By Milomir.Vojvodic-Oracle on Jul 06, 2016
Oracle released the new version of Oracle Stream Explorer and renamed it to Oracle Stream Analytics (OSA). It became a part of Oracle Data Integration And Governance Platform.
All data originates in a flash, whether it is from Internet-of-Things (IoT) devices, web clicks, transactions, or mobile app usage. But traditional analytics is done much, much later. Why wait? Analyze immediatelly simulated or live data feeds to determine event patterns, correlation, aggregation & filtering.
Patterns library for industry specific solutions
• Transportation: Monitor all airline's operational events to eliminate flight delays.
• Vehicle telematics : Reduce fuel cost alerting on every sensor based element of the vehicle, Improved safety by out-of-hours usage, transgressing unscheduled locations
• Retail: Customer service centers are using Fast Data for click-stream analysis and customer experience management. Proximity based marketing to provide personalized offers
• Banking : Immediate Action – Payments processed more than 60 minutes without ACK or Bank Error
• Telco: Location based offers, or Intelligent Network Management to drive new services and lower costs.
• Healthcare: Monitoring Medical Device Data to help save lives. Smart beds : Body sensors determining the immediate status of the critical condition of the patient and current status of the bed components.
• Manufacturing: Real-time corrective action for reducing maintenance costs or risk outages
• IT prevent power grid re-starts
This new version is an impressive release with new features :
The existing patterns have been enhanced substantially now including Spatial, Statistical, General industry and Anomaly detection through streaming machine learning.
New Geo-spatial pattern - can be used to analyze streams containing geo-location data and determine how events relate to pre-defined geo-fences in maps.
The Expression Builder allows to add calculated/derived fields on the Live Output Stream of an exploration, an important step towards the “streaming Excel sheet” idea of Oracle Stream Analytics.It provides the ability to apply and insert mathematical and statistical calculations into the active live output stream. Once a new expression has been defined and validated, a column will be added next to the column of relevance. This new column can then be used in subsequent filters and explorations.
The Business Rules section of the Stream Analytics canvas provides the ability to apply the more traditional IF-THEN-ELSE constraints and clauses on the various properties of the event shape. This capability enables the user to combine both streaming query analytics using temporal criteria together with a collection of business rules that can randomly effect the information in existing or new columns.
Oracle Stream Analytics supports new Event Stream sources and targets, such as MQTT, Apache Kafka and Twitter. Especially Kafka gets more and more important in modern Big Data architectures. We can now use Oracle GoldenGate for immediately capturing changes on any database table (CDC = change data capture), send these captured change events into Kafka using GoldenGate for BigData and consume it from OSA to apply streaming analytics on it.
Oracle Stream Analytics - you can deploy and execute streaming applications to a Spark Streaming infrastructure.