Skip to content
Datacoral was acquired by Cloudera.
Brief
Datacoral

Datacoral

Datacoral automated the creation and management of data pipelines, eliminating the need for extensive data engineering resources and reducing time-to-insight for analytics teams.

datacoral.comSan Francisco, California, United StatesFounded 2016

Last updated May 11, 2026 by ATDb automated enrichment

Industry
Data Infrastructure
Business Model
SaaS
Target Market
Mid-Market, Enterprise
Employee Count
11-50
Funding
$10M
Parent Company
Cloudera
API Available
Yes
Market Position

Niche player in automated data pipeline management before acquisition

Overview

Datacoral was a data infrastructure company that provided an automated platform for building, managing, and monitoring data pipelines. Founded by former Facebook engineers, the company focused on simplifying the complex process of data engineering by offering a managed service that automated much of the infrastructure work traditionally required to move and transform data. The platform was designed to help data teams spend less time on infrastructure maintenance and more time on analytics and insights. Datacoral's solution addressed the growing need for reliable data infrastructure as companies increasingly relied on data-driven decision making. The platform supported various data sources and destinations, providing orchestration, monitoring, and data quality features out of the box. While not strictly an AdTech company, Datacoral served clients across multiple industries including those in the advertising and marketing technology space who needed robust data infrastructure for their analytics and reporting needs. In 2021, Datacoral was acquired by Cloudera, a leading enterprise data cloud company. Following the acquisition, Datacoral's technology and team were integrated into Cloudera's broader data platform offerings, and the Datacoral brand ceased to operate as a distinct entity. The acquisition strengthened Cloudera's data engineering and pipeline automation capabilities.

Products & Features

Automated Data Pipelines

Automated creation and management of data pipelines from various sources to destinations

Data Orchestration

Workflow orchestration and scheduling for data processing tasks

Monitoring and Alerting

Real-time monitoring of data pipeline health with automated alerting

Data Quality Checks

Built-in data quality validation and testing capabilities

Key Features
Automated pipeline generationMulti-source data integrationBuilt-in orchestrationData quality monitoringAWS-native architectureSQL-based transformations
Use Cases
Marketing analytics data consolidationCustomer data platform integrationBusiness intelligence data preparationCross-platform reportingData warehouse automation
Customer Segments
Data-driven enterprisesMarketing technology companiesE-commerce platformsSaaS companiesDigital media companies

Explore further

2 views