Beyond Sheets: Get Started With Google BigQuery

This tutorial is written for Google Sheets users who have datasets that are too big or too slow to use in Google Sheets. It’s written to help you get started with Google BigQuery.

If you’re experiencing slow Google Sheets that no amount of clever tricks will fix, or you work with datasets that are outgrowing the 10 million cell limit of Google Sheets, then you need to think about moving your data into a database.

As a Google user, probably the best and most logical next step is to get started with Google BigQuery and move your data out of Google Sheets and into BigQuery.

We’ll explore five topics:

1. What is BigQuery?
2. Google BigQuery Setup
3. How to get your data from Google Sheets into BigQuery
4. How to analyze your data in BigQuery
5. How to get your data out of BigQuery back into Google Sheets for reporting

By the end of this tutorial, you will have created a BigQuery account, uploaded a dataset from Google Sheets, written some queries to analyze the data and exported the results back to Google Sheets to create a chart.

You’ll also do the same analysis side-by-side in a Google Sheet, so you can understand exactly what’s happening in BigQuery.

I’ve highlighted the action steps throughout the tutorial, to make it super easy for you to follow along:

Section 1: What is BigQuery?

Google BigQuery is a data warehouse for storing and analyzing huge amounts of data.

Officially, BigQuery is a serverless, highly-scalable, and cost-effective cloud data warehouse with an in-memory BI Engine and machine learning built in.

This is a formal way of saying that it’s:

• Works with any size data (thousands, millions, billions of rows…)
• Easy to set up because Google handles the infrastructure
• Grows as your data grows
• Good value for money, with a generous free tier and pay-as-you-go beyond that
• Lightning fast
• Seamlessly integrated with other Google tools, like Sheets and Data Studio
• Can import and export data from and to many sources
• Has Built-in machine learning, so predictive modeling can be set up quickly

What’s the difference between BigQuery and a “regular” database?

BigQuery is a database optimized for storing and analyzing data, not for updating or deleting data.

It’s ideal for data that’s generated by e-commerce, operations, digital marketing, engineering sensors etc. Basically, transactional data that you want to analyze to gain insights.

A regular database is suitable for data that is stored, but also updated or deleted. Think of your social media profile or customer database. Names, emails, addresses, etc. are stored in a relational database. They frequently need to be updated as details change.

Section 2: Google BigQuery Setup

It’s super easy to get started wit Google BigQuery!

There are two ways to get started: 1) use the free sandbox account (no billing details required), or 2) use the free tier (requires you to enter billing details, but you’ll also get $300 free Cloud credits).

In either case, this tutorial won’t cost you anything in BigQuery, since the volume of data is so tiny.

We’ll proceed using the sandbox account, so that you don’t have to enter any billing details.

Here’s that process shown as a GIF:

You’re ready for Step 2 below.

BigQuery Console

Here’s what you can see in the console:

1. The SANDBOX tag to tell you you’re in the sandbox environment
2. Message to upgrade to the free trial and $300 credit (may or may not show)
3. UPGRADE button to upgrade out of the Sandbox account
4. ACTIVATE button to claim the free $300 credit
5. The current project and where to create new projects
6. The Query editor window where you type your SQL code
7. Current project resource
8. Button to create a new dataset for this project (see below)
9. Query outputs and table information window

What is the free Sandbox Account?

The sandbox account is an option that lets you use BigQuery without having to enter any credit card information. There are limits to what you can do, but it gives you peace of mind that you won’t run up any charges whilst you’re learning.

In the sandbox account:

• Tables or views last 60 days
• You get 10 Gb of storage per month for free
• And 1 Tb data processing each month

It’s more than enough to do everything in this tutorial today.

How to set up the BigQuery sandbox (YouTube video from Google Cloud)

BigQuery Pricing for Regular Accounts

Unlike Google Sheets, you have to pay to use BigQuery based on your storage and processing needs.

However, there is a sandbox account for free experimentation (see below) and then a generous free tier to continue using BigQuery.

In fact, if you’re working with datasets that are only just too big for Sheets, it’ll probably be free to use BigQuery or very cheap.

BigQuery charges for data storage, streaming inserts, and for querying data, but loading and exporting data are free of charge.

Your first 1 TB (1,000 GB) per month is free.

Full BigQuery pricing information can be found here.

