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Platform introduction & Summary
The document provides information on the Big Data Europe Integrator Platform release, including its goals of being open source, easy to use, supporting various use cases, and embracing emerging big data technologies. It describes the platform architecture, supported frameworks, features like the development environment and administrator interface, and how it provides platform installation, usage instructions, and integrating custom components. Example pilot use cases are also listed covering domains like health, food, energy, transport, climate, social sciences, and security.
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Platform introduction & Summary
- 1. Big Data EuropePlatform ReleaseMay 3rd 2017 Big Data Europe Integrator Platform Empowering Communities with Data Technologies Platform release Dr. Hajira Jabeen Senior researcher University of Bonn
- 2. Platform Goals ◎Opensource ◎Ease ofUse ◎Support a variety of use cases ◎Embrace emerging Big Data Technologies ◎Simple integration with custom components
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- 9. Platform Architecture AlternateView Support Layer Init Daemon GUIs Monitor App Layer Traffic Forecast Satellite Image Analysis Platform Layer Spark Flink Semantic Layer Ontario SANSA Semagrow Kafka Real-time Stream Monitoring ... ... Resource Management Layer (Swarm) Hardware Layer Premises Cloud (AWS, GCE, MS Azure, …) Hadoop NOSQL Store CassandraElasticsearch ...RDF Store Data Layer
- 10. BDE Supported Frameworks Search/indexingData processing Apache Solr Apache Spark Data acquisition Apache Flink Apache Flume Semantic Components Message passing Strabon Apache Kafka Sextant Data storage GeoTriples Hue Silk Apache Cassandra SEMAGROW ScyllaDB LIMES Apache Hive 4Store Postgis OpenLink Virtuoso 10
- 11. Platform features ◎ BDEDevelopment Environment o Stack builder o Workflow builder o Instructions to add custom components to the BDE stack ◎ Administrator Interface o SwarmUI ◎ UI Integrator o Workflow monitor o Integrated web interface 11
- 12. What BDE Provides? ◎Platform Installation Instructions ◎Usage Instructions o Creating a stack o Creating a workflow o Monitoring the Stack o Integration of Custom Components 12
- 13. Platform installation ◎Manual installationguide ◎Using Docker Machine o On local machine (VirtualBox) o In cloud (AWS, DigitalOcean, Azure) o Bare metal ◎Screencasts 13
- 14. Deploying a BigData Stack ◎ Stack Builder ◎ Stack o Collection of communicating components to solve a specific problem ◎ Described in Docker Compose o Component configuration o Application topology 14
- 15. Creation of WorkFlows ◎PipelineBuilder o Allows creation of dependencies among different applications ◎ WorkFlow Monitor o Monitoring of pipeline-workflow using 15
- 16. Integrating Custom Components ◎Instructions oOrchestrator required for initialization process (init_daemon) ❖ Components may depend on each other ❖ Components may require manual intervention o User Interface Integration ❖ Standard Interfaces from components ❖ Combine and align the interfaces 16
- 17. User Interfaces ◎Target: Facilitatethe use of the platform o User Interface Adaption ◎Available interfaces o Workflow UIs ❖ Workflow Builder ❖ Workflow Monitor o Swarm UI o Integrator UI 17
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- 19. Presentations by Ivan,Aad and Jens 19
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- 22. Pilot Show Cases 22 SC1SC2 SC3 SC4 SC5 SC6 SC7 SC1 - Open PHACTS discovery platform relating to biological/medical questions SC2 - Discovery and Linking of Viticulture-relevant information SC3 - System monitoring in energy production units SC4 - Short-Term traffic flow forecasting. SC5 - Supporting data-intensive climate research SC6 - Citizens & Researchers Budget on Municipal Level SC7 - Ingestion of remote sensing images and social sensing data to detect and verify changes on the Earth surface for security applications
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- 26. 26 SC4 - Transport FCD:Floating Car Data NRT: Near Real Time
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- 28. SC6 - SocialSciences 28
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- 30. BDE vs Hadoopdistributions Hortonworks Cloudera MapR Bigtop BDE File System HDFS HDFS NFS HDFS HDFS Installation Native Native Native Native lightweight virtualization Flexible Modular Architecture no no no no yes High Availability Single failure recovery (yarn) Single failure recovery (yarn) Self healing, mult. failure rec. Single failure recovery (yarn) Failure recovery Cost Commercial Commercial Commercial Free Free Scaling Freemium Freemium Freemium Free Free Addition of custom components Not easy No No No Yes Integration testing yes yes yes yes -- Operating systems Linux Linux Linux Linux Windows/Mac/Linux Management tool Ambari Cloudera manager MapR Control system - Docker swarm UI+ Custom 30
- 31. BDE vs Hadoopdistributions ◎BDE is not built on top of existing distributions ◎Targets o Communities o Research Institutions ◎Bridges scientists and open data ◎Multi Tier research efforts towards Smart Data 31
- 32. Maintenance and Uptake ◎CommunityDriven ◎Adapters ■ Feuga , Eurostat, ILVO ■ I2cat, Vicomtech, IoF ◎Follow Up Projects ■ HOBBIT ■ Special ■ Big Ocean ■ Qrowd 32
- 33. Wrap up 33 ◎Big DataEurope - Platform o Containerized Components o Development and Runtime Facilities ◎Show Cases o From Components to Architectures o Evolving Microservice Architectures