Reactor

The Reactor platform

Preserve. Resolve.
Ship.

The platform that preserves your raw history, resolves meaning across every source, and ships governed data products ready for AI, analytics, and activation.

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500+ integrationsSOC 2 alignedHours to value

By the numbers

Built for scale.

100K+

Data transformations

Processed monthly across customer pipelines.

100B+

Events processed

Streamed and modeled at warehouse scale.

55%

Cloud cost savings

Average reduction vs traditional ETL stacks.

250ms

Transformation time

P50 latency from source to warehouse.

Key benefits

Built for data that evolves with you.

Reduce warehouse waste and keep your data model adaptable as the business changes.

Electron AI

AI-powered pipelines that build themselves.

Electron AI writes mappings in natural language, suggests joins, and tags data automatically, your data team stays focused on strategy.

Electron AI
“Map shopify.orders.customer_email to common.customer.email”
Mapping created
Shared meaning

Shared business meaning across every source.

Define customer, order, and product entities once. Every team works from the same data.

Data + metadata

Data and metadata move together.

Lineage and context flow with the data through every step, not bolted on later.

Data replay

Future-proof your data.

When the business changes, reinterpret history instead of rebuilding from scratch. Replay and remap previously collected data without re-pulling it from source.

Modern stack

Part of your modern stack.

Lands directly in Snowflake, BigQuery, or Databricks, shaped for your exact AI, analytics, or activation use cases.

Platform capability

Electron AI: your agentic co-pilot.

Reactor's Electron is your intelligent co-pilot for data engineering. Describe what you need in natural language and Electron generates the mapping expressions, refining its output until it matches your needs.

  • Natural Language Interface
  • Contextual Understanding
  • Iterative Refinement
Explore Electron AI
U
Map the customer email field across all my Shopify and Stripe sources to a common identity table.
Electron
// 12 mappings generated
LOWER(TRIM(shopify.orders.customer_email)) → common.identity.email
LOWER(TRIM(stripe.charges.receipt_email)) → common.identity.email
+10 more...
Deployed2.3s

Shift-left mapping

Resolve shared meaning at ingest.

Reactor's mapping engine resolves customer, order, and product meaning as soon as raw data is collected. Define shared entities once and Reactor applies them across every source, so context and governance flow through every downstream system.

  • Ingest Mapping for Context
  • Real-time Data Cataloging
  • Standardized Field-level Tagging
Source schema → common entity
shopify.orders.customer_emailcommon.customer.email
stripe.charges.receipt_emailcommon.customer.email
shopify.orders.total_pricecommon.order.value
salesforce.leads.emailcommon.customer.email
hubspot.contacts.emailcommon.customer.email
Unified stream
DATA
{ "amount": 49.99, "currency": "USD" }
META
{ "source": "stripe", "ingested_at": "2026-05-26T14:02:33Z" }
LINEAGE
{ "schema_v": 3, "mapped_by": "electron-ai" }
QUALITY
{ "completeness": 1.0, "tier": "gold" }

Stream architecture

Data and metadata, in stream.

A unified data set with aligned conventions gives Reactor users superpowers. Map data using common semantics so fields and values stay consistent across every source, with full lineage attached.

Compare to other platforms
TodayRemap shopify_v2 → common.order1.2M
YesterdayReplay 90 days of history240M
Last weekAdd ML feature to old data847M
Last monthReprocess for new use case1.4B
Future-proof

Mappings and models, ready for anything.

When the business changes, replay and remap the history you already collected instead of starting over. Every record is logged immutably, so you can reprocess for new use cases without loading source systems or burning warehouse compute.

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FAQ

Got questions?

Answers to common questions about Reactor, replayable history, and shared data meaning.

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01What is the Reactor Platform?
The Reactor Platform is an AI-powered, intelligent data pipeline designed for fast and efficient ETL (Extract, Transform, Load) and data onboarding. It helps transform raw data into governed data products ready for AI, analytics, and activation.
02How does Reactor's Electron AI simplify data engineering tasks?
Reactor's Electron AI acts as an intelligent co-pilot, allowing users to describe data mapping needs in natural language. It generates mapping expressions (in Excel-like functions and Python), understands data context, and refines outputs based on user feedback.
03What are the benefits of Reactor's 'mapping at ingest' approach?
Mapping data at ingest means defining and structuring raw data (field-level granularity, standardization) before it enters your data warehouse. This creates a governed semantic layer, ensuring context and definitions are shareable across all downstream systems and use cases.
04How does Reactor help reduce cloud data warehousing costs?
Reactor saves money on cloud data warehousing by enabling efficient data onboarding and transformation. Its immutable logging and replay capabilities reduce load on source systems and avoid expensive compute within your data warehouse by allowing data remapping without re-ingestion.
05How does Reactor compare to other ETL solutions like Fivetran or Matillion?
Reactor positions itself as an alternative to Fivetran and Matillion, onboarding, mapping, and modeling your data faster and at a fraction of the cost. It offers unique structural capabilities: immutable raw data logging for replay, semantic mapping at ingest, and versioned, governed data products downstream.
06How does Reactor ensure my data pipelines are 'future-proof'?
Reactor ensures future-proof data pipelines by permanently logging and storing raw data. This allows for bulk remapping and remodeling of data at any time as new use cases arise, without placing additional load on source systems or incurring expensive compute costs.
07What types of data initiatives can Reactor support?
Reactor is designed to support modern data initiatives by preparing data models for generative AI, advanced analytics, and data activation. It provides clean, well-defined data that is immediately usable for strategic insights.

Ready when you are

Simplify your data challenges.

Get started with Reactor today and accelerate your adoption of generative AI, analytics, and data activation.

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