Clicking on the blue “Try BigQuery free” button on the BigQuery homepage will let you register your account with billing details and claim the free $300 cloud credits.

Section 3: How to get your data into BigQuery

Extracting, loading and transforming (ELT) is sometimes the most challenging and time consuming part of a data analysis project. It’s the most engineering-heavy stage, where the heavy lifting happens.

You can load data into BigQuery in a number of ways:

1. From a readable data source (such as your local machine)
2. From Google Sheets
3. From other Google services, such as Google Ad Manager and Google Ads
4. Use a third-party data integration tool, e.g. Supermetrics, Stitch
5. Use the CIFL BigQuery connector
6. Write Apps Script to upload data
7. From Google Cloud Storage, such as Google Cloud SQL
8. Other advanced methods specific to Google Cloud

In this tutorial, we’ll look at loading data from a Google Sheet into BigQuery.

Get started with Google BigQuery: Dataset For This Tutorial

The first dataset is a record of pedestrian traffic crossing Brooklyn Bridge in New York city (source).

It’s only 7,000 rows, so it could be easily analyzed in Sheets of course, but we’ll use it here so that you can do the same steps in BigQuery and in Sheets.

The second dataset is a daily total of bike counts for New York’s East River bridges (source).

There’s noting inherently wrong with putting “small” data into BigQuery. Yes, it’s designed for truly gigantic datasets (billions of rows+) but it works equally well on data of any size.

Back in the BigQuery Console, you need to set up a project before you can add data to it.

Get started with Google BigQuery: Loading data From A Google Sheet

Think of the Project as a folder in Google Drive, the Dataset as a Google Sheet and the Table as individual Sheet within that Google Sheet.

The first step to get started with Google BigQuery is to create a project.

In step 1, BigQuery will have automatically generated a new project for you, called “My First Project”.

If it didn’t, or you want to create another new project, here’s how.

This new dataset will show up underneath your project name in the sidebar.

Section 4: Analyzing Data in BigQuery

Google BigQuery uses Structure Query Language (SQL) to analyze data.

The Google Sheets Query function uses a similar SQL style syntax to parse data. So if you know how to use the Query function then you basically know enough SQL to get started with Google BigQuery!

Basic SQL Syntax for BigQuery

The basic SQL syntax to write queries looks like this:

You’ll see all of these keywords and more in the exercises below.

Get started with Google BigQuery: First Query

The BigQuery console provides a button that gives you a starter query.

SELECT  FROM `start-bigquery-294922.start_bigquery.brooklyn_bridge_pedestrians` LIMIT 1000

SELECT * FROM `start-bigquery-294922.start_bigquery.brooklyn_bridge_pedestrians` LIMIT 10

SELECT
  *
FROM
  `start-bigquery-294922.start_bigquery.brooklyn_bridge_pedestrians`
LIMIT
  10

The output of this query will be 10 rows of data showing under the query editor:

Woohoo!

You just wrote your first query in Google BigQuery.

Let’s continue and analyze the dataset:

Exercise 2: Analyzing Data In BigQuery

Run through the following steps:

Think about doing the equivalent step in Google Sheets. Look at your dataset and scroll to the right, telling the story of a single row.

We do this step to understand our data, before getting too immersed in the weeds.

Select Specific Columns

SELECT
  hour_beginning,
  location,
  Pedestrians,
  weather_summary
FROM
  `start-bigquery-294922.start_bigquery.brooklyn_bridge_pedestrians`
LIMIT
  10

Math Operations

Let’s find out the total number of pedestrians that crossed the Brooklyn Bridge across the whole time period.

Let’s see how to do that in BigQuery:

SELECT
  SUM(Pedestrians) AS total_pedestrians
FROM
  `start-bigquery-294922.start_bigquery.brooklyn_bridge_pedestrians`

You’ll notice that I gave the output a new column name using the code “AS total_pedestrians“. This is similar to using the LABEL clause in the QUERY function in Google Sheets

Filtering Data

In SQL, the WHERE clause is used to filter rows of data.

It acts in the same way as the filter operation on a dataset in Google Sheets.

You end up with 61 rows of data showing only the “sleet” or “snow” rows.

Now let’s see that same filter in BigQuery.

