Stream processing fundamentals - CS 591 K1: Data Stream Processing and Analytics Spring 2020Data historical recent and historical ETL process complex fast and light-weight ETL: Extract-Transform-Load e.g. unzipping compressed files, data cleaning and standardization 6 Vasiliki Kalavri |0 码力 | 45 页 | 1.22 MB | 1 年前3- Scalable Stream Processing - Spark Streaming and FlinkString] = ... val windowedStream = stream.window(Seconds(20))... val joinedStream = windowedStream.transform { rdd => rdd.join(dataset) } 29 / 79 Operations on DStreams ▶ Input operations ▶ Transformation0 码力 | 113 页 | 1.22 MB | 1 年前3
- PyFlink 1.15 Documentationpl. ˓→createScan(RelFactories.java:495) at org.apache.calcite.tools.RelBuilder.scan(RelBuilder.java:1099) at org.apache.calcite.tools.RelBuilder.scan(RelBuilder.java:1123) at org.apache.flink.table.planner0 码力 | 36 页 | 266.77 KB | 1 年前3
- PyFlink 1.16 Documentationpl. ˓→createScan(RelFactories.java:495) at org.apache.calcite.tools.RelBuilder.scan(RelBuilder.java:1099) at org.apache.calcite.tools.RelBuilder.scan(RelBuilder.java:1123) at org.apache.flink.table.planner0 码力 | 36 页 | 266.80 KB | 1 年前3
- Course introduction - CS 591 K1: Data Stream Processing and Analytics Spring 2020Filtering, counting, sampling Graph streaming algorithms Vasiliki Kalavri | Boston University 2020 Tools Apache Flink: flink.apache.org Apache Kafka: kafka.apache.org Apache Beam: beam.apache.org0 码力 | 34 页 | 2.53 MB | 1 年前3
共 5 条
- 1