How to Upload a Google Sheets to Big Query

Our Sheets connector for BigQuery, that allows you to button information up to BigQuery or query information technology dorsum downward into Sheets, is now live!

Grab the 'Sheets + BigQuery connector' workbook through the link below.

• Brand a re-create of the Sheets workbook

The connector is 100% free, we're not asking for emails or money. But…I'm hoping you'll subscribe to my YouTube Channel for time to come updates.

• Subscribe to my YouTube Channel

Let's get into it!

PS – If you lot're just getting started with BigQuery, you lot may desire to pick up our free BigQuery class to advance your progress.

If y'all're managing 1 website or marketing campaign, Google Sheets is perfect for analysis.

You can whip up a Sheets template to pull your Google Analytics, Facebook Ads and Adwords data into ane place.

If you lot're working for a startup or e-commerce business, this is likely all y'all'll ever need.

Only if yous're running a digital agency, where you lot may take 5, 10, xx, l or even 100 clients at a time – yous pretty chop-chop reach the end of the spreadsheet.

You've reached the end of the spreadsheet when…

You're using a template named "Ad Entrada Dashboard (NEW NEW LATEST)."

Questions similar "where'd that Sheet go?" starting time to fly around the office.

And when y'all do, it's time to put our adult pants on, and move your analytics to a grown-upward database like Google BigQuery.

Why move from Sheets to BigQuery

The impact of pushing information up from Sheets into a database is hard to put into words…

You get to keep your training wheels on (by *seeing* your data in Sheets), only execute sweet moves past borer into the power of SQL.

If you lot're working in a team, this has two master effects:

i. Exercise improve work

Every team wants to exist data-driven, but y'all can't really do that across an entire digital agency without a information…er…base.

Measuring ROI across an unabridged 30-person squad simply tin can't be done from Google Sheets, never listen slicing information beyond clients, ad platforms, campaigns, and landing pages, etc.

Analyzing your entire set up of results allows yous to pick up on otherwise-hidden patterns in functioning – patterns that hold the key to your growth as a squad or agency.

two. Do less work

Wrangling information for reporting is, without question, the least favorite job of every digital marketing squad.

Instead of spreading that pain out across unlike members of a team, information pipelining allows you to consolidate information technology in ane person who'due south actually trained and equipped to handle it.

Time to burn down your squad from piece of work they hate, and free them upward to do work they love.

Bridging Google Sheets and BigQuery

At CIFL, we're helping digital agencies make this leap, with a service called the Agency Information Pipeline.

Simply if you're looking to prepare your own BigQuery analytics warehouse, you lot'll first need to take a long look in the mirror.

Keep looking.

OK, now we're prepare.

Now let'southward employ that same level of focus to three aspects of your work with data, which you'll demand to define *very conspicuously* when working in BigQuery.

What data do you actually need?

Moving into BigQuery is a neat opportunity to trim the fatty.

Since you need to define your database tables very specifically, there'due south no reason to upload data that you know yous'll never use.

So for each unique data source (Google Analytics, FB Ads, etc), now'due south the time to exercise theFine art of Tidying Up, and ask yourself "Does this bring me joy?".

And then for each data source, you can inquire yourself the aforementioned thing nearly the private metrics they offer, since you'll demand to ascertain exactly which columns yous'll need down the line (spend, clicks, conversions, etc).

If a data source or metric isn't driving results for your team, time to donate information technology to the digital Salvation Ground forces.

Can you automate pulling that information?

One time you've decided *which* data you'll need in your BigQuery warehouse, y'all can dig into how you'll get information technology.

If you're already pulling data into Sheets usingBlockspring or Supermetrics, then you're all set.

But if you're starting from scratch, and then you'll want to accept your list of data sources, and compare it to the connectors offered bySupermetrics andBlockspring.

For most digital agencies and marketers, Supermetrics ability to pull data on multiple accounts in the aforementioned data query will make it the best fit.

Once you've got information pulled into Sheets, the tool at the bottom of the post volition help you upload it to BigQuery.

Notation: Information pipelining (ETL) tools like Sew, Segment, and Funnel.io are besides helpful for getting information into BigQuery, but from our experience none of them (yet) comprehend all of the data sources you're probable to demand.

What can you do with your data?

At present that you've got data properly trimmed and uploaded to BigQuery, you're ready for the big testify.

You've finally got the information-modeling power of SQL at your fingertips – what volition yous exercise with it?

There are a few basic BigQuery information modeling patterns we've found helpful for digital marketing data.

Dive deeper into these in our full BigQuery SQL tutorial.

1. Joining tables to build a full funnel

Say you're running ad campaigns on LinkedIn for a B2B production.

LinkedIn will manifestly requite you spend, click and conversion data – but what about user engagement on your site, or ultimately sales in your bookkeeping tool?

Using BigQuery makes it easy to tie those information sources together usingJOINs, which can be super slow in Sheets.

2. Detecting anomalies

Picking out hot (or cold) landing pages and ad campaigns is super helpful in deciding where to spend your time.

But looking at this kind of momentum is about incommunicable in Sheets (due to adding time constraints).  In BigQuery, it becomes easy.

You tin can use'window' or analytic functions to summate moving averages across your data, and measure how your daily data compares to those averages.

3. Quality control

Are your FB Ads campaigns no longer existence UTM tagged properly?

Did a landing page terminate picking up conversions all of a sudden?

These kinds of unforced analytics tracking errors are unfortunately common, and totally preventable.

That same anomaly detection patterns in a higher place can be turned around to detect QC problems in your data.

How we use this at CIFL

Our internal data pipeline at CIFL is pretty simple, since in that location are three master channels that drive traffic to the blog and courses:

• Organic search
• YouTube
• Facebook Ads

The key for us is viewing conversion data across all of our channels in the same charts – then that we can run into which content (blog posts, videos) are really driving CIFL forward.

Nosotros use the Sheets tool (aforementioned version you lot can grab beneath) to button data from four sources to BigQuery: Google Analytics, Google Search Console, Youtube and FB Ads.

At the end of a lot of information modeling (via BigQuery views), nosotros terminate up with a consolidated dashboard that looks like so:

Update: based on pop demand, we also added the ability to pull data _back into _Sheets from BigQuery to the template below by writing SQL queries.

Ready to scale up to BigQuery?

Grab the Sheets <> BigQuery connector from our Getting Started with BigQuery course, where y'all'll also notice tons of other goodies.

If you need a manus building out your BigQuery data pipeline, always experience gratis to drop us a line.

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Source: https://codingisforlosers.com/google-sheets-bigquery-connector/

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