Optimizing Performance of LookML Queries

在此项目中,您将:
1 hour 30 minutes
中级
无需下载
可分享的证书
英语(English)
仅限桌面

This is a Google Cloud Self-Paced Lab. In this lab, you'll learn the best methods to optimize query performance in Looker. Looker is a modern data platform in Google Cloud that you can use to analyze and visualize your data interactively. You can use Looker to do in-depth data analysis, integrate insights across different data sources, build actionable data-driven workflows, and create custom data applications. Big, complex queries can be costly, and running them repeatedly strains your database, thereby reducing performance. Ideally, you want to avoid re-running massive queries if nothing has changed, and instead, append new data to existing results to reduce repetitive requests. Although there are many ways to optimize performance of LookML queries, this lab focuses on the most commonly used methods to optimize query performance in Looker: persistent derived tables, aggregate awareness, and performantly joining views.

您要培养的技能

  • Looker

  • Google Cloud Platform

  • LookML

  • Complex Data Queries

  • Data Analysis

项目工作原理

在交互式实践环境中学习新工具或新技能

您将能够访问云工作空间中的软件和工具 - 无需下载

提供方

Placeholder

Google 云端平台

常见问题