Optimizing Performance of LookML Queries

在此項目中,您將:
1 hour 30 minutes
中級
無需下載
可分享的證書
英語(English)
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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.

您要培養的技能

  • Looker

  • Google Cloud Platform

  • LookML

  • Complex Data Queries

  • Data Analysis

項目工作原理

在交互式實踐環境中學習新工具或新技能

您將能夠訪問云工作空間中的軟件和工具 - 無需下載

提供方

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Google 云端平台

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