Scratch Data

ScratchDB is a platform that automates all aspects of managing an analytical database: server configuration, data ingestion, queries, replication, and sharding. We are built on top of Clickhouse with a developer experience that you'll love.

High-Level Overview

Scratch Data is a New York-based startup founded in 2021 that provides an analytics platform designed for builders and developers to record and query billions of datapoints efficiently. Its product, ScratchDB, automates the management of analytical databases, including server configuration, data ingestion, queries, replication, and sharding. Built on top of Clickhouse, it emphasizes a developer-friendly experience aimed at simplifying complex data workflows for B2B SaaS and analytics use cases. The platform targets developers and engineering teams who need scalable, performant analytics infrastructure without the operational overhead. Although currently inactive, Scratch Data showed promise in addressing the challenges of managing large-scale analytical data with ease and speed[1].

Origin Story

Scratch Data was founded in 2021 by Jay Goel and Len Boyette. Jay Goel brought experience leading engineering teams at Frame.io, Squarespace, and Rent the Runway, while Len Boyette was an early engineer at Okta, where he founded the developer experience team and built platforms supporting hundreds of Okta integrations. The idea for Scratch Data emerged from their combined expertise in developer tooling and scalable infrastructure, focusing on creating a seamless experience for managing analytical databases. Early traction included participation in Y Combinator’s Summer 2021 batch, signaling validation from a leading startup accelerator. Despite this, the company is currently listed as inactive[1].

Core Differentiators

• Product Differentiators: ScratchDB automates all aspects of analytical database management, including server setup, data ingestion, query execution, replication, and sharding, reducing operational complexity.
• Developer Experience: Built on Clickhouse, the platform prioritizes ease of use and developer-friendly interfaces, aiming to make complex analytics accessible without deep database management expertise.
• Scalability and Performance: Designed to handle billions of datapoints efficiently, supporting high-scale analytics workloads.
• Integration and Ecosystem: Leverages open-source foundations and aims to integrate smoothly into existing developer workflows, benefiting from founders’ backgrounds in developer platforms and integrations[1].

Role in the Broader Tech Landscape

Scratch Data rides the growing trend of democratizing data analytics and infrastructure automation for developers. As companies increasingly rely on data-driven decision-making, the demand for scalable, easy-to-manage analytical databases grows. The timing aligns with the rise of cloud-native data platforms and the need to reduce operational burdens on engineering teams. By automating complex database management tasks, Scratch Data aimed to empower developers to focus on building analytics applications rather than managing infrastructure. This approach fits within the broader movement toward serverless and managed data services, influencing how startups and enterprises handle big data analytics[1].

Quick Take & Future Outlook

Although currently inactive, Scratch Data’s vision to simplify large-scale analytics infrastructure remains highly relevant. Future success in this space depends on continued innovation in automation, developer experience, and integration with evolving data ecosystems. Trends such as real-time analytics, edge computing, and AI-driven data management could shape the next phase of platforms like Scratch Data. If reactivated or relaunched, leveraging its founders’ strong engineering backgrounds and YC pedigree could position it well to capitalize on the growing demand for scalable, developer-friendly analytics solutions.

In summary, Scratch Data represents a focused attempt to transform how builders manage and query massive datasets by automating operational complexity, a critical need in today’s data-centric technology landscape[1].