased operational overhead and infrastructure costs. Managing and deploying these systems can be challenging, hindering developers from quickly analyzing large data streams.

Solution

Denormalized offers a real-time data platform that simplifies data processing by leveraging DataFusion's in-memory query engine and SlateDB for efficient single-node operation. This approach eliminates the need for distributed systems, allowing developers to ingest and analyze terabytes of data with minimal operational complexity. By scaling up to modern hardware, Denormalized provides blazing-fast performance and cost-effective data processing. The platform is designed for easy deployment and management, enabling developers to focus on data analysis rather than infrastructure management.

Features

Utilizes DataFusion's in-memory query engine based on Apache Arrow for efficient vectorized computation.

Employs SlateDB to offload state management over cloud object stores, enabling processing of terabytes of data on a single node.

Supports job checkpointing to the cloud for enhanced reliability and seamless continuation of processing.

Available as both a Python SDK and Rust crate for flexible integration.

Designed for single-node processing, simplifying deployment and management with zero operational overhead.

Target Audience

The primary audience includes data engineers, data scientists, and developers who need to process and analyze real-time data streams efficiently without the complexities of distributed systems.