Does Looker Store Data?
Thinking about using Looker or Looker Studio often leads to a fundamental question: does Looker actually store my data? The short answer is no, it doesn't store your core business data, but understanding how it works reveals why that's a powerful feature, not a limitation. This article breaks down Looker's architecture, its "live connection" model, and how it compares to other business intelligence tools.
The Short Answer: No, Looker Connects Directly to Your Database
Unlike some other tools, Looker is not a database or a data warehouse. It doesn’t create and store a permanent copy of your organization's data. Instead, it functions as a "data platform" that sits on top of your existing database infrastructure. Its primary job is to provide an interface to query, analyze, and visualize the data that you already have stored elsewhere.
Think of it this way: your database (like Google BigQuery, Amazon Redshift, or Snowflake) is the library holding all your books (your data). Looker is the expert librarian who knows exactly where every book is and can fetch any piece of information for you on demand. The librarian doesn’t keep a separate, photocopied version of the entire library, they simply provide a fast, intelligent way to access the original source.
This approach means that when you build a report in Looker, you're always looking at the most up-to-date, real-time information available in your database. There’s no data lag or need to wait for a scheduled refresh to see the latest sales figures or campaign results.
How Looker Works: The Live Connection Model
Looker’s power comes from its direct, in-database query approach. The process from user click to data visualization happens in a seamless, behind-the-scenes cycle. Here’s a simplified breakdown of the steps involved:
You Ask a Question: You interact with a Looker dashboard, filter a report, or use the "Explore" feature to build a new analysis. This is your request for information.
Looker Writes the Code: Looker's modeling layer, called LookML, translates your request into an optimized SQL query. You don't need to know SQL, Looker handles the translation for you.
The Query is Sent to Your Database: Looker sends this perfectly formatted SQL query directly to your connected database.
Your Database Does the Heavy Lifting: Your database, which is designed for high-performance querying, processes the request, crunches the numbers, and finds the answer.
Results are Sent Back: The database returns the query results - just the final numbers needed - back to Looker. Critically, it does not send the entire dataset.
Data is Visualized: Looker takes the formatted results and renders them into the chart, graph, or table you see on your screen.
This entire cycle happens in seconds, giving you an interactive and constantly fresh view of your business metrics.
The Magic Behind it All: Understanding the Semantic Layer (LookML)
The secret that makes Looker's live connection model work for non-technical users is its semantic layer, which is defined using a language called LookML (Looker Modeling Language).
A semantic layer is essentially a business-friendly map of your database. Instead of forcing marketers and sales leaders to learn complex table names like fct_sales_transactions and write SQL joins, a data analyst uses LookML to define business logic centrally. For example, they can create a "dimension" called "Revenue" and define it once as SUM(sale_price).
Once defined, any user in the company can simply drag and drop "Revenue" into a report, and Looker knows exactly how to write the correct SQL query to calculate it. This provides a single source of truth for your key metrics, ensuring everyone is speaking the same language.
The benefits of this are enormous:
Consistency: Everyone calculates "customer lifetime value" or "marketing qualified lead" the same way because the logic is defined in one place.
Accessibility: Your entire team can self-serve and ask questions of your data without needing to know SQL or bothering an analyst.
Governance: It allows you to control who sees what data, ensuring that sensitive information remains secure.
The One Exception: Looker's Caching System
While Looker doesn't store your raw data, it does have a performance trick up its sleeve: caching.
Caching is the process of temporarily storing the results of a query for a predetermined amount of time. If you run a report for "Sales Last 7 Days," Looker can cache the results. If your colleague runs the exact same report five minutes later, Looker will serve the result from its cache instead of sending a new query to the database.
This is done for two key reasons:
Speed: Serving results from a cache is almost instantaneous, making dashboards load faster.
Cost-Efficiency: It reduces the load on your database, which can save money since many data warehouses charge based on the amount of data queried.
It's important to remember that this is a temporary, performance-enhancing feature, not long-term storage. Administrators have full control over caching policies and can set how long results should be cached, balancing performance needs with the demand for real-time freshness.
How Looker Compares to Other BI Tools
Understanding whether a BI tool stores data is critical when comparing options. Looker’s live-query architecture is a key differentiator from the two main models used by tools like Tableau and Power BI.
Tableau's Model: Live Connection vs. Extracts
Tableau offers both live connections and proprietary data extracts (.hyper files). While its live connections are similar to Looker's, many users rely on Tableau Extracts for performance. An extract is a highly compressed snapshot of a dataset, or a part of it, which is imported and stored by Tableau.
Looker: Primarily designed for a live connection model, using your database's power. It doesn’t create separate static extracts.
Tableau: Can do both, but extracts are a very common way to speed up complex dashboards. This means the data is a periodic snapshot and not always live unless the extracts are refreshed frequently.
Power BI's Model: DirectQuery vs. Import
Microsoft's Power BI operates in a similar fashion to Tableau. It has a "DirectQuery" mode, which works like Looker's live connection. However, its default and most common mode is "Import." In Import mode, Power BI pulls a copy of your data into its internal, highly compressed columnar database engine.
Looker: Connects remotely to your data source.
Power BI: Often works with a copy of your data stored within the Power BI file itself. This makes reports fast and portable, but again, you're looking at a snapshot in time. You must set up scheduled refreshes to keep the data updated.
In essence, Looker is built on the philosophy that your data should live in a powerful, centralized database, and the BI tool's job is to be the smartest possible window into that database. Other tools often take the approach of pulling out copies of the data to optimize performance locally.
Final Thoughts
To recap, Looker does not store your core business data. It connects directly to your existing database, uses its powerful LookML semantic layer to translate business questions into SQL, and brings back an answer in real time. The only data it "stores" is a temporary cache of query results, used purely to make dashboards and reports load faster.
For organizations with a modern data stack but not the resources for an extensive data team, this BI landscape can still feel complex. We built Graphed to simplify this process entirely. Instead of configuring dashboards or learning a modeling language, you can connect your marketing and sales platforms - like Google Analytics, Shopify, Facebook Ads, and Salesforce - in seconds. From there, you just ask questions in plain English, and we instantly build the real-time dashboards you need, getting you from data to answers without the technical overhead.