Visually manage your streaming data- from Source to Sink.
TRY COMMUNITY EDITIONGuavus SQLstream acquires data from multiple, disparate sources with automated discovery features so you can easily to connect to data streams and start interacting with them with no coding necessary.
Streaming sources include:
A data scientist can easily spend 50 to 80% of his time cleaning and preparing his data before he even starts looking for insights in it. StreamLab makes it easy to transform data into a format that makes it easier to work with.
Guavus SQLstream enables you to enrich event streams with data from external tables and other event streams so that all information is available and formatted for analysis.
Guavus SQLstream puts powerful analytics at your fingertips with features like Aggregate, Bollinger Bands, Calculate, Categorize, Group Rank, Table Lookup, Time Sort and New Column.
Detect trends to identify:
Guavus SQLstream provides an enterprise platform for hosting machine learning models for predictive analysis. By continuously filtering and transforming data, SQLstream ensures that data is ready for scoring. SQLstream’s advanced features allow users to programmatically define triggers and actions based on real-time machine learning scores.
Guavus SQLstream enables you to automate responses when certain criteria is met in real-time.
Data Visualization
Guavus SQLstream can use native s-Dashboard visualization to push real-time updates to the browser or use our native web agent to push to external visualization tools.
Choose from dozens of dashboard templates:
Once you have analyzed, enriched, or otherwise modified data, you will often want to write this data out of s-Server into other locations, or “sinks”.
Export data out to:
READY TO GIVE IT A TRY?
SQLstream Blaze is free to download and use up to 1GB/day (after which it throttles).
Guavus SQLstream’s mission is to make real-time stream analytics easy to use and own with a one-stop-shop solution that performs the best, has the widest footprint, never turns off, and can be developed and customized by data scientists and engineers alike.
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