Enhancing Performance in Looker Studio: Leveraging Extract Data

What is Looker Studio?

If you’re familiar with data work, chances are you’ve come across Looker Studio, previously known as Google Data Studio. It’s a powerful tool from Google that lets you turn raw data into interactive dashboards and reports. In this article, we’ll explore into Looker Studio basics, focusing on a crucial feature called Extract Data, which greatly enhance dashboard performance

You can connect your data to Looker Studio with a wide variety of sources such as:

· Databases: BigQuery, MySQL, and PostgreSQL

· Google Marketing Platform products, including Google Ads, Analytics, Display & Video 360, Search Ads 360

· Google consumer products, such as Sheets, YouTube, and Search Console

· Flat files via CSV file upload and Google Cloud Storage

· Social media platforms such as Facebook, Reddit, and Twitter

· Blended data from any combination of related sources

Looker Studio also encourages collaboration in creating dashboards, you can work together with your team simply by inviting them to your dashboards and then you can work on it together in real-time.

For those who want to try Looker Studio, you can read this article on how to create your first dashboard on Looker Studio.

Create your first Looker Studio (Google Data Studio) Dashboard

For experienced Looker Studio users, I will be sharing ways to enhance your dashboard’s speed and cut costs. We’ll tap into Looker Studio’s Extract Data feature, providing insights, usage tips, and tricks.

Live Data vs. Extract Data

In this article, I will be using BigQuery as the source of our data so we can compare the performance later on. Before we get into how to use the Extract Data, we need to know the difference between live data connection and extract data.

· Live Data Connection

In a live connection, the analytics tool queries the data source in real time whenever information is requested. Making them suitable for scenarios where real-time information is critical, such as monitoring dashboards. This ensures the most up-to-date data, but it can impact performance, especially with extensive datasets or complex queries.

· Extract Data Connection

In contrast, an extract connection involves pulling data from the source and loading it into the analytics tool’s database, creating a snapshot for analysis. This static snapshot remains until a refresh occurs, making it suitable for historical analysis or scenarios where minimizing the load on the source system is vital.

Connecting data to Looker Studio usually results in a live connection. To optimize dashboard usage, choose the data connection based on your dashboard needs. If real-time data isn’t crucial, consider using the Extract Data option — it makes your dashboard load faster and saves costs.

Advantages & Disadvantages of Looker Studio Extract Data

Now we know how to use Extract Data on Looker Studio we also need to know the advantages and disadvantages of extracting data.

Advantages:

· Improved Dashboard Load Speed

Extracting and storing the data snapshot on visualisation tools reduces the need for the system to query the data, hence improving load speed and providing a faster and more responsive dashboard.

· Cost Optimization

By using Extract Data, visualization tools make a single data query, storing it in cloud storage. In comparison, imagine if 200 people accessing the dashboard, each triggering a data request. With usage-based pricing, choosing Extract Data can notably improve your billing costs.

Disadvantages:

· Data Freshness

The main trade-off is that the extracted data is static until the next refresh schedule occurs. This means the dashboard will not reflect the most recent changes in the data source, making it less suitable for scenarios that require real-time or near-real-time information.

· Limited Data Size

Be mindful: Looker Studio’s Extract Data caps at 100MB. If your data surpasses this, there’s no error alert — just inaccurate dashboard info. Keeping data within this limit is crucial for maintaining accuracy in Looker Studio.

How to use Extract Data on Looker Studio

1. Sign in to Looker Studio.
2. Make sure you are already connected to the data source.
3. On the Looker Studio home page, in the top left, click Create and then select Data Source.
4. In the connectors list, select Extract Data.
5. Select an existing data source to extract from.
6. Select the dimensions and metrics to extract by dragging them from the Available Fields list onto the targets, or by clicking Add. All the fields you add appear in the list on the far right.
7. (Optional) If the data is unaggregated, consider applying an aggregation, such as Sum, or Average, to reduce the amount of data extracted.
8. (Optional) Apply filters to the data to reduce the number of rows.
9. Apply a date range. Date ranges are required by some connectors, such as Analytics, but are optional for other connector types.
10. Name your data source by clicking Untitled Data Source in the upper left.
11. (Optional) To automatically refresh your data, in the lower right, turn on Auto-update and set an update schedule.
12. In the lower right, click SAVE AND EXTRACT.

In conclusion, this article has equipped you with the knowledge to utilize Extract Data in Looker Studio effectively. By understanding its benefits and trade-offs, you can optimize your dashboards, improve load speed, and make informed choices regarding data connections. Stay tuned for more insights in future articles!