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Effectively managing and analyzing data is key for businesses today, and BigQuery offers a solid solution for large-scale analytics.
Whether you're a Data Engineer or in Business Intelligence, understanding how to transfer data into BigQuery can improve your analytics workflow, utilizing data more effectively using SQL and therefore - the corporate culture of decision-making.
In this guide, we'll explore different ways to load data into BigQuery, covering both its built-in features and third-party tools.
By the end of this article, you'll have a clearer idea of how to make your data collection process more efficient and fully leverage BigQuery.
Whether you're working at a startup or a large enterprise, BigQuery's features ensure smooth, efficient data processing.
BigQuery handles massive datasets, making it suitable for organizations of any size. Whether you're a startup or a global company, it easily scales to meet growing data needs.
Optimized for analytics, BigQuery provides fast, reliable storage for structured, semi-structured, and unstructured data, supporting formats like JSON, Avro, and Parquet.
BigQuery integrates smoothly with visualization services like Looker Studio, Power BI or Google Sheets creating a connected data ecosystem and mankind data accessible by non-technical stakeholders.
BigQuery offers encryption and identity management, ensuring security and meeting regulatory standards like GDPR, PPDA, and HIPAA.
Google BigQuery is popular among businesses for its seamless integration with advertising services, analytics systems, CRM tools, and more.
It helps businesses automate tasks and respond quickly to market changes.
Now, let’s explore the different ways to collect data in BigQuery:
The BigQuery Data Transfer Service automates data transfers from popular sources like Google Ads, YouTube, and external platforms like Facebook Ads directly into BigQuery. This service allows users to create, configure, and monitor data transfers without manual intervention, ensuring timely and consistent updates.
The bq command-line tool manages data transfers into BigQuery, providing a more hands-on, flexible option than the web interface. It allows users to create, configure, and monitor data transfers through terminal commands, offering greater control over the data transfer process and automation options for advanced users.
Third-party paid tools offer advanced data transfer solutions for BigQuery, allowing users to connect with a wide range of platforms beyond Google’s ecosystem. These tools provide no-code options, pre-configured connectors, and support for custom schemas, making it easier to integrate and manage data from sources like Facebook Ads or CRM systems.
Custom BigQuery connectors enable smooth integration of external data sources into BigQuery, automating data ingestion without manual ETL processes.
These connectors act as direct bridges for data transfer, making analytics more efficient and reducing errors.
Businesses can tailor data workflows by building custom connectors, ensuring scalability, consistency, and accurate data flow for advanced analysis.
In this article, we’ll show you the ways of collecting data from popular sources by type of data: web analytics, ads platforms, spreadsheets, etc.
Collecting web analytics data in BigQuery is essential for in-depth insights and reporting.
For example, Google Analytics 4 (GA4) allows you to transfer event data into BigQuery automatically. Let’s take a closer look at both methods for collecting web analytics data into BigQuery.
Exporting data from Google Analytics 4 (GA4) to BigQuery allows you to analyze user behavior and site metrics more flexibly and deeply. Connecting GA4 to BigQuery will enable you to access raw event-level data for more advanced analysis.
Here’s a step-by-step guide on how to export GA4 data to BigQuery:
Step 1: Access Google Analytics Admin
Open your GA4 account and navigate to the Admin section at the page's bottom left. The Admin section manages all the property settings for your Google Analytics account.
Step 2: Select "BigQuery Linking"
Under the Property column, click on BigQuery Linking to begin setting up the connection between GA4 and BigQuery. This option lets you send raw event data directly from your GA4 property to BigQuery.
Step 3: Create a New Link
Click the Link button to create a new connection between your GA4 property and a BigQuery project. This step initiates the process of exporting data.
Step 4: Choose a BigQuery Project
Select the BigQuery project where you want to send your GA4 data. If you don't have a project, you must create one in Google Cloud Console.
Don’t forget to mark the checkbox next to the BigQuery project you want to link to your GA4 property to confirm your selection. Then click on “Confirm”.
Step 5: Select Preferred Data Location
After choosing your BigQuery project, select the preferred data location where your analytics data will be stored. It's recommended to choose a location that matches your business region to ensure compliance.