SELECT
  *
FROM
  `start-bigquery-294922.start_bigquery.brooklyn_bridge_pedestrians`
WHERE
  weather_summary = 'snow' OR weather_summary = 'sleet'

Check the count of the rows outputted by the this query. It’s 61, which matches the row count from your Google Sheet.

Ordering Data

Another common operation we want to do to understand our data is sort it. In Sheets we can either sort through the filter menu options or through the Data menu.

Let’s recreate this sort in BigQuery.

SELECT
  *
FROM
  `start-bigquery-294922.start_bigquery.brooklyn_bridge_pedestrians`
ORDER BY
  temperature ASC;

Use the keyword ASC to sort ascending (A – Z) or the keyword DESC to sort descending (Z – A).

You might notice that the first two records that show up have “null” in the temperature column, which means that no temperature value was recorded for those rows or it’s missing.

Let’s filter them out with the WHERE clause, so you can see how the WHERE and ORDER BY fit together.

SELECT
  *
FROM
  `start-bigquery-294922.start_bigquery.brooklyn_bridge_pedestrians`
WHERE
  temperature IS NOT NULL
ORDER BY
  temperature ASC;

Aggregating Data

In Google Sheets, we group data with a pivot table.

Typically you choose a category for the rows and aggregate (summarize) the data into each category.

In this dataset, we have a row of data for each hour of each day. We want to group all 24 rows into a single summary row for each day.

Your pivot table should look like this, with the total pedestrian counts for each day:

Now let’s recreate this in BigQuery.

If you’ve ever used the QUERY function in Google Sheets then you’re probably familiar with the GROUP BY keyword. It does exactly what the pivot table in Sheets does and “rolls up” the data into the summary categories.

SELECT
  EXTRACT(DATE FROM hour_beginning) AS bb_date,
  hour_beginning
FROM
  `start-bigquery-294922.start_bigquery.brooklyn_bridge_pedestrians`

SELECT
  EXTRACT(DATE FROM hour_beginning) AS bb_date,
  SUM(Pedestrians) AS bb_pedestrians
FROM
  `start-bigquery-294922.start_bigquery.brooklyn_bridge_pedestrians`
GROUP BY 
  bb_date

The output of this query will be a table that matches the data in your pivot table in Google Sheet. Great work!

Functions in BigQuery

You’ll notice we used a special function (EXTRACT) in that previous query.

Like Google Sheets, BigQuery has a huge library of built-in functions. As you make progress on your BigQuery journey, you’ll find more and more of these functions to use.

For more information on functions in BigQuery, have a look at the function reference.

There’s also this handy tool from Analytics Canvas that converts Google Sheets functions into their BigQuery SQL code equivalent.

Filtering Aggregated Data

We saw the WHERE clause earlier, which lets you filter rows in your dataset.

However, if you aggregate your data with a GROUP BY clause and you want to filter this grouped data, you need to use the HAVING keyword.

Remember:

• WHERE = filter original rows of data in dataset
• HAVING = filter aggregated data after a GROUP BY operation

To conceptualize this, let’s apply the filter to our aggregate data in the Google Sheet pivot table.

This filter removes rows of data in your Pivot Table where the data is on or after 1 November 2017. It leaves just the October 2017 data.

By now, I think you know what’s coming next.

Let’s apply that same filter condition in BigQuery using the HAVING keyword.

SELECT
  EXTRACT(DATE FROM hour_beginning) AS bb_date,
  SUM(Pedestrians) AS bb_pedestrians
FROM
  `start-bigquery-294922.start_bigquery.brooklyn_bridge_pedestrians`
GROUP BY 
  bb_date
HAVING 
  bb_date < '2017-11-01'

The output of this query is 31 rows of data, for each day of the month of October.

Get started with Google BigQuery: Joining Data

A SQL Query walks into a bar.
In one corner of the bar are two tables.
The Query walks up to the tables and asks:
Mind if I join you?

JOIN pulls multiple tables together, like the VLOOKUP function in Google Sheets. Let's start in your Google Sheet.

The data in your Sheet now looks like this:

That's great!

We summarized the pedestrian data by day and joined the bicycle data to it, so you can compare the two numbers.

As you can see, there's around 10k - 20k pedestrian crossings/day and about 2k - 3k bike crossings/day.

Joining tables in BigQuery

Let's recreate this table in BigQuery, using a JOIN.

Your project should now look like this in the Resources pane in the left sidebar:

What we want to do now is take the table the you created above, with pedestrian data per day, and add the bike counts for each day to it.

