About Navicat Data Modeler 4 Installation 5 Registration 6 Migration / Upgrade 7 End-User License Agreement 7 Chapter 2 - User Interface 14 Main Window 14 Chapter 3 - Collaboration 77 Editor 77 Auto Recovery 78 File Locations 78 Connectivity 79 Environment 79 3 Advanced 80 Chapter 14 - Hints and Tips 81 Model Hints and Tips 81 Chapter 15 - Hot Keys 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

About Navicat Data Modeler 4 Installation 5 Registration 5 Migration / Upgrade 6 End-User License Agreement 7 Chapter 2 - User Interface 14 Main Window 14 Chapter 3 - Collaboration 3 Model Hints and Tips 79 Chapter 15 - Hot Keys 81 Model Hot Keys 81 Chapter 16 - Trace Logs 82 Log Files 82 4 Chapter 1 - Introduction About Navicat Data Modeler Modeler Navicat Data Modeler is a powerful and easy-to-use GUI tool for creating and manipulating database models. It enables users to design database structures, reverse engineer, forward engineer, generate

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

MySQL/MongoDB Company: Xiaoxin Tech. Industry: Education Team: Big Data Leader: wangchao@xiaoheiban.cn About 100 billion data this year till now 30 million users We use Clickhouse in our daily Solutions 2. MySQL Engine Not suitable for big tables Not suitable for MongoDB Possible Solutions 3. Reinit whole table every day…… Possible Solutions 4. CollapsingMergeTree ● FINAL is slow ● GROUP one config file needed for a new Clickhouse table ● Init and keep syncing data in one app for a table ● Sync multiple data source to Clickhouse in minutes PTS Provider Transform Sinker ● Major Provider

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 Greenplum发展史 @Alibaba 2. AnalyticDB for PostgreSQL产品介绍 3. AnalyticDB for PostgreSQL 典型场景 4. AnalyticDB for PostgreSQL 未来演进 Greenplum发展史@Alibaba rac1 rac2 rac3 rac n Oracle RAC 11G ODPS ETL建模 交互式分析 AnalyticDB 为什么要提供Greenplum云服务 弹性 托管 高可用 安全 监控 1. Greenplum发展史 @Alibaba 2. AnalyticDB for PostgreSQL产品介绍 3. AnalyticDB for PostgreSQL 典型场景 4. AnalyticDB for PostgreSQL 未来演进 AnalyticDB for PostgreSQL 在线MPP数据仓库服务

ClickHouse — хорошая система Я расскажу 01 Почему ClickHouse — плохая система 02 И что с этим делать 03 3 Impala Spark SQL Presto/Trino Drill Actian Vortex Kylin Kudu MonetDB Actian Vector Actian Matrix Redshift Greenplum OmniSci (mapD) Brytlyt HyPER Pinot kdb+ Shakti MemSQL (SingleStore) Vertica SAP HANA Sybase IQ MS SQL with CS index Oracle Exadata IBM Netezza, IBM BLU TiDB Hawq Vectorwise Snowflake URL и IP-адресов • Performance monitoring: квантили • Geospatial: geoDistance, pointInPolygon, H3, S2 ClickHouse — гибкая система 8 Web analytics Mobile app analytics Ads analytics Realtime bidding

ClickHouse — хорошая система Я расскажу 01 Почему ClickHouse — плохая система 02 И что с этим делать 03 3 Impala Spark SQL Presto/Trino Drill Actian Vortex Kylin Kudu MonetDB Actian Vector Actian Matrix Redshift Greenplum OmniSci (mapD) Brytlyt HyPER Pinot kdb+ Shakti MemSQL (SingleStore) Vertica SAP HANA Sybase IQ MS SQL with CS index Oracle Exadata IBM Netezza, IBM BLU TiDB Hawq Vectorwise Snowflake URL и IP-адресов • Performance monitoring: квантили • Geospatial: geoDistance, pointInPolygon, H3, S2 ClickHouse — гибкая система 8 Web analytics Mobile app analytics Ads analytics Realtime bidding

Settings menu DB Navigator folders Simple and Advanced View Data editor Data Filters Data Ordering Value Panel JSON and Document View Data export Entity Diagrams SQL Editor Query Execution Plan Viewer Query History User Guide Table of contents User Guide CloudBeaver User Guide 23.1. Page 3 of 140. Resource Manager Installation Installation Version upgrade Workspace backup Configuration parameters Administration Authentication Theming Localization Database Navigator Data Editor SQL Editor Log Viewer Data Export ERD Connections Command line parameters CloudBeaver EE for AWS Overview