Make Queries on Thousands of Dimensions Fly. Instantly.
Deliver extremely fast data filtering, joining and aggregating on every dimension of any data source.


The power of Inline Indexing™

Fast data analytics at scale is limited, resource intensive, and often requires very long time-to-insight. Inline Indexing is our innovative approach for delivering flexibility - no need to compromise on dimensionality or scale - while maintaining interactive and fast response time. Deployed in your virtual private cloud, Varada adaptively indexes any data, from any source, across all columns so that data is ready for any query.

Inline Indexing™ reflects data as a mesh of nanoblocks™, each independently indexed and inter-connected. Any SQL query on Varada, will fly across nanoblocks to filter, join and aggregate using indexes across variety of domains.

Varada Dynamic Segmentation
Dynamic segmentation Slice your users across any dimension and at any granularity. With inline indexing, everything is indexed and ready.
Varada Behavioral Analytics
Behavioral analytics Dynamically define cohorts and funnels and track them across various data sources using inline indexing for joins.
Varada Root Cause Analysis
Root cause analysis Travel your data interactively without data preparation. Inline indexing covers from 360-view analysis to needle in a haystack query.


Hot data tier

Expose operational subsets of big data with an automatic fast indexed layer. Cut down on manual ETL efforts while improving performance and lowering compute cost.

Self-service data platform

Enable any data consumer to define operational datasets. Give data consumers the power to interactively query large, complex or slow data sources without depending on expert tuning and optimization.

Fast BI on S3

Implement BI dashboards with consistently interactive response times on billions of events stored in a data lake as AWS S3. Enable iterative analysis of complex data without breaking the analysis flow.

Presto for applications

Extend your Presto-based workflow to support low latency analytics with materialized Indexed Views.
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