Filtering and Pivoting in the explore section in Looker allows users to organize their data in a multitude of formats. In this short video, Elan demonstrates how an eCommmerce business can organize orders by state, month, and gender to gain insights to their recent orders.
With Looker, agencies can power digital transformation through data to help promote ethical governance, conscious design, and cultivate a learning culture.
Learn how LookML offers a new, efficient tool for making modeling faster and more reusable.
Access the ebook to learn how Looker's modern data modeling can help you identify problems, automate solutions, and future-proof your data strategy.
The focus of modern business intelligence has been self-service; pushing raw data into the hands of end users quickly with an accessible user interface so they can get their own answers fast. This has
The focus of modern business intelligence has been self-service; pushing data into the hands of end users more quickly with more accessible user interfaces so they can get answers fast and on their ow
Cohort Analysis is a great way to calculate various metrics in your organization such as customer lifetime value and retention analysis.
Looker Analyst, Chris Billet, runs through LookML filtering parameters on the explore level. Through these filtering expressions, you can specify the field you'd like to filter on for future queries.
Quick iteration and reusability of metric calculations for powerful data exploration.
Business users can drive engagement and improve retention by creating a cohort analysis in Looker. In this demo, we create a dataset starting with the date in which users signed up to a website. Then
Lindsey Meyer, Support Analyst at Looker, takes us through some basics to begin writing LookML.
Looker Founder and CTO Lloyd Tabb will introduce a major new component to the modeling layer that lets you build interesting and complex computational patterns.
New analytical tools such as Looker allow data analysts to speed up the dirty work around building data models—making it less painful to clean data, explore predictive factors, and evaluate results.
A key difference of LookML is that, unlike older approaches, it combines modeling, transformations, and derivations at the same layer (late-binding modeling).