High-availability, recovery semantics, and guarantees - CS 591 K1: Data Stream Processing and Analytics Spring 2020computation maintains state: • rolling aggregations • window contents • input offsets • machine learning models State in dataflow computations 3 Vasiliki Kalavri | Boston University 2020 Logic State computation maintains state: • rolling aggregations • window contents • input offsets • machine learning models State in dataflow computations 3 Vasiliki Kalavri | Boston University 2020 Logic State computation maintains state: • rolling aggregations • window contents • input offsets • machine learning models State in dataflow computations 3 Vasiliki Kalavri | Boston University 2020 4 Distributed0 码力 | 49 页 | 2.08 MB | 1 年前3- Introduction to Apache Flink and Apache Kafka - CS 591 K1: Data Stream Processing and Analytics Spring 2020Batch API Historic data Kafka, RabbitMQ, ... HDFS, JDBC, ... Event logs ETL, Graphs, Machine Learning Relational, … Low latency, windowing, aggregations, ... 2 Vasiliki Kalavri | Boston University0 码力 | 26 页 | 3.33 MB | 1 年前3
- State management - CS 591 K1: Data Stream Processing and Analytics Spring 2020computation maintains state: • rolling aggregations • window contents • input offsets • machine learning models State in dataflow computations 2 Vasiliki Kalavri | Boston University 2020 • No explicit0 码力 | 24 页 | 914.13 KB | 1 年前3
- PyFlink 1.15 Documentationworkloads, such as real-time data processing pipelines, large-scale exploratory data analysis, Machine Learning (ML) pipelines and ETL processes. If you’re already familiar with Python and libraries such as Pandas0 码力 | 36 页 | 266.77 KB | 1 年前3
- PyFlink 1.16 Documentationworkloads, such as real-time data processing pipelines, large-scale exploratory data analysis, Machine Learning (ML) pipelines and ETL processes. If you’re already familiar with Python and libraries such as Pandas0 码力 | 36 页 | 266.80 KB | 1 年前3
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