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
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.
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
version is an impressive release with new features :
patterns have been enhanced substantially now including Spatial, Statistical, General industry and Anomaly
detection through streaming machine learning.
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.
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.
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.
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.