Looker Blocks

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At Join 2017, we’ll have a session on Customer Retention by Maire Newton. Whether your product focuses on gaming apps or an ecommerce solution, customers matter. Apply retention analysis to provide comprehensive data around customer retention and factors that affect retention.

At Join 2017, we’ll have a session on Sessionization by Arielle Strong. Organizations want to use their event data to see where their site or application is converting the most and where there’s room for improvement. Apply Sessionization to track user flows to optimize user experience, improve retention, and increase conversation rates.

At Join 2017, we’ll have a session on Analyzing Redshift Performance by Fabio Beltramini. Learn how you can leverage our Redshift Performance blocks to analyze your redshift usage and decrease query latency.

At Join 2017, we’ll have a session on AWS Cloud Management by Dillon Morrison. Use AWS? Learn how you can bring together your AWS operations like billing, CloudTrail (audit & security), and ELB in Looker to maximize your usage.

“Blocks” - like a building block - are customizable pieces of business logic, optimized SQL patterns, or full scale LookML models, which can be used as a starting point for quick and flexible data modeling in Looker.

The ease of using a Block will vary, depending on the degree to which your database schema might be standardized:

  • Data collection tools such as Segment and Snowplow track events in a relatively standardized format. This allows us to create templatized design patterns - capable of data cleansing, transformation, and analytics - which can be used by any customer using these tools.

  • Other web applications - such as Salesforce, Marketo, etc. - allow you to add custom fields for your internal users. Naturally, this creates data in a less standardized format. As a result, we can templatize some of the data model to get the analytics up and running, but you’ll need to customize the non-standardized portion.

  • Finally, we have Blocks for general business insights. These are optimized SQL or LookML design patterns that are data source agnostic. For example, the lifetime value of a customer over time is an analysis that many companies want to perform. There are some assumptions baked into these patterns, but they can be customized to match your specific business needs. These patterns reflect Looker’s point-of-view on the proper way to conduct certain types of analysis.

If you’re new to Looker, your Looker Analyst can help you get the most from these models.

Available Blocks

To find out what Blocks are currently available, please see the Blocks category on Discourse, Looker’s online community.

Adding a Block to Your Model

Directions for adding a Block to your model can be found in each Block’s respective Discourse post. And of course, feel free to reach out to a Looker Analyst for assistance.

Some things to keep in mind:

  • Some Blocks demonstrate both explores and views in the same file. This is for ease of viewing, but generally speaking, you’ll want to copy the appropriate sections of LookML into the appropriate places in your data model.
  • In some cases you’ll probably want to create new LookML files in your data model to house the examples.
  • All Looker Blocks require some customization to fit your data schema.
Still have questions?
Go to Discourse - or - Email Support
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