Build a next generation datamart on Spark for B.I and A.I combined

Slice and dice analysis on Native Spark

Use Spark SQL to power your B.I and analytics with 100 times the speed of comparable in-memory technologies.

Advanced optimizations

SNAP includes advanced optimizations that allows you to use joins in your star schema and still achieve seconds response times for large datasets. SNAP Qubes define a logical data model of your domain which can then be reused across multiple applications and front ends. Qubes can be modeled to take advantage of advanced optimizations for queries involving timestamps, metric binning, high cardinality dimensions and more.

Machine learning and A.I analysis on SNAP

Connect Python, R, Scala to SNAP and optimize your data science workloads. Use all your data instead of a subset of your data.
Combine SQL with Python/Scala or R at blazing speed.

No Extracts

Eliminate building summary tables, extracts and specialized aggregations for each use case. Store your data in Sparkline at the lowest granularity and aggregate on the fly.

Compressed data and indexes

Sparkline uses compressed in-memory data and indexes to accelerate your queries. For business intelligence type queries, just in-memory data is not enough for interactive analysis.

Deploy on cloud or on premise

SNAP can be deployed on AWS with EC2. It can run on YARN or in Standalone mode accessing data from HDFS, S3 or elsewhere.
SNAP can be deployed inhouse on any commodity hardware or as part of an existing Hadoop cluster.

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