General FAQs

This FAQ is designed to help prospective and current customers quickly find answers about Reactor's Intelligent ETL platform, capabilities, setup, and differentiation.

Getting Started

Q: What is Reactor Data's Reactor platform?

A: Reactor is a modern ETL pipeline platform built on streaming technologies like Kafka, enabling real-time and batch data ingestion, transformation, and delivery to your data warehouse or lake. Reactor helps replace legacy tools like Fivetran or DBT by simplifying operations and reducing cost.

Q: Do I need to install anything to use Reactor?

A: No installation is needed. Reactor is a cloud-native platform connecting your sources and destinations via managed connectors or our ingestion API. You can configure everything through our UI.

Q: How do I set up a new connector source?

A: Visit the “Sources” section in the Reactor UI, select a supported connector, follow the credentials/authentication prompts, and save the configuration. Reactor Data Support can send you help articles for each connector if requested. We will eventually have a KB article for each connector.

Connectors & Integrations

Q: What types of data sources does Reactor support?

A: Reactor supports SaaS apps (e.g., Shopify, NetSuite, Iterable), databases (e.g., Postgres, MySQL), and APIs. Our connector library is based on Airbyte OSS with additional enhancements for schema evolution, metadata tracking, and Google-native deployment.

Q: Can I request a custom connector?

A: Yes, Reactor offers rapid connector development for niche sources or legacy systems. Custom connector work may require onboarding or SOW implementation.

Q: Does Reactor support destinations like Snowflake and BigQuery?

A: Yes. Reactor natively supports Snowflake (via Snowpipe), BigQuery, and other popular warehouses like Redshift and Databricks.

Transformation & Mapping

Q: How does Reactor handle data transformations?

A: Reactor supports both its native expression language and Python-based expressions to transform data between the source and the destination schema and in intermediate models. These transformations are defined during the mapping step and can be reused across pipelines.

Q: Can I manage schema drift and evolution?

A: Yes. Reactor users can sync with their destinations to update destination schemas in the transformation layer, and work with Electron, Reactor’s AI assistant, to update mapping configurations to reflect the new destination schema. A source API or schema change will not break a pipeline or a downstream table loading.

Electron AI Assistant

Q: What is Electron?

A: Electron is Reactor’s AI assistant that helps you manage data mappings, generate transformation logic, and answer questions about your data sources, destinations, and functions.

Q: What can Electron’s agents do?

A: Electron has agents for mapping assistance, mapping generation, and data lineage evaluation. You can interact with Electron to auto-suggest mapping expressions or review downstream impact of schema changes.

Pricing & Usage

Q: How is Reactor priced?
A: Reactor uses a fixed-cost, transparent annual pricing model based on an aggregate volume of records (or orders) across all connectors. While it considers usage volume like MAR-based models, pricing is determined once annually and does not fluctuate monthly per connector. This simplifies budgeting and eliminates surprise overages, offering a predictable, cost-efficient alternative to usage-based ETL platforms.

Q: Is there a free trial?
A: Yes. Reactor offers a free trial with guided onboarding so you can evaluate the platform and test connectors with your real data.

Q: How does Reactor compare to Fivetran or DBT?
A: Unlike Fivetran, Reactor doesn’t bill monthly based on MARs per connector. Instead, we calculate pricing using your estimated annual record volume across all sources, giving you a simpler, flat-rate fee. Unlike DBT, Reactor provides an integrated solution for ingest, transform, and load—all built on streaming architecture with schema-awareness and lineage tracking natively available in the UI.

Platform Architecture

Q: Is Reactor built on open source?

A: Yes. Reactor leverages Airbyte OSS connectors, Apache Kafka, and is built on Google Cloud Platform. It augments open source with proprietary enhancements for pipeline management, observability, and AI automation.

Q: Can Reactor be deployed in a private cloud or on-premises?

A: At this time, Reactor is a fully managed SaaS platform hosted on GCP. Private cloud deployment is under consideration for enterprise clients.

Q: What happens if a source or destination schema changes?

A: Reactor users can sync with their destinations to update destination schemas in the transformation layer, and work with Electron, Reactor’s AI assistant, to update mapping configurations to reflect the new destination schema. A source API or schema change will not break a pipeline or a downstream table loading.

Security & Compliance

Q: Is Reactor SOC 2 compliant?

A: Yes. Reactor is SOC-2 Type II certified. Security is a core focus, and we also support VPC peering and other enterprise-grade security features.

Q: How is customer data handled?

A: Reactor securely stores raw event data for reprocessing and applies strict access controls. Customers can configure data retention and export policies.

Support & Services

Q: Do you offer onboarding help?

A: Yes. All customers receive onboarding assistance. For larger implementations or complex mappings, we offer premium services or partner integrations.

Q: How can I get support?

A: You can submit a request via the in-app support or reach us at support@reactordata.com. We also offer a Help Center with step-by-step articles.