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

1 Table of Contents Chapter 1 - Introduction 4 About Navicat Data Modeler 4 Installation 5 Registration 6 Migration / Upgrade 7 End-User License Agreement 7 Chapter 2 - User Interface Trace Logs 84 Log Files 84 4 Chapter 1 - Introduction About Navicat Data Modeler Navicat Data Modeler is a powerful and easy-to-use GUI tool for creating and manipulating database models print models to files, etc. Navicat Data Modeler is available on three platforms - Microsoft Windows, macOS and Linux. Here are some highlights of Navicat Data Modeler: • Create and manipulate c

1 Table of Contents Chapter 1 - Introduction 4 About Navicat Data Modeler 4 Installation 5 Registration 5 Migration / Upgrade 6 End-User License Agreement 7 Chapter 2 - User Interface Trace Logs 82 Log Files 82 4 Chapter 1 - Introduction About Navicat Data Modeler Navicat Data Modeler is a powerful and easy-to-use GUI tool for creating and manipulating database models print models to files, etc. Navicat Data Modeler is available on three platforms - Microsoft Windows, macOS and Linux. Here are some highlights of Navicat Data Modeler: • Create and manipulate c

Unified Data Layers: A New Era for Scalable Analytics, Search, and AI v 1.1Table of Contents Introduction 1. The Interconnection of Analytics, Search, and AI 2. What is a Real-Time Unified Data Layer Layer? 3. Why Do You Need a Real-Time Unified Data Layer? 4. 5.CrateDB: A Modern Real-Time Unified Data Layer1. Introduction Data teams are facing more challenges than ever. As applications generate and and consume unprecedented volumes of data across a growing number of sources and formats, data engineering and architecture teams must design systems that not only scale but also deliver real-time access

阿里云 AnalyticDB for PostgreSQL - 打造更简单易用的Cloud SQL Data Warehouse 个人介绍 缪长风 ⚫ 2010年初加入支付宝，负责Oracle RAC和Greenplum数据仓库 ⚫ 有幸参与了Oracle RAC到 Greenplum再到Hadoop以及最终到 ODPS的架构演进工作。 ⚫ 2012年起，转至阿里巴巴大数据团队，负责Hbase/OTS业务支 RDS 关系型数据 库D ECS自建 自建大数据计算平台 Hadoop Spark EMR Dataphi n OSS海量云存储 Data Lake Store DTS 实时同步 Dump Data Dump Data 数据集成 按需回流 数据仓库应用 在线数据仓库 数据 查询 数据集成 批量同步 1 3 4 2 云化在线数仓 ： 简化、敏捷、一栈式构筑数据仓库

Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.3 3. Create Data Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 ShardingSphere‐Proxy 27 Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Data Node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Sharding . . . . . . . . . . . . . . . . . . . 57 Inventory Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Incremental Data . . . . . . . . . . . . . . . . . . . . . . . . . .

Rules Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.3 3. Create Data Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 ShardingSphere‐Proxy 24 Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Data Node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Sharding . . . . . . . . . . . . . . . . . . . 60 Inventory Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Incremental Data . . . . . . . . . . . . . . . . . . . . . . . . . .

27 Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Data Node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Sharding . . . . . . . . . . . . . . . . . . . 59 Inventory Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Incremental Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Mode Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Data Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Rules . .

. . . . . . . . . . . . . . . . . . . . . . 19 Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 19 Mass data real‐time analysis in OLAP scenarios . . . . . . . . . 20 Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Data Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Sharding . 42 Request Limit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.7 Data Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 iii

78 Maintain Objects 79 Redis 80 Databases 80 Data 80 Chapter 6 - Data Viewer 81 About Data Viewer 81 RDBMS 81 RDBMS Data Viewer 81 Use Navigation Bar 81 Edit Records 82 Replace Records 88 Filter Records 90 Manipulate Raw Data 90 Format Data View 91 View Field Comments 92 MongoDB 93 MongoDB Data Viewer 93 Use Navigation Bar 93 Grid View 94 Tree Tree View 100 JSON View 102 Sort / Find / Replace Documents 102 Redis 105 Redis Data Viewer 105 Use Navigation Bar 105 Edit Keys 106 Sort Keys 106 Search Keys 107 Assistant Editors