Connect data centers, public cloud, private cloud, and devices
Guavus SQLstream enables fully-connected, real-time-responsive cloud environments via real-time stream processing and analytics from, within, and among on-premise data sources and cloud environments.
The Guavus SQLstream download page provides installers that are regularly tested on Windows, Red Hat, Centos, Ubuntu, and SuSe. s-Server, s-Studio, and Client Tools portions of SQLstream Blaze install on almost any version of Linux. StreamLab installs separately, also on almost any version of Linux. Client software–s-Studio or Client Tools—can be installed on Windows or Linux. Customers who download and install Blaze get a perpetual license to use the software to process 1GB of data per day, as well as a free 60-day Enterprise license which allows them to process an unlimited amount of data. After 60 days, customers can purchase an annual license based on numbers of servers and cores that is not tied to the 1GB limit.
SQLstream offers downloadable Virtual Machines appliances for VMWare (on Windows, Mac, or Linux), Virtual Box (on Windows, Mac, or Linux), the Windows Hypervisor, or Parallels on Mac. Guavus SQLstream is also available as Docker container from Docker Hub with the same licensing terms as standard native installs.
Guavus offers Guavus SQLstream as an EC2 AMI in all Amazon regions on the same licensing terms as customers who download and install. It also offers Blaze on the AWS Marketplace in all regions as a pay-as-you-go basis. And, Blaze is available on the AWS Intelligence Community Marketplace on a pay-as-you-go basis. Blaze is also available as a Virtual Machine on Azure under standard download-and-install licensing terms.
Cloud/On-Premise Mixed Environments- Guavus SQLstream is tightly integrated with Amazon Kinesis, and SQLstream has a continuing partnership with Amazon. Customers requiring mixed cloud/on-premise deployments have many options. They can use Guavus SQLstream on-premise and in the Amazon cloud either as an EC2 AMI or via the Marketplace. Or, via Kinesis they can blend Guavus SQLstream with Amazon Kinesis Analytics and other AWS services. Both Guavus SQLstream and Kinesis Analytics use the exact same underlying SQL engine, and SQL queries are transportable between the two environments.
Embedded Systems and Applications- The full Guavus SQLstream server can run in 3G of RAM and 2 Intel Atom cores. SQLstream also offers the Guavus SQLstream agent which can collect, parse, filter, and aggregate data on small footprint devices before sending it on to a Guavus SQLstream server for downstream processing as well as the Guavus SQLstream JDBC driver which allows any Java application to connector s-Server for distributed stream processing.
Browser Based Application User Interfaces- StreamLab and s-Dashboard are built for, tested on, and supported on Google Chrome, Mozilla Firefox, Microsoft Edge, and Microsoft Internet Explorer. Both leverage the Guavus SQLstream WebAgent for passing SQL query results across multiple clients, multiplexing multiple streaming queries over a single web socket.
Industry Standard Administration User Interfaces- s-Studio is an Eclipse User Interface and runs as installable software on all Linux and Windows platforms. SQLLine is a Linux or Windows command line user interface and is installable on all Linux and Windows platforms.
Extend your data to cloud and other environments through real-time data wrangling (streaming ingestion, cleansing, and load).
more on wrangling
Capture and act on your perishable insights in real-time with cloud-based, real-time, continuous stream processing.
stream analytics
Reuse infrastructure, intelligence and skill sets to select and store relevant data, processes, or analytic results.
the power of SQL
READY TO GIVE IT A TRY?
Guavus SQLstream 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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