How to connect Google Data Studio to a CSV file using BigQuery and Cloud Storage

How to connect Google Data Studio to a CSV file using Big Query and Cloud Storage

So let’s say you have data sitting in a very big CSV file. What’s the best way to connect it to Google Data Studio? In this post I’ll show you a method of connecting a big CSV file using Google BigQuery and Cloud Storage.

So let’s say we have a CSV file that is very big. It’s over 30 mb and has over 35,000 rows. That’s too large for Google Sheets. The best option is to upload it to Google Cloud Storage and then access it via Google Big Query.

a CSV file that is very big

In this blog post I’ll show you a method to do that.

Step 1: Create a Storage Bucket within Google Cloud Platform

If you don’t already have a Google Cloud Platform account you will need to set one up.

Here is the link to Google Cloud Platform.

The next step is to create a Storage Bucket. Click on Storage.

create a Storage Bucket in Cloud Storage

Now click on ‘Create Bucket’.

Now click on 'Create Bucket'.

You will then need to give your bucket a unique name. For this example I’ve used ‘example_bucket_abcdefg’.

naming your bucket in cloud storage

Next there are various options for setting up your bucket. You can choose whichever options best suit your needs, or simply go for the default options.

For example, one of the options presented is to choose Mult-region as the Location type.

choosing bucket location in cloud storage

Once you have finished selecting the options for your bucket, click Create.

the screen shown after successfully creating a bucket in google cloud storage

Step 2: Uploading your CSV file to the Storage Bucket

The next step is to upload the CSV file to your newly created storage bucket.

Click ‘Upload files’ and select the file you want.

bucket details in google cloud storage

Your file should then start uploading, as shown below.

uploading a csv file to cloud storage

Once it is upload you will see it in your storage bucket.

Step 3: Create a Dataset based on the file in BigQuery

Open up Google BigQuery and click ‘Create Dataset’.

Open up Google BigQuery and click 'Create Dataset'.

Give it a unique name. In this case I’ve named it ‘example_data_01’.

creating a dataset

You will see a notification that the data source has been created and will be able to view it under your project’s datasets.

datasets in bigquery

The next step is to create a table within this dataset

Step 4: Create a new Table within this Dataset

Next we need to click ‘Create Table’ within our newly created dataset.

creating a table in google bigquery

When asked where to create the table from we need to select ‘Google Cloud Storage’.

Create a new Table within this Dataset

We then need to choose the storage bucket we created a few moments ago. Then after clicking into the storage bucket we then choose our csv file.

Once we’ve selected the csv file there are a few more things we need to do.

We need to give the table a name. In this example I’ve named it ‘example_data_table’.

I’ve also checked the box to auto-detect schema and input parameters. We can then click ‘Create table’.

creating a table in google bigquery

Once we’ve create the table we will see a notification in the bottom left corner of the screen. Click ‘Go to table’.

We now have our table with all the data accessible using BigQuery.

table with all the data accessible using BigQuery.

Step 5: Connect to the data using Google Data Studio

The final step is to connect to this data using Google Data Studio.

Open up Google Data Studio and choose the BigQuery connector.

The BigQuery connector in Google data Studio

Open up the Project, Data set and table that we’ve created. Click the correct table and add the data.

Lo and behold we have the data accessible in Google BigQuery! Just ready for some very fast visualizations.

data visible in google data studio

I hope you found this blog post about Google BigQuery and Cloud Storage useful.

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