Data Router

The most flexible way to collect &
transform your data for analysis

Introducing Qrvey Data Router

Data Router is a powerful tool for quickly collecting, transforming and preparing your data for analysis. It supports structured, semi and unstructured data sources with both realtime push loading and batch processing via S3 buckets of high volumes of data. Data Router is built with a serverless, microservices-based architecture that makes it infinitely scalable. Load as much data as you need, but only pay for the computing power you use.

Data Router Can…

Scale

Efficiently scale to support any data size, type or velocity requirements

Transform

Efficiently transform and cleanse all types of data from any data source

Support

Full support for “upsert” loading of new and updated data at the same time

Powerful APIs

Data Router’s powerful APIs can ingest data from any source, type or structures, including other APIs. It can connect to third-party applications, IoT devices, files, images, audio, video and more. And because Data Router supports push data loading, it’s perfect for loading real-time data into Qrvey. 

Transformations Made Easy

Data Router includes a built-in transformation layer that allows you to manipulate, cleanse or transform data as it is being loaded. Out-of-the-box transformations include:

  • String and text manipulation
  • Numeric and math functions
  • Date processing and manipulation
  • Data conversions
  • Conditional, case, and filter-based logic
  • Lookups
  • Data Flattening

AI/ML Comes Standard

Data Router includes built-in AI/ML functions for text analytics and natural language processing, including keyphrase detection and sentiment analysis. Images and video include objection, text detection and more.

Maximum Flexibility

Data Router supports the creation of custom fields for self-service analysis, which is exceptionally powerful for embedded analytics use cases. Since Qrvey’s data model isn’t a traditional database, users can send new data fields at any time and have them made instantly available for analysis. Data fields don’t need to be predefined in the datasets, they can simply be pushed in as part of the API payload and Data Router does the rest.

This makes Qrvey particularly useful for any application where each customer may be storing data differently with different custom field values. If you’re getting data from 3rd party or public sources, such as finance data, real estate, demographics, weather or others, each service may have slightly different fields, and all of them can be made available for analysis purposes with Data Router.

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and see the Qrvey difference

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