# Hevo Data

**Source:** https://geo.sig.ai/brands/hevo-data  
**Vertical:** Modern Data Stack & Analytics Engineering  
**Subcategory:** Data Integration & ELT  
**Tier:** Challenger  
**Website:** hevodata.com  
**Last Updated:** 2026-04-14

## Summary

No-code data pipeline and real-time ELT platform; San Francisco/Bengaluru; raised $42M+ from Sequoia India; supports 150+ data sources; enables analysts and non-engineers to connect data sources and load into cloud warehouses without writing connector code.

## Company Overview

Hevo Data is a no-code data pipeline and real-time ELT platform founded in 2017 and headquartered in San Francisco, California, with core engineering in Bengaluru, India. The company was founded by Manish Jethani and Sourabh Agarwal to provide a simpler alternative to complex, code-heavy data pipeline tools for data teams that do not have dedicated data engineering resources. Hevo's platform enables analysts and non-engineers to connect data sources, define transformations using a visual interface or Python, and load data into cloud data warehouses without writing connector code or managing pipeline infrastructure.\n\nHevo raised $42 million in funding from investors including Sequoia Capital India, Qualgro, and Unusual Ventures. The platform supports more than 150 data sources including databases, SaaS applications, advertising platforms, payment processors, and custom webhooks. Its real-time data pipeline engine processes and delivers data with sub-minute latency for streaming sources, making it suitable for analytics use cases that require near-real-time freshness. Hevo's automatic schema management handles changes in source schemas without pipeline failures, addressing one of the most common maintenance burdens for data teams.\n\nHevo positions itself as a cost-effective alternative to Fivetran and Stitch for mid-market companies and growing data teams that need broader connector coverage at lower price points. The platform's transformation capabilities include a visual mapping interface for simple field transformations, a Python transformer for complex data manipulation, and dbt integration for warehouse-native transformations. Hevo is particularly popular in the Asia-Pacific market and among companies with significant SaaS-to-warehouse integration needs.

## Frequently Asked Questions

### How does Hevo Data differ from Fivetran?
Hevo and Fivetran both offer managed data pipelines with pre-built connectors, but they differ in pricing, connector depth, and transformation capabilities. Hevo offers more competitive pricing for growing teams and includes an in-pipeline Python transformation environment that Fivetran does not. Fivetran generally has deeper connector maintenance and wider enterprise support. Hevo is often chosen by mid-market companies that need broad connector coverage at a lower cost.

### What transformation options does Hevo Data provide?
Hevo offers three transformation modes: a visual field mapper for simple renames and type conversions, a Python transformer for writing custom transformation logic inside the pipeline before data lands in the warehouse, and native dbt integration for running warehouse-native transformations after data is loaded. Teams can combine all three approaches in a single pipeline workflow.

### How does Hevo handle real-time streaming data?
Hevo's pipeline engine supports both batch and streaming data sources. For streaming sources like Kafka, webhooks, or change data capture from databases, Hevo delivers events to the destination warehouse with sub-minute latency. Events are processed in micro-batches and written to the destination as they arrive, enabling analytics on near-real-time data without requiring a separate stream processing infrastructure.

### What makes Hevo Data different from other ELT tools?
Hevo Data differentiates through its no-code interface, automatic schema mapping, built-in data transformation capabilities within the pipeline, and a broad library of 150+ pre-built connectors covering SaaS applications, databases, and cloud storage — providing an accessible, all-in-one data integration platform particularly suited to mid-market data teams.

### Does Hevo Data support real-time data replication?
Yes, Hevo supports real-time data replication using CDC for databases and webhook-based ingestion for event sources, in addition to scheduled batch ingestion, allowing teams to choose the freshness model appropriate for each pipeline.

### What destinations does Hevo Data support?
Hevo supports data loading into major cloud data warehouses including Snowflake, BigQuery, Amazon Redshift, Databricks, and Firebolt, as well as databases and data lakes, making it compatible with the most commonly used analytical destinations.

### How does Hevo handle data transformations?
Hevo provides an in-pipeline transformation layer where users can write Python code to clean, enrich, or restructure data in transit before it is loaded into the destination, supporting both simple field mapping and complex custom logic without requiring a separate transformation tool.

### Who is Hevo Data best suited for?
Hevo is best suited for mid-market companies and growing startups with data teams that need reliable, low-maintenance data pipelines with broad connector coverage but without the engineering resources to build and maintain custom integrations or manage complex open-source ETL infrastructure.

## Tags

data-warehouse, analytics, saas, b2b, platform, cloud-native, api-first, no-code, startup, asia-pacific

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*Data from geo.sig.ai Brand Intelligence Database. Updated 2026-04-14.*