Low Code Intelligent Data Pipeline
Why Reactor?
An extract, transform and load "ETL" pipeline for generative AI
Is Reactor an ETL data onboarding 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.
Reactor is an intelligent data pipeline for advanced data onboarding and modeling; with comprehensive pre-built analytical data models for key retail functions like acquisition and retention marketing, merchandising and operations; and with natively integrated analytics and data 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 too. 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.
Long Answer
Like standalone ETL solutions, Reactor offers pre-built data collectors to ingest data from diverse SaaS and on-prem systems and applications. Reactor onboards to modern cloud data warehouses (like Snowflake and BigQuery) your mission critical data. Reactor also provides useful, retail-ready data models that offer immediate business insights – and which are customizable to address the unique aspects of your business and business model as they evolve over time.
Unlike other ETL solutions, Reactor addresses the entire data value chain. Reactor offers flexible ways to collect, model, analyze and activate mission-critical data using off-the-shelf cloud infrastructure like Snowflake and Google Cloud BigQuery data warehouses (which you own and control), and modern data stack tooling like DBT, Sigma, and Census. You can use Reactor interfaces to engage the semantic models, and use your favorite tools and applications too.
Following an “EtLT” pattern for data onboarding, Reactor provides dedicated semantic labeling at ingest for better shared understanding of data and easier corporate data governance. Reactor’s data typing, data labeling and data model logic are applied early upstream in the data pipeline, so that metrics and KPIs in analytical reports and dashboards match up with customer and audience segmentation and other data outputs and activation use cases. For organizations with data engineering teams, Reactor provides mapping and modeling logic transparency through Reactor user interfaces and by offering DBT source-code libraries for the advanced analytical logic best suited to run in the data warehouse.
Reactor views ETL as just one small step of a complete, turnkey data infrastructure built upon the open standards of the Modern Data Stack.
Future-Proof your Cloud Data Infrastructure
Discover the nine important characteristics of a functional cloud data architecture.
Reactor vs. Data Onboarding Solutions.
Similarities and Differences.
It’s Time to Move on from Standalone ETL Solutions.
Generative AI platforms like Snowflake Cortex and GCP Gemini on Vertex AI provide the opportunity to do more with data than ever before. Modern tooling makes data work accessible to engineers and non-engineers alike, accelerating insights and actions across the organization. But cost and complexity are real concerns in the cloud data era, and the right approach and architecture can minimize your cost, time and risk to make data a true competitive advantage.
When implementing your cloud data warehouse, look for ways to simplify the data work by tackling key considerations both upstream and downstream of the data store.
Immutably log your data ahead of the data warehouse to reduce analytical load on operational systems, and make reinterpretation of data easier in the future.
A robust semantic layer, defined and applied at data ingest simplifies downstream understanding and governance.
No code/low code interfaces calling shared libraries of mappings and models improve access to data and accelerate time to value.
Streaming data through your data pipelines unlocks real-time use cases ranging from site personalization to triggered customer communications.
In concert, all of these EtLT features make data more available and useful across the entire enterprise – driving competitive advantage and profitable growth.
Standalone ETL is dead. Long live the intelligent data pipeline!
Get Started with Reactor
Simplify the complex data challenges across your entire enterprise
Get started with Reactor today and accelerate your adoption of generative AI, analytics and data activation.