About Prophecy

Prophecy offers a Data Transformation Copilot that enables users to build, deploy, and monitor data pipelines using an AI-powered visual interface that generates native Spark or SQL code. This platform addresses the challenge of inefficient data processing by allowing business users to self-serve, significantly reducing reliance on data engineers and accelerating analytics workflows.

```xml <problem> Data processing bottlenecks often arise from the need for specialized data engineers to build and maintain complex data pipelines. This reliance on technical experts slows down analytics workflows and prevents business users from directly accessing and manipulating data. Traditional coding-based approaches can be difficult for non-programmers to understand and modify, creating a barrier to self-service analytics. </problem> <solution> Prophecy offers a Data Transformation Copilot that empowers both business and data platform teams to collaboratively build, deploy, and monitor data pipelines. The platform features an AI-powered visual interface that generates native Spark or SQL code, enabling users to create data transformations using a drag-and-drop canvas. AI assistance is integrated throughout the visual designer, providing recommendations, converting natural language to business logic, completing pipelines, generating tests and documentation, and suggesting fixes for errors. This approach reduces the need for extensive coding knowledge and accelerates the development and deployment of data pipelines. </solution> <features> - AI-powered visual designer for building data pipelines without extensive coding - Generation of native Spark or SQL code from visual workflows - Integration with dbt core™ for easy editing of existing SQL projects - AI assistance for code completion, error detection, and documentation - Support for version control, testing, and software best practices through Git integration - Centralized data pipeline development and deployment for data platform teams - Plugin architecture for extending the copilot with custom Spark and SQL code - Metadata management, data quality monitoring, and lineage tracking - Flexible deployment options, including public cloud, private cloud, and on-premises </features> <target_audience> The primary target audience includes data analysts, business intelligence professionals, and data engineers within enterprises seeking to accelerate data processing and enable self-service analytics. </target_audience> ```

What does Prophecy do?

Where is Prophecy located?

Prophecy is based in San Francisco, United States.

When was Prophecy founded?

Prophecy was founded in 2017.

How much funding has Prophecy raised?

Prophecy has raised 115640000.

Location

San Francisco, United States

Founded

2017

Funding

115640000

Employees

160 employees

Major Investors

Smith Point Capital

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Prophecy

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Executive Summary

prophecy.io 20K+

Crunchbase

Founded 2017 – San Francisco, United States

Funding

Estimated Funding

$100M+

Major Investors

Smith Point Capital

Team (100+)

No team information available.

Company Description

Problem

Data processing bottlenecks often arise from the need for specialized data engineers to build and maintain complex data pipelines. This reliance on technical experts slows down analytics workflows and prevents business users from directly accessing and manipulating data. Traditional coding-based approaches can be difficult for non-programmers to understand and modify, creating a barrier to self-service analytics.

Solution

Prophecy offers a Data Transformation Copilot that empowers both business and data platform teams to collaboratively build, deploy, and monitor data pipelines. The platform features an AI-powered visual interface that generates native Spark or SQL code, enabling users to create data transformations using a drag-and-drop canvas. AI assistance is integrated throughout the visual designer, providing recommendations, converting natural language to business logic, completing pipelines, generating tests and documentation, and suggesting fixes for errors. This approach reduces the need for extensive coding knowledge and accelerates the development and deployment of data pipelines.

Features

AI-powered visual designer for building data pipelines without extensive coding

Generation of native Spark or SQL code from visual workflows

Integration with dbt core™ for easy editing of existing SQL projects

AI assistance for code completion, error detection, and documentation

Support for version control, testing, and software best practices through Git integration

Centralized data pipeline development and deployment for data platform teams

Plugin architecture for extending the copilot with custom Spark and SQL code

Metadata management, data quality monitoring, and lineage tracking

Flexible deployment options, including public cloud, private cloud, and on-premises

Target Audience

The primary target audience includes data analysts, business intelligence professionals, and data engineers within enterprises seeking to accelerate data processing and enable self-service analytics.