To do that we use an INNER JOIN.

There are several different types of JOIN available in SQL, but we'll only look at the INNER JOIN in this article. It creates a new table with only the rows from each of the constituent tables that meet the join condition.

In our case the join condition is matching dates from the pedestrian table and the bike table.

We'll end up with a table consisting of the date, the pedestrian data and the bike data.

Ready? Let's go.

WITH pedestrian_table AS (
  SELECT
    EXTRACT(DATE FROM hour_beginning) AS bb_date,
    SUM(Pedestrians) AS bb_pedestrians
  FROM
    `start-bigquery-294922.start_bigquery.brooklyn_bridge_pedestrians`
  GROUP BY 
    bb_date
  HAVING 
    bb_date < '2017-11-01'
)

SELECT 
  pedestrian_table.bb_date,
  pedestrian_table.bb_pedestrians,
  bike_table.Brooklyn_Bridge AS bb_bikes
FROM
  pedestrian_table

INNER JOIN 
  `start-bigquery-294922.start_bigquery.nyc_bridges_bikes` AS bike_table

ON 
  pedestrian_table.bb_date = bike_table.Date

WITH pedestrian_table AS (
  SELECT
    EXTRACT(DATE FROM hour_beginning) AS bb_date,
    SUM(Pedestrians) AS bb_pedestrians
  FROM
    `start-bigquery-294922.start_bigquery.brooklyn_bridge_pedestrians`
  GROUP BY 
    bb_date
  HAVING 
    bb_date < '2017-11-01'
)
SELECT 
  pedestrian_table.bb_date,
  pedestrian_table.bb_pedestrians,
  bike_table.Brooklyn_Bridge AS bb_bikes
FROM
  pedestrian_table
INNER JOIN 
  `start-bigquery-294922.start_bigquery.nyc_bridges_bikes` AS bike_table
ON 
  pedestrian_table.bb_date = bike_table.Date

You'll notice that the names of the columns in our SELECT clause are preceded by the table name, e.g. "pedestrian_table.bb_date".

This ensures there is no confusion over which columns from which tables are being requested. It’s also necessary when you join tables that have common column headings.

The output of this query is the same as the table you created in your Google Sheet step 20 (using the pivot table and VLOOKUP).

Formatting Your Queries

Last couple of things to mention with the SQL syntax is how to add comments and format your queries.

-- single line comment, ignored when the program is run

/* multi-line comment
everything between the slash-stars 
is ignored by the program when it's run */

Section 5: Export Data Out Of BigQuery

You have a few options to export data out of BigQuery.

In the Query results section of the editor, click on the "SAVE RESULTS" button to:

• Save as a CSV file
• Save as a JSON file
• Export query results to Google Sheets (up to 16,000 rows)
• Copy to Clipboard

In this tutorial, we're going to export the data out of BigQuery and back into a Google Sheet, to create a chart. We're able to do this because the summary dataset we've created is small (it's aggregated data we want to use to create a chart, not the row-by-row data).

Explore BigQuery Data in Sheets or Data Studio

If you want to create a chart based on hundreds of thousands or millions of rows of data, then you can explore the data in Google Sheets or Data Studio directly, without taking it out of BigQuery.

Click on the "EXPLORE DATA" option in the Query results section of the editor:

• Explore in Google Sheets using Connected Sheets (Enterprise customers only)
• Explore directly in Data Studio

Get started with Google BigQuery: Export to Google Sheets

In this tutorial, the output table is easily small enough to fit in Google Sheets, so let's export the data out of BigQuery and into Sheets.

There, we'll create chart a chart showing the pedestrian and bike traffic across the Brooklyn Bridge.

The data now looks like this in the new Sheet:

Yay! Back on familiar territory!

From here, you can do whatever you want with your data.

I chose to create a simple line chart to compare the daily foot and bike traffic across Brooklyn Bridge:

Congratulations!

You've completed your first end-to-end BigQuery + Google Sheets data analysis project.

Seriously, well done!

Get started with Google BigQuery: Resources

BigQuery Documentation

Explore the public datasets in BigQuery for query practise.

Google BigQuery: The Definitive Guide book is quite advanced for a beginner but extremely comprehensive.

The full code for this tutorial "Get started with Google BigQuery" is also available here on GitHub.