Assaf Araki – Real Time Analytics at Scale
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Flink Forward 2015
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Assaf Araki – Real Time Analytics at Scale
- 1. REAL time Analytics AT SCALE SMART DATA PIPES For THE INTERNET OF THINGS Assaf Araki, Big Data Analytics Architect Big Data Analytics, Intel
- 2. Intro to Big Data Analytics @ Intel People (+100) Data Scientists Management Big Data Developers Analytics PMs 13% 41% 9% 37% CONTRIBUTION TO Data Center Group CONTRIBUTION TO INTEL Operations MISSIO N #1 Operational excellence #2 Help Intel win area of Intelligent machines VISION Analytics is a competitive advantage for Intel Industry / Academy Technical due-diligence assessment for Intel Capital Benchmark with startups Academy Collaborations Assist Intel Sales & Marketing DESIGN Cut validations time-to-market MANUFACTURI NGReduce test cost SALES & MARKETINGIncrease sales through analytics Stream Analytics Cloud Parkinson Research Machine Learning Strategy
- 3. The IOT challenge CloudIngestionThings Cloud Infrastructure Data Platform Analytics Platform UI Services
- 4. Use case : The Parkinson Disease research 44 CLINICAL TRIALS Create and Validate Algorithms & Measures POPULATION STUDY Generate insights Using Big data analytics
- 5. 10 Medication reporting Medication reminder Report PATIENT REPORTING OTHER Configurable data collections Contribution score Integrated Login and registration Pebble notifications OBJECTIVE MEASURES Gait Sleep Tremor Activity Level Controlled Tests
- 6. So, Why is it Big-Data Problem? 30 subjects 5 DaysperSubject 0.15TB Weekly 500 subjects 30 DaysperSubject 1GB PerSubjectperDay 15TB Monthly 1000 subjects 365 DaysperSubject 365TB Yearly 1GB PerSubjectperDay 1GB PerSubjectperDay
- 7. SERVICE BATCH ANAYTICS STREAM ANALYICS INGESTION STORAGE USER INTERFACE Mosquitt o 7 CLOUD COMPUTING SERVICES
- 8. Smart Ingestion characteristics Personalized Easy to use Smart Data Pipe • Per single device or user • Maintain state and required data for ML • Easily subscribe to any Stream • Use familiar development Languages (Java, Scala) • Developers focus on logic development • Apply analytics on the Stream • Trigger actions (close the feedback loop) in timely manner Scalability • Linear scalability (scale Out) • Extremely High concurrencies • High Throughput Fault Tolerance• No Single point of failure • Seamless recovery • Persistent
- 9. Smart Data Ingestion – High level overview 9 Device Device Device Device Scalable, Persistent Broker Processing, Stream Analytics
- 10. What is Akka? • Micro-service(Actor) oriented. • Message Driven • Lock-free • Location-transparent • High performance • Fault Tolerant • Scales linearly
- 11. Stream Processing - the Akka way… 11 Each actor is a small peace of Java or Scala code performing its role A set of actors creates a topology which is responsible for device’s data stream processing A single Akka node may have millions of concurrent actors handling different streams and operations Change detection Automatic change detection time rules matcher Detect & raise alert for matched rules Sleep quality calculating users’ sleep quality Tremor detection Tremor detection based on devices’ Aggregator Aggregation (50hz to minutes / hours) Sample Parkinson Disease re Subscriber Parser Aggregator HBase Writer Analytics Manager Change Detection UnZip Real Time Rules Sleep Quality
- 12. STREAM Processing MANAGEMENT Layer (“Pigeon”)
- 13. • Core OS & Docker containers enable portability and ease of deployment anywhere • Enables the flexibility of choosing a set of desired containers based on a given use case requirements Easy Portability With Docker & Core OS Preconfigured containers ready to be loaded
- 14. • IoT data Ingestion goes beyond moving the data into the cloud • We have deployed a scalable and fault tolerance, multi-protocol pipeline that enables stream Analytics • Stream Analytics platform is leveraged for Other IoT projects Summary
- 15. Thank You!
Editor's Notes
- The Internet of Things (IoT) is creating unprecedented business opportunities for both individuals and organizations.
- The story The name of the man in the picture on the left is Andy Grove and he is one of Intel’s founders and has Parkinson (PD) The story begins when he reads and article in the NY times about Big Data and decides to start a project within Intel related to PD and Big Data He contacts Michael J fox foundation and then decides to start a joint effort together The idea is to elaborate Internet of things, wearable's technology and big data platforms to assist PD research PD Neurodegenerative disease, movement disorder symptoms Existing treatment are mainly for quality of life improvements and not for curing ~6M patients, ~1M in the US and ~5M in the rest of the globe Life expectancy: ~10-15 years 1 out 100 over the age of 60 is a PD patient No Test and no Progression markers
- On this slide the focus should be on the patient reported capabilities and the configurable data collection strategies. For the patient reported explain the Medication reminder and reporting capabilities which helps us track patients compliance, learn abour medication effect on the motor symptoms and this while providing value to the patients The Objective measures part is covered later on in the PPT. In the Other section talk about the ability to configure which sensorial data to use for each cohort of users
- Quick review of PD solution layers as a use case of IoT platform Batch Layer based on Spark Storage layer using Hadoop, HBase & MySQL for Metadata Powerful, scalable ingestion layer based on Akka & Kafka A dynamic stream analytics layer based on Akka actor system framework Scalable Service layer providing set of APIs for registration & data extraction out of the platform UI layer – the only layer in this diagram which is unique to PD solution – using Pebble watch and Android application to collect data and interact with patients You can note that 5 out of the presented 6 layers (excluding the UI layer) are part of the IoT platform and can be used for similar products / verticals
- Multi-protocol pipeline built over AKKA & KAFKA KAKFA is a fast, scalable, durable & distributed messaging system - high-throughput, low-latency platform for handling real-time data feeds. AKKA is an Actor based framework allowing high concurrency, distributed and resilient based on events / messaging This layer is responsible for: Pulling messages Parse & Process Concurrent & controlled write
- Writing correct concurrent, fault-tolerant and scalable applications is hard. Akka uses the Actor Model to raise the abstraction level and provide a better platform to build correct concurrent and scalable applications. Can support millions of concurrent actors handling different streams which is a good fit to IoT characteristics. We use Akka for: Processing messages Near Real-time rules Change detection at the device level
- Docker is an open-source project that automates the deployment of applications inside software containers CoreOS is an open source lightweight operating system based on the Linux kernel and designed for providing infrastructure to clustered deployments
- Change Detection – Single (Kolmogorov-Smirnov) & Multi sensor ( Under patent ) Anomaly Detection Periodicity Stream classification