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

poloclub.github.io/#cse6242
 CSE6242/CX4242: Data & Visual Analytics
 Simple Data Storage; SQLite Duen Horng (Polo) Chau
 Associate Professor, College of Computing
 Associate Director, MS Analytics
 Faloutsos How to store the data?
 What’s the easiest way? Easiest Way to Store Data As comma-separated files (CSV) But may not be easy to parse. Why? 3 Easiest Way to Store Data 4 https://en.wikipedia org/famous.html
 iPhone (iOS), Android, Chrome (browsers), Mac, etc. Self-contained: one file contains data + schema Serverless: database right on your computer Zero-configuration: no need to set up! See

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

Data is all you need for fusion N 1int x = 4; callee(x); // do work } #include #include #include "benchmark.h" #include "matrix_lib.h" int main(..){ std::vector a; a.reserve(100); reserve(100); // Initialize other data // data-structures. cblas_sgemm(a.data()...); cblas_sgemm(....); return 0; } High Performance code is about Hardware Matrix Multiply 2int x = 4; callee(x); // "matrix_lib.h" int main(..){ std::vector a; a.reserve(100); // Initialize other data // data-structures. cblas_sgemm(a.data()...); cblas_sgemm(....); return 0; } High Performance code is about Hardware

Back to Basics: Algebraic Data Types I also do C++ training! arthur.j.odwyer@gmail.com Arthur O’Dwyer 2020-09-16Outline ● Why the name “algebraic data types”? [3–18] ○ Memory layout diagrams. Why Questions? 2What do I mean by algebraic types? pair C++98. The original algebraic data type. tuple C++11. optional C++17. variant C++17, with minor tweaks to its constructors The memory layout of pair or tuple is going to be pretty much the same as the layout of a plain old data struct. pair tuple 6 A The compiler will do some padding for alignment, and may

Introduction Persistent Hash Map Persistent Transactional Data Structures Live Demonstration ReferencesIntroduction Persistent Hash Map Persistent Transactional Data Structures Live Demonstration References Overview Transactional Data Structures Design Goals Methodology Performance Results Live Demonstration A Persistent Hash Map for Graph Processing Workloads and a Methodology for Persistent Transactional Data Structures Structures 2IntroductionIntroduction Persistent Hash Map Persistent Transactional Data Structures Live Demonstration References Introduction Persistent Memory ▶ Persistent Memory is positioned as a new tier