Why Reactor

Not just another data pipeline.

More than ETL. Reactor preserves your raw history, resolves shared meaning across sources, and ships trusted data products to your warehouse, at a fraction of the cost of legacy pipelines.

EtLT, not ETL

Define meaning before warehouse sprawl begins.

Reactor uses the EtLT pattern: small-t transformations at ingest (cleaning, validation, mapping), big-T modeling in your warehouse. Cheaper compute, faster queries.

Data replay

Future-proof every pipeline.

Every record is immutably logged. When new use cases arise, you replay and remap historical data without re-ingesting from source.

Built for AI

Ready for analytics, AI, and activation.

Reactor delivers governed, labeled, and modeled data ready for vector stores, training pipelines, and analytics workloads downstream teams can trust.

Common question

Is Reactor an ETL solution?

Fast answer

Yes, and more.

Reactor onboards data to modern cloud data warehouses, providing useful data sets and data models for AI, activation, and analytics.

Long answer

Built to evolve with your business.

Reactor is built so your data model can evolve without re-pulling the past, re-stitching identities by hand, or rebuilding downstream tables from scratch.

It's the intelligent data platform for advanced data onboarding and modeling, with comprehensive pre-built analytical models for key retail functions like acquisition and retention marketing, merchandising and operations, plus natively integrated analytics and activation.

Like ETL solutions, Reactor ingests, maps, and models data for hosting in modern data warehouses like Snowflake and Google Cloud BigQuery, making data available for generative AI, analysis, and activation in your favorite tools and applications. Technically speaking, Reactor follows an “EtLT” pattern for data onboarding, applying data labels and definitions early, and analytical modeling late in the data life cycle.

Similarities to ETL

Like standalone ETL solutions, Reactor offers pre-built data collectors to ingest data from diverse SaaS and on-prem systems. Reactor onboards your mission-critical data to modern cloud warehouses (Snowflake, BigQuery, Databricks). It also provides useful, retail-ready data models that offer immediate business insights, customizable as your business evolves.

Differences from ETL

Unlike other ETL solutions, Reactor addresses the entire data value chain. Flexible ways to collect, model, analyze, and activate mission-critical data using off-the-shelf cloud infrastructure that you own and control, paired with modern data stack tooling like dbt, Sigma, and Census.

Following an EtLT pattern, Reactor provides dedicated semantic labeling at ingest for better shared understanding and easier corporate data governance. Data typing, labeling, and model logic apply early in the upstream pipeline, so metrics and KPIs in analytical reports and dashboards match up with customer segmentation and activation use cases. For data engineering teams, Reactor provides mapping and modeling logic transparency through its interfaces and offers dbt source-code libraries for advanced analytical logic best suited to run in the warehouse.

Compare it yourself

See how Reactor stacks up.

Side-by-side comparisons with the data tools you're evaluating.

Move data is only step one.

Fivetran moves raw data. Reactor preserves history, resolves shared meaning across sources, and ships reusable downstream assets.

Compare full feature set

Skip the credit-based complexity.

Matillion helps orchestrate transformations. Reactor preserves raw history and applies shared meaning earlier, with less downstream rebuild work.

Compare full feature set

A new era

It's time to move on from standalone ETL.

Generative AI platforms like Snowflake Cortex and GCP Gemini on Vertex AI are letting teams do more with data than ever before. To take advantage, modernize the data work upstream and downstream of your warehouse.

Immutable raw data logging

Log raw data ahead of the warehouse to reduce analytical load on operational systems, and make reinterpreting data easier in the future.

Shared meaning at ingest

Shared customer, order, and product definitions applied at data ingest simplify downstream understanding and governance.

Low-code interfaces

Low-code interfaces calling shared libraries of mappings and models improve access to data and accelerate time to value.

Real-time streaming

Streaming data through your pipelines unlocks real-time use cases ranging from site personalization to triggered customer communications.

Model logic before downstream sprawl

Dedicated semantic labeling, data typing, and modeling logic applied early, upstream of the warehouse. Consistent metrics across reports, dashboards, and activation.

In concert, all of these EtLT features make data more available and useful across the enterprise, driving competitive advantage and profitable growth.

FAQ

Common questions.

Answers about Reactor, the EtLT pattern, and what makes us different from traditional ETL.

Talk to our team

01Is Reactor just another ETL data onboarding solution?

No. Reactor is more than just an ETL solution. It's the intelligent data platform for advanced data onboarding and modeling, designed to ship governed data products ready for AI, activation, and analytics. While it ingests, maps, and models data like ETL, it follows an 'EtLT' pattern, applying data labels and definitions early in the data lifecycle and analytical modeling late.

02What is the 'EtLT' pattern that Reactor uses, and why is it beneficial?

The 'EtLT' pattern stands for Extract, transform (with little t), Load, and Transform again. Reactor applies dedicated semantic labeling, data typing, and data model logic early in the upstream data ingest process. This ensures better shared understanding, easier corporate data governance, and consistency of metrics and KPIs across reports, dashboards, and activation use cases. It also lets advanced analytical logic run efficiently within the data warehouse.

03How does Reactor differ from traditional ETL solutions like Fivetran or Matillion?

Unlike standalone ETL solutions that primarily focus on ingestion and basic transformation, Reactor addresses the entire data value chain. It offers advanced data typing, semantic labeling, semantic mapping capabilities, and pre-built analytical models to support natively integrated AI, analytics, and activation tools. Reactor aims to simplify complex data challenges across the entire enterprise, moving beyond just data onboarding.

04What cloud data warehouses does Reactor support?

Reactor onboards mission-critical data to modern cloud data warehouses that you own and control, including Snowflake, Google Cloud BigQuery, Databricks, AWS, and Apache Iceberg.

05How does Reactor help with Generative AI initiatives?

Reactor provides clean, well-defined data modeled specifically for generative AI, analytics, and activation. By simplifying the data work and providing optimized data sets, Reactor accelerates the adoption of generative AI platforms like Snowflake Cortex and GCP Gemini on Vertex AI, enabling organizations to do more with their data.

06Does Reactor require extensive coding or data engineering teams?

No. Reactor offers low-code/no-code interfaces, making data work accessible to both engineers and non-engineers, accelerating insights and actions across the organization. For teams with data engineering capacity, Reactor provides mapping and modeling logic transparency and offers DBT source-code libraries for advanced analytical logic.

07What kind of pre-built analytical models does Reactor offer?

Reactor provides comprehensive pre-built analytical data models for key retail functions such as acquisition and retention marketing, merchandising, and operations. These models offer immediate business insights and are customizable to adapt to your unique business needs.

Ready when you are

Let's talk data.

Tell us about your stack, your goals, and the tool you're evaluating. We'll show you why teams switch.