We could consider three types of Real Time when we manage data and depends on each stage:
1. Real Time Processing: Is the possibility of ingest data at the time the event is produced in real live. This includes only processing step, i.e copying data from source to destiny and guarantees data to be ready for analytics
You can try some online demos here
Technologies:
2. Stream Analytics: it performs analytics of data on the fly, as a stream is usually analyzed in a window time frame, the analytics we can do here is limited because only attack a very limited data set
Technologies:
3. Real Time Analytics: refers to two basic conditions: the most recent data will be included in any report, graphic, etc, that analytics will take near to 0 time in execute
Technologies:
In Memory Mapreduce
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Apache Spark
(Spark SQL)
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Apache Flink
(FQL)
Column Storage Engines
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Kafka + (Spark | Flink) +
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Druid
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InfluxDB
(Time series analytics)
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Kylin
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Marketing (Product recommendations based on latest updates)
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Fraud Detection (Tracking suspect activities on events that appear to be fraudulent)
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Health Care Monitoring (Social network trending topics can help to this)
