REAL-TIME STREAMING ANALYTICS DATA IN, ACTION OUT

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1 REAL-TIME STREAMING ANALYTICS DATA IN, ACTION OUT SPOT THE ODD ONE BEFORE IT IS OUT flexaware.net

2 Streaming analytics: from data to action Do you need actionable insights from various data streams fast? Do you want to extract business value from data in motion, similar to traditional analytics tools that analyze data at rest? Streaming analytics enable near real-time decision making by letting you inspect, correlate and analyze data as it streams into the platform from a myriad of different sources. of data whose value decays fast over time. It is key to analyze and take action as soon as the data arrives, to extract the largest possible business value from the incoming data. Streaming analytics can be performed in software at the center of the network, but is much more performant when executed with optimized hardware at the right place in the network., the many-core processor platform for real-time streaming analytics Meet, a real-time streaming analytics embedded platform, built with hundreds of parallel processor cores and accelerators that can analyze your data extremely fast. extracts critical information from your streaming data and turns it into immediate action. is simple and straightforward, regardless of the underlying many-core hardware complexity. The embedded platform combines hardware for your application domain with a software development environment. A streaming analytics platform processes data before Instead of storing all incoming data for later analysis, The embedded platform was designed from it lands in a database, and allows you to act on the data much faster than traditional data analytics technologies do. This is important for the many types streaming analytics lets you analyze the incoming data immediately and select only those relevant pieces of data that can then be stored for later reference. the ground up to receive large amounts of streaming data in parallel, process in real-time, and turn the data into actions, corrections, or alerts. Programming Action Correction Alert

3 hardware: data in, action out FlexaMiner FM8 server analytics hardware offers superior data processing capabilities for real-time streaming analytics. It transfers vast amounts of real-time input data directly to the parallel processing cores and hardware accelerators. is a many-core embedded hardware design with a flexible, scalable and modular structure. Specific combinations of modules inside the "black box" determine the performance and cater to the application. To offer the best match for your data analytics applications, hardware is available in distinct configurations. FlexaGate FG4 IoT analytics gateway Specifications: Analytics gateway for fast IoT data processing at the edge of the cloud Allows instantaneous reaction to timesensitive data Saves cloud bandwidth, storage and processing costs 8 x Gbps RJ45 copper Ethernet inputs for streaming data ingestion from multiple sensors x Gbps RJ45 copper Ethernet output for communicating analysis results to the cloud Parallel data streams feed right into the processor network + RISC processor cores and optimized hardware accelerators for IoT data processing Low-latency local memories in a distributed memory architecture Passive cooling Size 2,4 (24,4) x 8,2 x 22,5 cm (width (mounting plate) x height x depth) Specifications: 9" U appliance Parallel data streams feed right into the processor network 6 x Gbps RJ45 copper Ethernet inputs An internal high-speed Gbps interconnect enables fast communication between cores 2+ RISC processor cores and hardware accelerators Low-latency local memories in a distributed memory architecture Hardware accelerators speed up compute-intensive data processing tasks 2x Gbps RJ45 copper Ethernet output communicates analysis results Active cooling 9" rack shelf form factor 44,2 (48,4) x 4,4 x 43,4 cm (width (mounting plate) x height x depth) Optional redundant power supplies

4 SDE: from idea to application in 3 simple steps You need processing power. Real-time performance. And you want simple programming. s software development environment (SDE) is an intuitive graphical tool with predefined and custom building blocks. You can turn ideas into a final product in 3 easy steps. Most distinctly, s SDE gives insight in how your application runs on the many-core hardware. 2 Get insight in application behavior Invisibly, under the hood, the runtime (a many-core OS ) automatically maps tasks and channels on the hardware. The runtime takes on scheduling, memory allocation and task mapping of your application, and coordinates the information exchange between the processor cores and the external I/O. The SDE takes in the information from the runtime and shows how the application would behave in real-life conditions. The SDE provides you with insights and suggestions that allow you to review and optimize your application design. Result: insight in your application. Task Task 2 Input NoiseFilter float <64> Estimator float <64> block_t <4> Block Matching Input Task 3 Task 4 Task 5 block_t <6> Task 6 int<28> Task 7 Searching 2 vector_t<8> Encoding Output 6 28 Output Task 8 Task 9 vector_t<2> Task Task Task 2 Design your application in the SDE Assisted by the SDE, convert your application into sets of parallel tasks, and data flows. Being explicit about concurrency and parallelism in your application design allows the SDE to automatically optimize the application for the many-core hardware. 3 Finalize your application and your product Run it on the simulator or directly on the hardware. Capture performance figures on a live running system for further optimization. Explore alternative hardware configurations as your application target. In the Flexaware SDE, capture your applications graphically or describe them in C using the proprietary Tweak properties of tasks or communication channels to fine-tune. API (Application Programming Interface). Validate the expected data-to-action timing. Add predefined building blocks from s platform library or create your own reusable building Finalize. blocks to integrate unique functionality. Result: the initial version of your application. Result: the final, optimized version of your application.

5 Suggested applications Any application that requires the analysis (filtering, transformation, aggregation, correlation, etc.) of large amounts of data for immediate action. Examples are: IoT sensor analysis, Automotive ADAS, Machine vision inspection, Network monitoring, Security & safety monitoring, Access control, Operation and maintenance monitoring. Recore Systems PO Box AB Enschede, NL flexaware.net