up an environment to develop Flink programs • Implement streaming data processing pipelines • Flink managed state • Event time Streaming in Apache Flink • Streams are natural • Events of any type like sensors, click streams, logs • Batch processing as a subset of stream processing Processing Data Dataflows Let's Talk About Time • Processing Time • Event Time • Events may arrive out of order totalFare Float total fare collected Lab 1 -- Ride Cleansing Transforming Data Transforming Data public static class EnrichedRide extends TaxiRide { public int startCell; public int

??? Vasiliki Kalavri | Boston University 2020 CS 591 K1: Data Stream Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 4/14: Stream processing optimizations ??? Vasiliki Vasiliki Kalavri | Boston University 2020 2 • Costs of streaming operator execution • state, parallelism, selectivity • Dataflow optimizations • plan translation alternatives • Runtime optimizations Revisiting the basics 4 Dataflow graph • operators are nodes, data channels are edges • channels have FIFO semantics • streams of data elements flow continuously along edges Operators • receive

Kalavri | Boston University 2020 CS 591 K1: Data Stream Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 4/28: Graph Streaming ??? Vasiliki Kalavri | Boston University a vertex and all of its neighbors. Although this model can enable a theoretical analysis of streaming algorithms, it cannot adequately model real-world unbounded streams, as the neighbors cannot be continuously generated as a stream of edges? • How can we perform iterative computation in a streaming dataflow engine? How can we propagate watermarks? • Do we need to run the computation from scratch

University 2020 Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu CS 591 K1: Data Stream Processing and Analytics Spring 2020 2/04: Streaming languages and operator semantics Vasiliki Kalavri | Boston University University 2020 Vasiliki Kalavri | Boston University 2020 Languages for continuous data processing 2 Vasiliki Kalavri | Boston University 2020 • Transforming languages define transformations specifying interval of 5–15 s) by an item of type C with Z < 5. 8 Vasiliki Kalavri | Boston University 2020 Streaming Operators 9 Vasiliki Kalavri | Boston University 2020 Operator types (I) • Single-Item Operators

Scalable Stream Processing - Spark Streaming and Flink Amir H. Payberah payberah@kth.se 05/10/2018 The Course Web Page https://id2221kth.github.io 1 / 79 Where Are We? 2 / 79 Stream Processing Systems Spark streaming ▶ Flink 4 / 79 Spark Streaming 5 / 79 Contribution ▶ Design issues • Continuous vs. micro-batch processing • Record-at-a-Time vs. declarative APIs 6 / 79 Spark Streaming ▶ Run Run a streaming computation as a series of very small, deterministic batch jobs. • Chops up the live stream into batches of X seconds. • Treats each batch as RDDs and processes them using RDD operations

Red Hat OpenShift Data Foundation 4.12 更新 OpenShift Data Foundation 针对集群和存储管理员的有关升级的说明 Last Updated: 2023-09-19 Red Hat OpenShift Data Foundation 4.12 更新 OpenShift Data Foundation 针对集群和存储管理员的有关升级的说明 other trademarks are the property of their respective owners. 摘要 摘要 本文档解释了如何更新以前的 Red Hat OpenShift Data Foundation 版本。 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 对红帽文档提供反 帽文档提供反馈 馈 第 第 1 章 章 OPENSHIFT DATA FOUNDATION 更新 更新过 过程概述 程概述 第 第 2 章 章 OPENSHIFT DATA FOUNDATION 升 升级频 级频道和 道和发 发行版本 行版本 第 第 3 章 章 将 将 RED HAT OPENSHIFT DATA FOUNDATION 4.11 更新至 更新至 4.12 第 第 4

pandas: powerful Python data analysis toolkit Release 0.19.0 Wes McKinney & PyData Development Team Oct 02, 2016 CONTENTS 1 What’s New 3 1.1 v0.19.0 (October 2, 2016) . . . . . . . . . . . . . . . . . . . . 345 4.3 Visualizing Data in Qt applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 5 Package overview 347 5.1 Data structures at a glance . . . . . . . . . . . . . 347 5.1.1 Why more than 1 data structure? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 5.2 Mutability and copying of data . . . . . . . . . . . . . . . . . . . .

pandas: powerful Python data analysis toolkit Release 0.19.1 Wes McKinney & PyData Development Team Nov 03, 2016 CONTENTS 1 What’s New 3 1.1 v0.19.1 (November 3, 2016) . . . . . . . . . . . . . . . . . . . 347 4.3 Visualizing Data in Qt applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 5 Package overview 349 5.1 Data structures at a glance . . . . . . . . . . . . . 349 5.1.1 Why more than 1 data structure? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 5.2 Mutability and copying of data . . . . . . . . . . . . . . . . . . . .

pandas: powerful Python data analysis toolkit Release 0.20.3 Wes McKinney & PyData Development Team Jul 07, 2017 CONTENTS 1 What’s New 3 1.1 v0.20.3 (July 7, 2017) . . . . . . . . . . . . . . . DataFrame/Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3.1.2 dtype keyword for data IO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3.1.3 .to_datetime() has gained 394 4 Package overview 395 4.1 Data structures at a glance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 4.1.1 Why more than 1 data structure? . . . . . . . . . .

pandas: powerful Python data analysis toolkit Release 1.4.2 Wes McKinney and the Pandas Development Team Apr 02, 2022 CONTENTS 1 Getting started 3 1.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 2.1.2 Viewing data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 2.1.3 Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 2.1.4 Missing data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 2.1.5 Operations