SQLstream Blaze 4.0 is the next generation of our flagship Big Data stream processing platform. SQLstream Blaze 4.0 includes s-Server 4.0, the fastest and most scalable release of our renowned SQL-compliant stream processor, new tools for real-time visualization of high volume data streams, StreamApps for rapid implementation of industry solutions, and integration with Apache Storm, the open source distributed computation framework.
SQLstream s-Server 4.0 takes streaming Big Data performance to new levels. Our previous release of s-Server had already set the industry standard for streaming data throughput performance with an independent benchmark of 1.3 million records per second on a commodity 4-core server. s-Server 4.0 has been benchmarked at 570,000 records per second per core making it the most scalable and capable platform for streaming Big Data analytics.
Our customers recognize that s-Server 4.0 is the only product capable of delivering the million records per second requirements for today’s real-time security intelligence, M2M, telecommunications and Internet of Things applications.
SQLstream s-Visualizer enables enterprise power users to build real-time displays using a powerful graphical drag and drop interface. With sophisticated meta-driven configuration, users can build powerful, streaming data driven analytics and visualizations coupled with a positive, sophisticated user experience.
Existing BI dash boarding tools are poll-based, meaning they must read stored, static data, and reread the data in order to refresh the display as new data arrive. This impacts performance, cost and latency, making these tools inadequate for real-time applications. SQLstream s-Visualizer offers push-based visualization – dashboards are updated in real-time as new data and analytics arrive – with intelligent aggregation and throttling for high velocity data streams.
SQLstream s-Dashboard is an HTML5 platform for building real-time streaming dashboards on any device. Delivered as an integral component of our core s-Server stream processing platform, s-Server Dashboard enables developers to build sophisticated push-based visualizations easily and quickly.
SQLstream’s StreamApps enable organizations to realize immediate value from their streaming log file, sensor, device and network data. This new concept in Big Data offers rapid time to revenue with application flexibility, and the lowest Total Cost of Performance of any Big Data technology. Each StreamApp contains a set of standard components, typically including log, file equipment and device adapters for real-time data collection, a package of relevant streaming analytics, and the associated streaming dashboards.
Streaming data analytics and real-time Operational Intelligence applications are critically dependent on their ability to collect and integrate machine data at scale. For example, many BI systems can connect and analyze simple log file data on a small scale. But processing 10,000s of different log files coupled with the simultaneous calculation of real-time analytics is a problem on an entirely different scale.
SQLstream Blaze 4.0 adds a new unified sub-system for simplifying connectivity to machine data sources such as log files, sensors and devices, and to improve the ease of data adapter and agent extensibility. The enhancements include a range of performance optimizations for machine data collection that enable SQLstream to scale to the millions of records per second level required of many of today’s operational intelligence applications.
SQL has emerged as the de facto language for Big Data processing. For example, Cloudera’s Impala and Google’s BigQuery. SQL is also the ideal language for processing data streams using real-time, time window-based queries. With SQLstream Blaze 4.0, we’ve continued our commitment to standards in the Big Data industry. SQLstream 4.0 adds new SQL support, aligned with SQL:2008 and SQL:2011 standards, including additional SQL operators, simplified and extended support for SQL/MED integration (that enables SQL queries to execute directly over machine data sources including log files), and performance optimization for user defined functions (UDXs). UDXs enable sophisticated Java applications and functions to be built into a streaming SQL pipeline.
For our customers, standards mean lower cost, maintainable and future-proof systems, with significantly lower lifecycle cost of ownership.
Storm is a distributed stream processing framework available under the Apache Open Source license. The primary focus of the Storm project is the distributed infrastructure, rather than the real-time analytics or connectors for machine data sources and enterprise storage platforms. That’s where SQLstream comes in. SQLstream’s Stream Processor for Storm enables Storm implementations to utilize continuous SQL queries for streaming analytics. The Stream Processor for Storm enables Storm Bolts (stream processing nodes) to be implemented as continuous SQL queries, and for SQLstream’s s-Server stream processor to be deployed as Storm Spouts (machine data source), utilizing SQLstream’s machine data agent and adapters for real-time machine data collection and integration.
Organizations with an existing Storm deployment, or considering a future Storm implementation, will benefit from greater performance throughput on significantly less hardware, faster time to value for real-time applications, and dynamic updates to operational Storm systems.
