Meet Electron: Your AI Teammate for Data Pipelines
June 24, 2025
The intelligent data platform
Your company has more
data than ever.
And less clarity than ever before. Reactor preserves your raw history, resolves shared meaning across every source, and ships trusted data products to your warehouse.
Different source fields. One shared customer identity.
shopify
customer_email
stripe
receipt_email
klaviyo
email
Common entity
common.customer.email
500+ integrationsReplay without re-ingestLess engineering overhead
app.reactordata.com
LIVE
Trusted by modern brands
The problem
Data tools move data.
They don't make it useful.
Modern data stacks promised speed. They delivered complexity. Here's what your team is up against:
Time wasted
6+months
To build usable pipelines.
Engineers spend quarters on plumbing, not on the value layer where analytics live.
Months 1–12
Effort drain
80%
Spent on plumbing.
Data teams burn cycles wiring, validating, and re-shaping. Only 20% touches modeling.
80%
Plumbing
Real work
Tool sprawl
3–5tools
For a single workflow.
Extractors, schedulers, transformers, semantic layers, BI. All duct-taped together.
EL
T
BI
SL
QA
Fragmented
Reactor was built to end this. Here's how.
The solution
Your data should work
as hard as your team.
Reactor turns raw data into reusable data products in your warehouse, with lineage, replay, and shared definitions built in. No months of pipeline building. No army of engineers.
Delivers to
Snowflake
BigQuery
Databricks
AWSapp.reactordata.com/mappings
How it works
Three steps to trusted data.
From raw signals to trusted data products, without months of engineering.
Step 0101
Preserve & replay
Never re-ingest again.
Every record lands immutably from day one. When the business changes, replay your history without re-pulling what you already collected.
Replay activity
Remap shopify_v2 → common.order1.2M
Replay 90 days of history240M
Add ML feature, reprocess847M
Step 0202
Resolve & unify
Define meaning once.
Define your shared entities once. Reactor applies your definitions across every source at ingest, so the same customer or order looks identical everywhere.
Identity resolved
shopify
customer_email
stripe
receipt_email
klaviyo
email
Common entity
common.customer.email
Step 0303
Govern & ship
Ship governed data products.
Shape your outputs with Excel-like expressions. Land versioned, governed data products in your warehouse, ready for analytics, AI, and activation.
Data products
customer_360
4 sources · governed
revenue_daily
2 sources · governed
ml.feature_store
6 sources · governed
app.reactordata.com/pipelines
The platform
From raw data to
business value.
1
Preserve the raw history
Collect once, then replay as requirements change. Every raw record is logged immutably so you can reinterpret history without re-pulling from source.
2
Resolve business meaning across sources
Define shared customer, order, and product entities once. Reactor applies your definitions across messy source systems at ingest, before downstream sprawl begins.
3
Ship trusted downstream assets
Deliver reusable data products for analytics, AI, and activation. Lineage tracked, definitions shared, history replayable.
Why Reactor
Not just another pipe.
Fivetran and Matillion move data. Reactor makes it usable. When the business changes, replay and remap the history you already collected instead of re-pulling it from source.
Recommended
What you get with Reactor, vs. just moving rows around.
- Move raw data to warehouse
- Built-in semantic mapping
- Immutable raw data logging
- Data replay without re-ingest
- Governed data products
- Low-code, analyst-friendly
- Hours to value, not months
The impact
Results,
not just infrastructure.
Reactor customers ship faster, spend less, and never re-ingest data. Here's what changes when you flip the switch:
Time-to-value
10x
Faster time-to-value.
Hours to your first data product, not months of plumbing and refactoring.
Old way6+ months
With ReactorHours
Cost reduction
60%
Lower warehouse costs.
Eliminate redundant transformations. One pipeline, one source of truth, fewer credits burned.
-60%
Reactor
Legacy stack
Data replay
100%
Historical data, replayed.
Every record immutably logged. Remap and reprocess for new use cases. No re-ingestion.
HistoricalReplayed
Capabilities
Everything you need to build trusted data products.
Visual schema builder
Build pipelines in hours, not months.
Point Reactor at any source: APIs, databases, flat files. Define schemas visually. Ship with less custom pipeline code.
Semantic mapping
Shared meaning across every source.
Shared definitions across every source. Every team works from the same data.
Source
user_id
Mapped
customer_id
New
Electron AI
Your AI teammate.
Electron writes mappings, suggests joins, and tags your data automatically.
Electron
Mapped 47 fields automatically.
Data replay
Your data needs will change. Reactor is ready.
Every record is immutably logged. When new use cases arise, you simply remap and reprocess. No re-ingestion. No lost history.
Learn about replay
Replay
Immutable logging
Audit-ready by default.
Full lineage and audit trail. Every record logged, every change tracked.
Warehouse-native
Lands where you live.
Tables land directly in Snowflake, BigQuery, or Databricks, shaped for your use case.
Low-code interface
Analysts ship without custom pipeline code.
Drag-and-drop with Excel-like expressions. No Python or SQL required.
14:02:33Schema mappedshopify.orders
14:02:31Records ingestedstripe.charges+1,247
14:02:28Field addedsalesforce.leads
14:02:24Pipeline deployedcommon.customer
Enterprise-grade
Run with confidence at any scale.
Immutable logging, full audit trails, SOC 2-aligned controls. The trust your security team needs, with the speed your data team wants.
Security & complianceIntegrations
Scale without switching tools.
Deep integrations with the systems your business already runs on. 500+ connectors and counting.
Klaviyo
500+
Connectors
REST
+ SDKs
Hours
to integrate
Customers
Loved by data teams
everywhere.
From iconic brands to fast-growing teams. Reactor powers the data behind real businesses.
100+
Integrations
10B+
Rows modeled monthly
Hours
to first table
Featured story
“With Reactor and Snowflake, we're building future-proof data infrastructure for machine learning, advanced analytics and data activation.”
France Roy
CTO, Balsam Brands
“Reactor provides near real-time centralized customer insights. Reactor has been key to PacSun unlocking value from our data.”
Shirley Gao
CIO, PacSun
“Reactor enabled our internal data teams to focus on integrating our proprietary data to provide a more complete picture of our audience's interests.”
Robert Gash
CTO, Hearst
“Reactor helps Eberjey unify and model data across our entire business footprint, while we own our data and open data infrastructure.”
Dani May
VP Ecommerce, Eberjey
“We can more effectively track consumer demand, optimize inventory, and identify emerging product trends to better serve our customers.”
Sally Wilson
CMO, Mountain House
FAQ
Got
questions?
Everything you need to know about Reactor. Can't find what you're looking for?
Talk to our team01How does Reactor fit into my existing data stack?
Reactor works alongside your data warehouse: Snowflake, BigQuery, Databricks, or AWS. It sits between your source systems and your warehouse, delivering governed data products that are immediately useful for AI, analytics, and activation.
02How is Reactor different from Fivetran or Matillion?
Most tools just move data. Reactor preserves your raw history, resolves meaning across every source, and ships governed data products with versioning and lineage, not just raw tables that need hours of downstream transformation.
03How long does it take to get started?
Hours, not months. Connect a source, define your schema, and start landing clean data in your warehouse. Most teams see value in their first week.
04Do I need a data engineering team?
No. Reactor's low-code interface means analysts and ops teams can build and manage data flows with a drag-and-drop interface and Excel-like expressions. No Python or SQL expertise required.
05What happens when my data needs change?
Reactor immutably logs all raw data. When new use cases arise, you can remap and reprocess historical data without re-ingesting from source. We call this data replay.
Ready when you are
Put your data to work.
See value in your first week. No engineering team required.