議題
金融業的資料庫中，累積了巨大的資料量，就單單以信用卡為例，本行截至 2023 第 1 季，流通卡數達到了470.1 萬張，加上我們其他產品線業務，可以想像是一項巨大的數據處理工程。作為 AI 部門的 PostgreSQL 資料庫管理人員，我們致力於以系統面向的角度來提升服務效能。
In the database of the financial sector, a vast amount of data has been accumulated. Take the number of credit cards as an example, our bank has reached a circulation of 4.71 million cards as of the first quarter of 2023. When combined with our other product lines, it can be imagine as a massive data processing . As the PostgreSQL database administrator in the AI department, our goal is to enhance service performance from a system-oriented perspective.
為了更深入探討這個議題，我將進一步探討 PostgreSQL 底層資料處理的概念以及需要注意的事項。同時，分享如何解決和優化資料科學家們在 SQL 中遇到的效能瓶頸，這裡整理了幾個 SQL 執行中常見的情況和可解決的方案，並提供一些實用的 PostgreSQL 資料庫技巧，這些技巧能夠幫助你在面對大量資料時，保持 SQL 程式碼的執行效率，期待與您相見，交流彼此的經驗心得。
To delve deeper into this topic, I will discuss the concepts and considerations of data processing in PostgreSQL. Additionally, I will share how to solve and optimize the performance bottlenecks encounter by data scientists in SQL. Here, I have compiled several common scenarios in SQL, and provided some practical PostgreSQL database tips. These tips will assist you in maintaining the execution efficiency of your SQL code when dealing with large volumes of data. I look forward to meeting you and exchanging our experiences and insights.
講者
JackyLin
金融業資料工程師經歷 9 年，目前於金融業 AI 部門 擔任 Data engineer 使用 kubernetes 架構金融業 AI 分析平台 目前擔任 DBA 負責管理 Postgres DB 資源與效能