Step 6: Choose Data Streams
Next, select the data streams you want to export from GA4 to BigQuery. Depending on your needs, you can choose specific streams, such as website or app data.
Step 7: Set Up Frequency
In this step, choose the export frequency. Depending on your reporting needs, you can select between daily exports for regular data updates streaming exports for near real-time data analysis.
Step 8: Submit
After configuring all settings, click Submit to finalize the setup. This will initiate the linking process between GA4 and BigQuery.
Step 9: Linking Complete
Once submitted, the connection between your GA4 property and BigQuery is established, and data will start transferring based on the schedule you’ve set.
OWOX Website Tracker offers an effective solution for accurate customer acquisition reporting using server-side first-party cookies collected into BigQuery in real-time.
This method extends cookie lifespan to 365 days even for the browsers like Safari, ensuring more accurate returning visitor tracking.
Unlike traditional client-side cookies, OWOX BI’s solution requires no engineering resources and offers an out-of-the-box setup, enhancing campaign accuracy by over 30%.
For a better understanding, let's compare GA4 vs. OWOX BI Sreaming.
Cookie Type
To gather advertising data from platforms like Google Ads, Facebook Ads, or LinkedIn into BigQuery, you can use the BigQuery Data Transfer Service or third-party tools like OWOX BI Pipelines.
The BigQuery Data Transfer Service makes it easy to import data from various sources directly into your BigQuery tables. It works with Google services like Google Ads, YouTube, and Google Play, as well as external platforms such as Amazon S3 and Teradata. Transfers are automatically scheduled, ensuring your data is consistently updated without any manual effort.
A standout feature is the ability to backfill data so that you can recover any lost information during outages. You can manage the service through the:
This gives you flexibility in handling data imports. The service lets you customize transfer schedules or start manual transfers when needed. With its integration and automation, the BigQuery Data Transfer Service helps businesses keep their datasets accurate and up-to-date for real-time analysis and reporting.
Transferring Google Ads data to BigQuery allows merging campaign performance data with GA4 event data, enabling an overview of customer behavior and ROI.
Steps to Set Up Google Ads Data Transfer to BigQuery:
Step 1: Create a Dataset in BigQuery
Step 2: Configure Data Transfer
Step 3: Backfill Data Transfer
Step 4: First QueryRun your first query by replacing in the sample query provided to analyze metrics like impressions, interactions, and cost for your Google Ads campaigns.
select
c.customer_id,
c.campaign_name,
c.campaign_status,
sum(cs.metrics_impressions) as impressions,
sum(cs.metrics_interactions) as interactions,
(sum(cs.metrics_cost_micros) / 1000000) as cost
from
`google_ads.ads_Campaign_` c
left join
`google_ads.ads_CampaignBasicStats_` cs
on
(c.campaign_id = cs.campaign_id
and cs._data_date between
date_add(current_date(), interval -31 day) and date_add(current_date(), interval -1 day))
where
c._data_date = c._latest_date
group by
1, 2, 3
order by
impressions descThis query retrieves Google Ads campaign performance data from the last 31 days. It calculates total impressions, interactions, and cost for each campaign, joining campaign details with basic campaign statistics
Transferring Facebook Ads data to BigQuery allows businesses to merge ad performance metrics with other datasets.
There are three key methods for this transfer: manual data uploads, using API-based connections, or opting for no-code automated tools for seamless, ongoing transfers.
1. Manual Transfer Steps:
2. Facebook Ads API Transfer Steps:
Here's a basic example to demonstrate part of the workflow. This assumes that you have the google-cloud-bigquery and requests libraries installed in your environment.
