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

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synchronous_commit = on, it was possible that after writing to the database and reading from a replica that the data might not yet have be applied.. • Synchronous replication (on) may reduce write transaction throughput potentially more so • If it is acceptable to have stale reads from replicas, and it is ok to lose some data and you need more write throughput, consider synchronous_commit off. Can set per transaction. Postgres replicates everything – pglogical’s logical replication permits selective replication for subsets of data for example Postgres Conference China 2016 中国用户大会 Bidirectional Replication (BDR) • 2nd Quadrant’s

Guide 24.2.ea. Page 7 of 1010. Data Editor overview Data View and Format Data Filters Data viewing and editing Data refresh Navigation Managing Panels Managing Data formats Managing Charts Working Information System (GIS) data GIS examples Working with XML and JSON Virtual column expressions SQL Editor overview Data Editor tools Viewing and editing data Data operations Advanced data types SQL Editor Pending Transactions Auto and manual commit modes Query Manager Data transfer Data import and replace Search overview Data Search Database full text Search Database metadata Search File Search

User Guide 24.1. Page 7 of 1008. Data Editor overview Data View and Format Data Filters Data viewing and editing Data refresh Navigation Managing Panels Managing Data formats Managing Charts Working Information System (GIS) data GIS examples Working with XML and JSON Virtual column expressions SQL Editor overview Data Editor tools Viewing and editing data Data operations Advanced data types SQL Editor Pending Transactions Auto and manual commit modes Query Manager Data transfer Data import and replace Search overview Data Search Database full text Search Database metadata Search File Search

Project security Editors overview Data Editor overview Data View and Format Data Filters Data viewing and editing Data refresh Navigation Managing Panels Managing Data formats Managing Charts Projects Editors in DBeaver Data Editor tools Viewing and editing data Data operations DBeaver Ultimate User Guide 24.1. Page 8 of 1169. Working with Spatial Geographic Information System (GIS) data GIS examples examples Working with XML and JSON Virtual column expressions Mock Data generation SQL Editor overview SQL Execution Query execution plan Script management SQL Assist and Auto Complete SQL templates

Project security Editors overview Data Editor overview Data View and Format Data Filters Data viewing and editing Data refresh Navigation Managing Panels Managing Data formats Managing Charts Projects in DBeaver Data Editor tools Viewing and editing data Data operations DBeaver Ultimate User Guide 24.2.ea. Page 8 of 1171. Working with Spatial Geographic Information System (GIS) data GIS examples examples Working with XML and JSON Virtual column expressions Mock Data generation SQL Editor overview SQL Execution Query execution plan Script management SQL Assist and Auto Complete SQL templates