Platform Streaming analytics made simple

SQLstream Blaze is a streaming analytics platform, offering powerful visual development tools and services to enable software developers and data analysts to efficiently move from concept to streaming apps in minutes.

SQLstream Blaze overview

Blaze is designed and patented as a distributed platform based on open standards and APIs for flexible development and deployment of scalable stream processing, reusable analytic operations, and applications.  
Blaze provides extreme performance and throughput without requiring tuning or low-level coding.

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Blaze: concurrent, continuous, real-time processing

Live data (from sensors, logs, transactions, message queues, Web and Social, databases (CDC), Apps) is continuously integrated with historical data (from Apps, In-memory analytics, databases, Cloud, NoSQL, data warehouses, etc.).

Acquire Act Analyze

Millions of Records Per Second

1 — Acquire

Unstructured, structured, local, distributed, in-motion, at-rest, live or historical — Blaze instantly welcomes all data, from all available sources, in all formats, and at all speeds.

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2 — Analyze

Create contextual business insights through the perfect mixture of live and historical data streams.

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3 — Act

Data is instantly operationalized through action (streaming visualization, automated triggers and alerts), or by being stored for future analysis.

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Real-time apps—smarter, faster, at a lower cost

Productivity
  • Develop and maintain apps in minutes, not months
  • Focus on business logic not low-level coding
  • Use existing data sources, applications, and skill-sets
Performance
  • 100x greater performance than alternatives
  • Millions of records per second per CPU core
  • 1-5 millisecond latency
Cost
  • up to 50x reduction in hardware requirements
  • Fastest time to market and lowest cost of ownership
  • Use existing database and data warehouse investments

SQLstream Blaze Components

SQLstream Blaze includes the core stream processor, development and visualization environments for business analysts and developers, platform management tools, and a comprehensive suite of agents adaptors for machine data and enterprise integration.

StreamLab

StreamLab is a visual platform for interactively exploring machine data streams and building real-time streaming apps without having to write any code.  An intelligent suggestions engine supports users in building complete apps from ingestion to analysis and into action, by simply accepting suggestions made by StreamLab.  All of the execution takes place on s-Server, at massive scale, and deployment is immediate. With StreamLab, business analysts or Java, SQL, Python, or Scala developers can efficiently move from concept to real working application in minutes.

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s-Server: Real Time Complex Event Processing

s-Server is the core streaming analytics platform, most recently licensed by Amazon to power their Kinesis Analytics service. s-Server is the only stream processor to use ANSI standards-compliant SQL for querying live data streams. Processing data at over 1M events/second per CPU core on benchmarks and at over 1M events/second per server on real-world applications, s-Server is the most powerful and scalable data processing platform available. s-Server includes a suite of agents and adapters for machine data mediation and enterprise system/storage platform integration.

s-Dashboard

s-Dashboard is an HTML5 platform for building push-based, real-time visualizations on s-Server. s-Dashboard enables developers to deploy real-time dashboards for streaming analytics on any device or channel, and is pre-configured with a standard set of panels, widgets, and dashboards.

s-Studio

s-Studio is a stream inspection, application development, and administration console for the s-Server platform. s-Studio enables dynamic updates to live applications, adding new queries as needed or changing existing queries or views. s-Studio is built as a plug-in to the standards-compliant Eclipse framework.

Industry-leading features

SQLstream’s approach to streaming minimizes the time developers spend writing low-level code.  SQLstream Blaze automatically enforces data integrity while optimizing an application’s latency and performance, simplifies application maintenance, and streamlines the integration of existing code.  At the heart of Blaze is a rich and flexible set of operators to efficiently process and analyze incoming data streams.

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  • An Ecosystem With Broad Integration And Extensibility

    Work with any data source, format or destination, concurrently, continuously, and in real-time.

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  • Full Support for Java Functions

    SQL UDXes (User Defined Transform) enable Java development of streaming operations, and inclusion of third party libraries and even entire systems in a streaming SQL query.

  • Auto-Optimized Time Window Operators

    Blaze implements declarative windowing meaning that windows describe the scope of the tuples taking part in windowed operations, rather than CEP-approaches that normally view windows as data structures.

  • Deterministic Processing for Data Accuracy and Application Integrity

    Blaze is a declarative platform based on industry standard SQL data processing semantics – the language of business data – to assure existing business rules are supported with ease and to eliminate the risks in procedural language platforms.

  • Enrichment of Streaming Data with Any Source

    Blaze can perform a SQL JOIN between a stream and native table, external table (for example, Oracle), or re-streamed data from a native table without compromising performance.

  • Full-Spectrum of Support for Proper Event Sequencing

    Blaze has native, continuous incremental, and deterministic support for missed events and lack of events while preserving order of events and enabling action on delayed events, useful for any application where record-by-record correctness for monitoring is critical such as network monitoring, security, and financial.

  • Seamless Telemetry

    SQLstream Blaze continuously computes telemetry data that quantifies behavior and performance. Enabling businesses to leverage existing technology investments, this telemetry is available as a table or a stream for easy integration with any third-party monitoring tool.

  • Low Latency + High Performance at Scale

    Blaze provides extreme performance and throughput without requiring tuning or low-level coding.

  • Hardware Optimization – 3x-500x less

    Blaze can process over 1 Million 16-byte records/sec per CPU core while scaling linearly to over 16 cores on commodity hardware.

  • Probabilistic Model-Driven Recovery for High Availability

    By redundantly computing the results of all stream operations on multiple commodity nodes, Blaze achieves arbitrary “x9”s of availability in real-world apps without compromising performance or resorting to exotic and costly hardware.