from google.cloud import bigquery
import requests
# Initialize BigQuery client
client = bigquery.Client()
# Facebook Ads API credentials and endpoint setup
fb_access_token = 'YOUR_FACEBOOK_ACCESS_TOKEN'
ad_account_id = 'YOUR_AD_ACCOUNT_ID'
fb_ads_endpoint = f'https://graph.facebook.com/v12.0/{ad_account_id}/insights'
# Parameters for the API call
params = {
'fields': 'impressions,clicks,spend',
'access_token': fb_access_token
}
# Fetch data from Facebook Ads API
response = requests.get(fb_ads_endpoint, params=params)
ads_data = response.json()['data'] # Ensure this matches the structure of your expected API response
# Define the BigQuery dataset and table IDs
dataset_id = 'YOUR_DATASET_ID'
table_id = 'YOUR_TABLE_ID'
# Transform ads_data as needed to match your BigQuery table schema
# Assuming ads_data is a list of dictionaries that match your BigQuery table schema
# Insert data into BigQuery
errors = client.insert_rows_json(f'{dataset_id}.{table_id}', ads_data)
if errors == []:
print("New rows have been added.")
else:
print(f"Errors occurred: {errors}")3. Automated Transfer (No-code integration):
An automated or no-code integration allows businesses to transfer Facebook Ads data to BigQuery using third-party tools, eliminating the need for coding skills.
OWOX BI Pipelines offer a seamless, no-code solution for transferring Facebook Ads data to BigQuery. With easy setup and automated data updates, OWOX BI ensures real-time insights without technical expertise.
The BigQuery Data Transfer Service also automates data transfers from other Google products like Display & Video 360 (DV360), Search Ads 360, and YouTube.
Steps for Setting Up Data Transfer for DV360:
Your DV360 data will now be automatically transferred to BigQuery on a scheduled basis.
OWOX BI Pipelines simplifies collecting and transferring data from multiple sources, including advertising platforms, CRMs, and web analytics, into BigQuery.
Benefits of OWOX BI Pipelines:
Steps for Setting Up OWOX BI Pipelines:
Connecting Google Ads to BigQuery using the OWOX BI Pipeline allows the integration of advertising data from multiple sources for comprehensive analysis. This enables businesses to store, query, and analyze large volumes of ad data in real-time, facilitating data-driven decisions.
💡If you want to dive deeper about connecting Google Ads Data to BigQuery, read this article on Google Ads to BigQuery.
With OWOX BI Pipelines, you can easily upload Facebook Ads data to BigQuery, linking advertising costs with user activities, website data, and CRM purchase orders. This integration gives you a complete view of ad performance.
💡 You can learn more about connecting Facebook Ads data to BigQuery in this article.
OWOX BI allows you to automatically import LinkedIn Ads data into Google BigQuery, eliminating the need for manual uploads. It collects daily campaign data, including costs, impressions, and clicks, while converting it into your default currency. This integration helps businesses streamline reporting, optimize ad performance, and ensure data accuracy.
💡 You can know more about connecting Facebook Ads data to BigQuery in this article.
OWOX BI streamlines transferring Microsoft Ads(Bing ads) data to BigQuery through an automated pipeline, removing the need for technical expertise. Users receive clean, ready-to-use datasets that update automatically, enabling them to focus on analysis and insights without manual data processing, ultimately improving decision-making efficiency.
💡 You can learn more about connecting Facebook Ads data to BigQuery in this article.
Moving spreadsheet data to BigQuery enables real-time insights and more efficient, scalable data analysis. Here are a few ways to achieve this.
Google Sheets is a simple, convenient tool for small teams and enterprises to perform analysis, build reports, and share insights. However, as data grows and comes from multiple sources, spreadsheets become less effective for storage. At that stage, sending data from Google Sheets to BigQuery can help streamline data processes and handle larger volumes efficiently.
Below are three effective methods that allow for data integration.
Option #1: Upload Sheets Data to BigQuery with OWOX BI Reports Extension
Using the OWOX BI Reports Extension, you can easily upload Google Sheets data to BigQuery. This no-code solution simplifies the process, allowing you to map columns, define data types, and automate data transfers for real-time analysis and reporting.
Option #2: Transfer Data from Google Sheets using the BigQuery Interface
Using the BigQuery web interface allows you to upload Google Sheets data directly into a new or existing table.
Step 1: Open the BigQuery Console and create a new dataset.
Step 2: In the dataset, select "Create Table" and choose "Drive" as the data source.
Step 3: Enter the Google Sheets URL and configure the schema before uploading.
