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 Chapter 4 - Model 23 About Model 23 Create Model 25 Chapter 5 - Physical Model 27 About Physical Model 27 Add Databases / Schemas to Physical Model 27 Add Tables to Physical Model 28 Add to Physical Model 30 Work with View Builder (Available only in Non-Essentials Version) 31 Edit View's Definition 37 Add Foreign Keys to Physical Model 40 Chapter 6 - Logical Model 41 About

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 Chapter 4 - Model 23 About Model 23 Create Model 25 Chapter 5 - Physical Model 27 About Physical Models 27 Add Databases / Schemas to Physical Model 27 Add Tables to Physical Model 28 Add to Physical Model 30 Work with View Builder (Available only in Non-Essentials Version) 31 Edit View's Definition 37 Add Foreign Keys to Physical Model 40 Chapter 6 - Logical Model 41 About

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

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

76 Maintain Objects 77 Redis 78 Databases 78 Data 78 Chapter 6 - Data Viewer 79 About Data Viewer 79 RDBMS 79 RDBMS Data Viewer 79 Use Navigation Bar 79 Edit Records 80 Replace Records 86 Filter Records 88 Manipulate Raw Data 89 Format Data View 89 View Field Comments 91 MongoDB 91 MongoDB Data Viewer 91 Use Navigation Bar 92 Grid View 93 Tree Tree View 98 JSON View 100 Sort / Find / Replace Documents 100 Redis 103 Redis Data Viewer 103 Use Navigation Bar 103 Edit Keys 104 Sort Keys 105 Search Keys 105 Assistant Editors

76 Maintain Objects 77 Redis 78 Databases 78 Data 78 Chapter 6 - Data Viewer 79 About Data Viewer 79 RDBMS 79 RDBMS Data Viewer 79 Use Navigation Bar 79 Edit Records 80 Replace Records 86 Filter Records 88 Manipulate Raw Data 89 Format Data View 89 View Field Comments 90 MongoDB 90 MongoDB Data Viewer 90 Use Navigation Bar 91 Grid View 92 Tree Tree View 97 JSON View 99 Sort / Find / Replace Documents 99 Redis 102 Redis Data Viewer 102 Use Navigation Bar 102 Edit Keys 103 Sort Keys 104 Search Keys 104 Assistant Editors

Uber Trips › Where to download: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page › How to import data into ClickHouse: https://clickhouse.yandex/docs/en/getting_started/example_datasets/nyc_taxi/ -and-uber-trips-with-a-vengeance/ 3 / 62 External Tools Tools you got used to Small sample of data is enough to start All you need is to get it from ClickHouse Couple of lines for Python + Pandas TSVWithNames' resp = requests.get(url, data=query) string_io = io.StringIO(resp.text) table = pd.read_csv(string_io, sep="\t") 5 / 62 Table (part) 6 / 62 How to sample data You already know it! › LIMIT N

Uber Trips › Where to download: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page › How to import data into ClickHouse: https://clickhouse.yandex/docs/en/getting_started/example_datasets/nyc_taxi/ -and-uber-trips-with-a-vengeance/ 3 / 62 External Tools Tools you got used to Small sample of data is enough to start All you need is to get it from ClickHouse Couple of lines for Python + Pandas TSVWithNames' resp = requests.get(url, data=query) string_io = io.StringIO(resp.text) table = pd.read_csv(string_io, sep="\t") 5 / 62 Table (part) 6 / 62 How to sample data You already know it! › LIMIT N