Create a live in-memory datamart from MYSQL/Oracle/SQL Server and other databases

Your traditional relational datawarehouse/database can be a bottleneck for reporting and analytics. Move data to SNAP for faster response times.
Leverage SNAP for beyond SQL. Run machine learning workloads and SQL on one single data access layer.

Query data in S3/HDFS

Query data on S3 or Hadoop in seconds using your favorite visualization tool.

Speed and Scale with the SQL you know

Use SQL with joins without worrying about query time. SNAP allows you to keep your SQL joins but eliminates joins under the hood.

Ad-Hoc Slice and Dice

Query slices of very large datasets and drill down on dimensions in the datasets with interactive response times. Perform aggregations on the fly and keep data at its lowest grain eliminating extracts and summary tables.

Interactive Analytics on terabyte scale data

Sparkline SNAP enables interactive fast analysis on Spark, bringing think time BI to Big Data

Using Python with SparklineData

Fast iterative data exploration using notebooks and Python on large datasets

Data engineers and scientists need to explore data to find patterns. Data exploration involves interactive queries on full grain datasets. SNAP returns data in seconds and seamlessly works with Notebooks eliminating all friction and slowness when working with detail data.

Using Tableau with SparklineData

Eliminate expensive extracts and connect Tableau with Live data for real time analysis

Running ad-hoc queries at interactive response times is a challenge. Running it on live datasets, real time with hundreds of users accessing various segments of the data can be impossible with existing solutions. SNAP can now turbo charge your Tableau visualizations by connecting to live data with think time response times.