Speed and Scale

Query terabytes of data in seconds and present it to your visualization tools like Tableau. It works with Spark SQL and can support hundreds of concurrent users.

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.

Query data in S3/HDFS

Query data on S3 or HDFS. Deploy on EMR or HDP clusters with Spark. Move data from legacy MPP or other datawarehouses into S3/HDFS and accelerate queries with Sparkline.

Migrate data from MYSQL/Oracle and other transaction systems

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


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.