Step 4: Configure the schema before uploading
Option #3: Connect to BigQuery from Google Sheets With Connected Sheets
Connected Sheets is Google’s native tool for quickly uploading and working with BigQuery data directly from Sheets, with no code needed.
Step 1: Open the Spreadsheet
Step 2: Access the Data Menu
Step 3: Get Connected
Step 4: Select Google Cloud Project
Step 5: Choose a Dataset
Step 6: Select a Table or View
Step 7: Create Connected Charts and Pivot Tables
Once connected, you can build charts, pivot tables, and functions that either work with the entire dataset or extract subsets of data directly into Google Sheets.
Uploading CSV files to BigQuery allows businesses to efficiently manage and analyze large datasets that would otherwise be limited by traditional spreadsheet tools. Using BigQuery, you can quickly upload CSV files and leverage powerful features like fast query execution, machine learning, and geographic data analysis.
You can load CSV data into BigQuery using three main methods:
Each method ensures efficient data integration, helping you maximize BigQuery’s advanced analytics capabilities.
Integrating business and finance data into BigQuery streamlines analysis and reporting. By utilizing custom connectors or third-party tools, you can automate data imports from various platforms, ensuring comprehensive insights.
Custom connectors are essential for automating and streamlining data transfers from unique sources, ensuring real-time access to critical information.
By building custom connectors, organizations can extend BigQuery's capabilities to suit their specific data needs.
Process of Setting Up a Custom Connector:
Example: Custom Connector in Python
This example illustrates how to create a Python-based custom connector to fetch data from an API, transform it, and load it into BigQuery.
# Import necessary libraries
import requests
from google.cloud import bigquery
from google.oauth2 import service_account
# Define your credentials and project details
credentials = service_account.Credentials.from_service_account_file('path/to/key.json')
bq_client = bigquery.Client(credentials=credentials, project='your-project-id')
# Fetch data from the external API
api_url = 'https://your.api/source'
data = requests.get(api_url).json()
# Optional: Transform your data as needed
transformed_data = transform_function(data)
# Load data into BigQuery
dataset_id = 'your_dataset'
table_id = 'your_table'
table_ref = bq_client.dataset(dataset_id).table(table_id)
job = bq_client.load_table_from_json(transformed_data, table_ref)
job.result() # Wait for the job to completeIt demonstrates the basic steps of authenticating with BigQuery, fetching data from an external source, and optionally transforming the data before uploading it.
Third-party data transfers in BigQuery Data Transfer Service allow businesses to automate the recurring loading of data from external sources such as Salesforce CRM, Adobe Analytics, and Facebook Ads.
Using third-party tools simplifies the extraction, transformation, and loading (ETL) process, ensuring timely and accurate data ingestion across multiple platforms.
Example: Automated Data Transfer from Facebook Ads to BigQuery with OWOX BI
Using OWOX BI Pipelines, businesses can automate the transfer of Facebook Ads data into BigQuery. OWOX BI simplifies the ETL process, automatically extracting ad performance data and loading it directly into BigQuery for real-time analysis.
This automation allows businesses to focus on deriving insights and optimizing campaigns rather than managing data transfers.
Getting the data in BigQuery is just the beginning. Now, you need to take it further by turning it into clear, actionable reports. With simple, intuitive tools, turning your data into clear, actionable decisions has never been smoother.
With OWOX SQL Copilot, you can write up to 50 times faster SQL queries. It integrates directly into BigQuery, allowing users to streamline complex queries without needing advanced SQL skills.
Best of all, it's completely free, boosting productivity and accelerating data-driven insights.
Google Sheets can act as a front-end interface for BigQuery, allowing non-technical users to interact with data. With the OWOX Reports Extension for Google Sheets, you can effortlessly visualize BigQuery data directly in Google Sheets. This integration simplifies data access, enabling teams to analyze and report on corporate data, making data-driven decisions more efficient.
![[GA4] BigQuery Export: How to Connect Step-by-Step](https://cdn.prod.website-files.com/676a9690ef4ec151a69571ff/67aca1d1185b30f68471d7fc_Pipeline%20updates-min.avif)
