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

MaterializeMySQL Database engine in ClickHouse WinterZhang(张健) About me • Active ClickHouse Contributor • MaterializeMySQL Database Engine • Custom HTTP Handler • MySQL Database Engine • BloomFilter Skipping https://github.com/zhang2014 MySQL Table Engine • Mapping to MySQL table • Fetch table struct from MySQL • Fetch data from MySQL when execute query MySQL Database Engine • Mapping to MySQL database • Database Engine • Mapping to MySQL database • Consume MySQL BINLOG and store to MergeTree • Experimental feature (20.8, recommend latest stable version) MaterializeMySQL Database Engine MaterializeMySQL

SAP SAP Data Intelligence 3 on Rancher Kubernetes Engine 2 using VMware vSAN and vSphere SUSE Linux Enterprise Server 15 SP4 Rancher Kubernetes Engine 2 SAP Data Intelligence 3 Dr. Ulrich Schairer, (SUSE) 1 SAP Data Intelligence 3 on Rancher Kubernetes Engine 2 using VMware vSAN and vSphere SAP Data Intelligence 3 on Rancher Kubernetes Engine 2 using VMware vSAN and vSphere Date: 2023-07-24 SAP possi- ble errors or the consequences thereof. 2 SAP Data Intelligence 3 on Rancher Kubernetes Engine 2 using VMware vSAN and vSphere Contents 1 Introduction 4 2 Requirements 5 3 Preparations 7

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

??? Vasiliki Kalavri | Boston University 2020 2 • Costs of streaming operator execution • state, parallelism, selectivity • Dataflow optimizations • plan translation alternatives • Runtime optimizations the basics 3 source sink input port output port dataflow graph ??? Vasiliki Kalavri | Boston University 2020 Revisiting the basics 4 Dataflow graph • operators are nodes, data channels are edges ??? Vasiliki Kalavri | Boston University 2020 12 • What does efficient mean in the context of streaming? • queries run continuously • streams are unbounded • In traditional ad-hoc database queries

Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 4/28: Graph Streaming ??? Vasiliki Kalavri | Boston University 2020 Modeling the world as a graph 2 Social networks 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 for

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 2020 Vasiliki Kalavri | Boston 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 println!("seen: {:?}", x))
 .connect_loop(handle);
 }); t (t, l1) (t, (l1, l2)) Streaming Iteration Example Terminate after 100 iterations Create the feedback loop 13 Vasiliki Kalavri

relatively static and historical data • batched updates during downtimes, e.g. every night Streaming Data Warehouse • low-latency materialized view updates • pre-aggregated, pre-processed streams streams and historical data Data Management Approaches 4 storage analytics static data streaming data Vasiliki Kalavri | Boston University 2020 DBMS vs. DSMS DBMS DSMS Data persistent relations stream can be viewed as a massive, dynamic, one-dimensional vector A[1…N]. The size N of the streaming vector is defined as the product of the attribute domain size(s). Note that N might be unknown

以及找不到老師教的技術，想辦法變 成自己的專長。 目前負責 Java 與 .NET 雲端運算相 關技術的推廣，主要包括 Hadoop Platform 與 NoSQL 等 Big Data 相關 應用，Google App Engine、Microsoft Azure 與 CloudBees 等雲端平台的運 用，以及 Android、Windows Phone 等 Smart Phone 的應用程式開發。 PS. 除了我的照片之外，投影片裡頭 (Slave)！ 10 / 74 Hadoop 1.x 架構與限制 比較基本的模組： Hadoop HDFS (Storage) Hadoop MapReduce (Computing Engine + Resource Management + Job Scheduling / Monitoring + ...) 比較明顯的限制： 每個 Cluster 大概就是 4,000 - 4,500 x 架構 比較基本的模組： Hadoop Common (Core Libraries) Hadoop HDFS (Storage) Hadoop MapReduce (Computing Engine) Hadoop YARN (Resource Management + Job Scheduling / Monitoring) 比較沒人知道的事： Hadoop 2.x 也默默地做了四五年了