What is an Explore in Looker?

Jumping into Looker for the first time, you’ll immediately run into a concept called an "Explore." It's the starting point for everything you do, from building a simple chart to creating a comprehensive dashboard. This article will break down exactly what an Explore is, how it works, and why it forms the foundation of self-service analytics in Looker.

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So, What Is a Looker Explore, Exactly?

Think of an Explore as a curated, business-friendly menu for your company's data. Behind the scenes, your company has a massive, complex database - that's the kitchen. Stored in that kitchen are raw ingredients (data tables) and complex recipes (SQL queries) that a data professional needs to understand to cook up a good report.

Instead of forcing every user to become a master chef and navigate that complicated kitchen, Looker developers create Explores. An Explore is an organized, logical starting point designed for a specific type of analysis. For example, you might have common Explores named:

• Order Items: For analyzing product sales, revenue, and customer order behavior.
• Users: For understanding user demographics, sign-up trends, and engagement.
• Ad Campaign Performance: For tracking marketing spend, impressions, clicks, and conversions.

Each Explore presents you with a pre-selected list of options - called dimensions and measures - that you can use to "build your own report" without writing a single line of code. It’s the tool that transforms raw data into a playground for business users to ask and answer their own questions.

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The Foundation of Explores: A Quick Peek at LookML

Explores don't just appear out of nowhere. They are carefully constructed by data analysts or Looker developers using a language called LookML (Looker Modeling Language). While you don't need to know how to write LookML to use an Explore, understanding its role is helpful.

LookML is where all the business logic lives. It’s a layer that sits on top of your raw database and translates complex technical schemas into simple business terms. Here’s what developers define in the LookML model that ultimately powers an Explore:

• Views: These correspond to a table in your database (or a derived table). For an ecommerce store, you might have views for users, orders, and products.
• Joins: This is a critical piece. Developers define how different views relate to each other. For example, they’ll specify that the orders view can be joined to the users view using a user_id. This allows you to combine data from different tables, like analyzing which U.S.-based users placed orders last month.
• Dimensions: These are the "attributes" or "grouping" fields for your data. Think of them as the nouns in your analysis. They appear as blue columns in an Explore. Examples include Order Date, User City, Product Name, or Campaign Type.
• Measures: These are the "aggregations" or "calculations" you want to perform on your data. Think of them as the verbs. They appear as gold or orange columns in an explore. Examples include Count of Orders, Sum of Revenue, or Average Sale Price.

The beauty of this system is that all this complex setup happens once, behind the scenes. When a developer defines "Total Revenue" in LookML, everyone in the company who uses that measure gets the exact same calculation, creating a single source of truth for key metrics.

Navigating the Explore Interface: An Anatomy

When you click on an Explore in Looker, you're brought to the main query-building interface. It can feel like a lot at first, but it's organized into a few key areas that are easy to master.

1. The Field Picker (Left Panel)

This is your primary toolkit. It’s a sidebar that lists all the available fields you can use in your query, neatly organized under view names.

Dimensions are the fields you use to group, categorize, or describe your data. If you want to see sales by country, by date, or by product category, you'd select the "Country," "Date," and "Product Category" dimensions.

Measures are the fields that calculate values. After you group your sales by country, what you actually want to see is the "Total Sales" or "Average Order Size." Those are your measures.

Looker gives you visual cues: dimensions are blue, and measures are gold/orange. This simple color-coding system helps you quickly distinguish between fields for grouping and fields for counting.

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2. The Filters Section (Top Center)

This is where you narrow down your data. A report showing all your sales from the beginning of time is rarely useful. The filters section lets you set conditions to analyze a specific slice of your data.

Common filters include:

• Filtering a date dimension to "is in the last 90 days."
• Filtering a country dimension to "is equal to United States."
• Filtering a revenue measure to be "is greater than 100."

3. The Data Section (Main Area)

After you’ve selected your fields and applied your filters, you click the "Run" button. The results of your query appear here in a tabular format. Looker shows you exactly what it pulled from the database based on your selections. You can sort columns, reorder them via drag-and-drop, and perform table calculations directly in this section.

4. The Visualization Panel

Data tables are great, but charts tell a story. The Visualization panel lets you instantly transform your data table into a visual. You can choose from dozens of chart types, including bar, line, pie, map, scatterplot, and more. Looker provides intuitive options to customize colors, labels, axes, and trend lines without leaving the Explore.

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Putting It All Together: A Step-by-Step Example

Let's make this concrete. Imagine you're a marketing manager at an online store, and your boss asks, "What were our total sales by product category in North America during the first quarter of the year?"

Instead of asking a data analyst and waiting hours or days for a response, here’s how you’d answer this yourself using a Looker Explore:

1. Find the Right Explore: You log in to Looker and see a list of Explores. Ones named "Order Items" or "Sales Performance" sound relevant. You click on "Order Items."
2. Select Your Dimension: In the field picker on the left, you see a view named "Products." You expand it and click on the "Product Category" dimension.
3. Select Your Measure: You then find a view named "Sales" and click on the "Total Sale Price" measure.
4. Apply Filters: Now you narrow it down.
5. Run and Review: You click the big "Run" button. Seconds later, a table appears showing each Product Category alongside the total sales for that category within your specified date range and countries.
6. Visualize: A table is good, but a chart is better. You go to the Visualization tab and select a "Bar Chart." Instantly, your data transforms into a chart that clearly shows which categories performed best.
7. Save or Share: From here, you can save your query as a "Look" to come back to later or add your new chart directly to a dashboard to track this metric over time.

In less than five minutes, you've gone from a question to a data-driven, visual answer - all thanks to the structured environment of the Explore.

The Business Value of Explores

The Explore concept is more than just a user-friendly feature, it's a strategic approach to data that offers significant benefits.

• Data Governance and Consistency: By centralizing business logic in LookML, everyone is using the same definitions. "Revenue" means the same thing for the sales team, the marketing department, and the executive board, eliminating confusion and fostering trust in the data.
• Empowers Self-Service: Explores democratize data access. They empower non-technical team members to find their own insights, freeing up the data team from handling endless ad-hoc report requests so they can focus on more strategic projects.
• Speed and Agility: Businesses move fast. Waiting days for a report is no longer acceptable. Explores allow teams to answer questions at the speed of thought, identify trends as they happen, and make decisions based on up-to-the-minute information.

Final Thoughts

In short, a Looker Explore is your starting point for asking questions of your data. It's a curated view that transforms complex database tables into an interactive, user-friendly interface of dimensions and measures, empowering business users to build their own reports and visualizations without needing to know SQL.

Ultimately, the goal is always to make data more accessible and useful for everyone. We believe that shouldn't stop at building a great user interface. Our approach with Graphed is to remove the interface clicks altogether. You can connect your data sources - like Google Analytics, Shopify, and Salesforce - and simply have a conversation with your data. Ask "Show me my top-selling products in the UK for the last 30 days," and we instantly build the dashboard for you, helping you get directly from question to insight in seconds.