Looker Studio in Real-Time synchronization with Big Query

The blog belongs to those who are interested in data, and you can discover how to combine the powerful data analytics tools Looker Studio and Big Query here, along with an explanation of how doing so helped clients.

The following subjects will be covered in the blog:

• What is Google Big Query and Looker Studio?
• How can I real-time connect Big Query data with Looker Studio?
• The advantages of Looker Studio with Big Query.
• An explanation of how using Looker Studio with a Big Query client saves a tonne of time and effort.

Organisations can explore and analyse their data using the business intelligence and data analytics tool known as Looker Studio. Users can develop and share interactive dashboards, reports, and visualizations that offer insights into their data using Looker Studio. Looker Studio features a drag-and-drop user interface, simple visualization tool, and is accessible even to non-technical users.


(We have a wide range of data sources that we can connect from Looker Studio.)

The flexibility of Looker Studio to connect to a range of data sources, such as databases, cloud services, and APIs, is one of its primary advantages. Users may now quickly access and analyze data from various sources on a single platform thanks to this. Additionally, Looker Studio provides strong data modelling and transformation capabilities, enabling users to convert unstructured data into actionable insights.

Organisations can use Looker Studio to make data-driven decisions based on current data insights. They are able to spot patterns, recognize anomalies, and make well-informed judgements that promote corporate expansion and success.

Let’s now discuss Big Query. Large datasets can be quickly and affordably analyzed using Google Cloud Platform’s Big Query, a cloud-based data warehousing and querying tool. Users of Big Query may store and analyze enormous amounts of data quickly and easily without the need for a sophisticated infrastructure or on-site hardware.

One of the key benefits of Big Query is its scalability. Big Query can handle petabytes of data and can be scaled up or down as needed, making it suitable for businesses of all sizes. Big Query also supports real-time data ingestion, allowing users to analyze data as it’s generated.

Big Query is designed to be easy to use and offers a powerful SQL-like query language that allows users to quickly analyze their data. It also offers a range of integration options, including with Looker Studio, making it easy for organizations to connect and analyse their data on a single platform.

(Inbuilt function of Big Query to explore results in Looker Studio)

Overall, Looker Studio and Big Query are both powerful tools for data analytics and can help organizations make data-driven decisions. By combining the two, organizations can access real-time data insights and unlock the full potential of their data.

Visualize Big Query data with looker studio using real-time connections:

There are a variety of ways to link Big Query data to Looker Studio; in this part, we’ll focus on the most effective ones.

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Using the Looker Studio Connection “Custom Query Connector”:

This is the most effective technique to visualize the results of a big query. With the Custom Query Connector, you can extract and manipulate data in ways that are not possible with regular Looker connections, making it a powerful tool for working with Big Query data in Looker. However, since it necessitates a solid grasp of SQL and database connectivity, not all users may find it appropriate. But the Data Engineers and Data Analysts can easily feel the efficiency in this.

The Big Query project and dataset, as well as the SQL query that obtains the data, must be provided in order to set up the Custom Query Connector. Once the connection is made, you can use the data to build Looker models, views, and dashboards, and you can use mechanisms like caching and data refreshing to update them in real-time.

The scheduling feature of the custom query in looker studio, runs the query in Big Query and updates the dashboard at scheduled intervals.
When dealing with large amounts of data, it will be difficult to sort and filter when we establish this from the Looker studio end, but we can implement this from either end.


(The above picture shows the options to select when we do it from the looker studio end.)

Click on Big Query data source and then select custom query, as highlighted in the above picture.

Additionally, there is a second method for using the custom query connector on the BQ end; all we need to do is use the Big Query function “explore data using looker studio” after query is executed.

(The above picture shows the options to select when we do it from the Looker Studio end from the Big Query end. )

Click on “Explore Data” and then click on “Explore with Looker Studio” as highlighted in the above picture.

It is easy and straightforward to create BI dashboards using the Custom Query connector, and we can schedule it. Later, we can add more data sources using the same query or a different query, and we can merge the data using the “Edit Connection” tool in Looker Studio.


(The above picture shows the options to select to edit the connection in Looker Studio.)

Click on “Big Query Custom SQL” under Data Function and then click on “Edit Connection” as highlighted in the above picture.
When we choose “Big Query Custom SQL” under Data Sources and then click “edit”, a pop-up window al-lowing us to “edit the connection” to the data source appears.

There are a few other ways to connect Big Query with Looker Studio.

Using Google Cloud Pub/Sub to send data updates to Looker in real-time:

With Big Query as your data source, you can set up a Cloud Function that listens for data changes in Big Query and publishes those changes to the Pub/Subtopic. Once you’ve created a Pub/Sub topic and config-ured your data source to send data updates, you’ll need to set up a Looker connection that can receive those updates. This can be done by creating a new Looker connection and configuring it to use the Pub/Sub topic as its data source.

Using cache warming process, which preloads the Looker cache with the latest data at regular inter-vals:

Cache warming is the process of preloading Looker’s cache with the latest data at regular intervals. This improves the performance of Looker dashboards and visualisations by ensuring the most up-to-date data is readily available in the cache. The process involves scheduling the cache warming, running a script to populate the cache with the latest data, monitoring the process, and tuning it for efficiency.

Looker Studio with Big Query benefits:

Real-time data visualisation: Looker Studio provides real-time access to data stored in Big Query, ena-bling users to visualise and analyse data as it is updated in real-time.

Centralised data modelling: Looker Studio enables you to develop centralised data models that can be uti-lised by numerous teams and departments within your company, ensuring accuracy and consistency in your data analysis.

Customizable dashboards: Looker Studio enables you to create customised dashboards that can be tailored to the specific needs of different teams and departments, making it easier to share insights and drive data-driven decision-making.

Easy-to-use interface: The user-friendly interface on Looker Studio makes it simple for users to construct and edit dashboards and visualisations without the need for substantial technical expertise.

Scalability: Because Looker Studio is highly scalable, you can manage significant data volumes and meet rising user demand without sacrificing performance.

Integration with other tools: Data analysis may be easily included into your current processes thanks to Looker Studio’s seamless integration with a variety of other tools and technologies, including Google Cloud Platform and a wide range of third-party applications.

Overall, Looker Studio provides a powerful, flexible, and user-friendly platform for visualising and ana-lysing data stored in Big Query, enabling organisations to gain valuable insights and make data-driven de-cisions with greater speed and accuracy.

Looker Studio with the Big Query client use case saved a lot of time and effort.

Here is a challenge we learned about from one of our clients, the largest multiplex chain in India. When the team was considering a solution to identify pipeline failures for more than 50 production pipelines, I suggested using Looker Studio and suggested that we create a metadata table that collected data on the success and failure of runs and exceptions using pub/sub messages. From this metadata table, I built a complex query using windowing fun.

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Now that you are more familiar with the connections, you should be able to see how we can rapidly link our Big Query data to Looker Studio. Overall, Looker Studio with Big Query provides a scalable, flexible, and user-friendly platform for visualising and analysing data, making it the ideal choice for enterprises wishing to get insights and expedite decision-making with greater speed and accuracy.

In the next blogs, you will see other unique aspects of the integration of Big Query with Looker Studio.

Gratitude for reading. I hope this is helpful.