Hevo Data eliminates the complexity of building and maintaining data pipelines, enabling businesses to automatically sync data from 150+ sources to their data warehouses in real-time without writing code, allowing data teams to focus on analysis rather than infrastructure.
Last updated Feb 8, 2026
Leading no-code data pipeline platform serving marketing and analytics teams with strong presence in the modern data stack ecosystem
Hevo Data is a no-code data pipeline platform founded in 2017 that automates the process of data integration, extraction, transformation, and loading (ETL/ELT). The platform enables businesses to consolidate data from over 150 sources including databases, SaaS applications, cloud storage, and SDKs into data warehouses and analytics destinations. While not exclusively an AdTech company, Hevo Data plays a significant role in the AdTech ecosystem by enabling marketing and advertising teams to integrate data from various advertising platforms, analytics tools, and customer data sources for unified reporting and analysis. The platform's no-code approach democratizes data integration, allowing marketing teams and data analysts to build and maintain data pipelines without extensive engineering resources. Hevo Data serves as critical infrastructure for AdTech operations by connecting advertising platforms like Google Ads, Facebook Ads, LinkedIn Ads, and other marketing tools to data warehouses such as Snowflake, BigQuery, Redshift, and Databricks. This enables companies to perform cross-channel marketing attribution, campaign performance analysis, and customer journey mapping. The company has positioned itself as a modern alternative to traditional ETL tools, focusing on real-time data replication, automated schema mapping, and pre-built transformations that reduce the time-to-insight for marketing and analytics teams.
Pre-built connectors for 150+ data sources including advertising platforms, databases, SaaS applications, and cloud storage with automated data replication
Python and SQL-based data transformation capabilities with drag-and-drop interface for data cleaning, enrichment, and modeling
Automated workflow orchestration for scheduling, monitoring, and managing data pipeline execution
Near real-time data replication with change data capture (CDC) for databases and streaming data ingestion
Automated data quality checks, schema evolution handling, and pipeline monitoring with alerts