How to Sync Data from BigQuery to Marketo? | 2 Easy Methods

The exponential amount of data production in any modern corporation from various SaaS applications, Marketing Channels, and so on has forced companies to transition from on-premise databases to Cloud-Based Data Warehouses. Cloud Data Warehouses have become the Single Source of Truth (SSOT) for practically every other enterprise in recent years. They not only store data for various departments, but they also do it in a consistent and chronological fashion. All of the data housed in data warehouses is critical for any organization’s decision-making and sustainability. Google BigQuery is one such well-known and commonly used Cloud-based Data Warehouse Application.

Marketo is a Marketing Automation System solution that allows customers to streamline and track the success of their marketing workflow initiatives. Marketo requires enterprises to provide specific data on their prospects and customers in order to use its services. This is where organizations using Google BigQuery as their data warehouse can connect BigQuery to Marketo.

In this article, you will gain information about the different methods of connecting Google BigQuery to Marketo. You will also receive a comprehensive understanding of Google BigQuery and Marketo, including their key features, as well as the need for and benefits of transferring data from Google BigQuery to Marketo.

Table of Contents

• Prerequisites
• What is Google BigQuery?
• What is Marketo?
• Why connect Google BigQuery to Marketo?
• Methods for setting up BigQuery to Marketo Connection
  • Method 1: Using CSV Files to Manually Connect Google BigQuery to Marketo 
  • Method 2: Sync Data from Google BigQuery to Marketo Connection using Hevo Activate
• Advantages of Connecting Google BigQuery to Marketo
• Conclusion

Prerequisites

• Fundamental understanding of Google BigQuery
• Fundamental understanding of Marketo
• Ensure that you have been granted the following permissions in BigQuery:
  • bigquery.tables.export permissions 
  • bigquery.jobs.create permissions 
  • storage.objects.create and storage.objects.delete permissions

What is Google BigQuery?

Google BigQuery is a Cloud-based Data Warehouse that provides a Big Data Analytic Web Service capable of handling petabytes of data. It is intended for massive data analysis. The BigQuery Warehouse Architecture is divided into two sections: storage and query processing. It processes queries with the Dremel Query Engine and stores them in the Colossus File System. The 2 components i.e, storage and query processing are independent and can be scaled separately and on-demand. BigQuery uses the Columnar Storage for quick data scanning, as well as a tree architecture for running ANSI SQL queries and aggregating results across enormous computer clusters. Furthermore, because of its quick deployment period and on-demand pricing, Google BigQuery is serverless and meant to be incredibly scalable.

The scalable, distributed analytical engine in BigQuery allows you to query a massive amount of data in seconds. Google  BigQuery is an “externalized version” of Google’s Dremel query service software. BigQuery may be used to store and analyze data, as well as to evaluate data from other sources.

For further information about Google Bigquery, you can follow the Official Documentation. 

Key Features of Google BigQuery

Google BigQuery has continuously evolved over the years and is offering some of the most intuitive features:

• User-friendly: With only a few clicks, you can save and analyze your data in BigQuery. You can rapidly set up your cloud data warehouse with an easy-to-use interface and basic instructions.
• On-Demand Storage Scaling: You can be assured that your warehouse will scale automatically as and when needed with your increasing data needs. BigQuery, which is based on Colossus (Google Global Storage System), can store data in a columnar format and operate directly on compressed data without having to decompress the files on the fly.
• Real-Time Analytics: With the help of real-time data transmission and rapid analytics, BigQuery will keep you up-to-date with everything. It allocates resources efficiently to deliver the optimum performance and results, allowing you to produce business reports as needed.
• Scalability: Google BigQuery delivers genuine scalability and consistent performance because of its massively parallel processing and secure storage engine.
• Data Ingestion Formats: Google BigQuery supports a wide range of data ingestion formats, including AVRO, CSV, JSON, and others.
• Parallel Processing: It uses a cloud-based parallel query processing engine to read data from thousands of discs at the same time.
• Secure: Data access permissions can be defined for groups and individuals by BigQuery administrators. Row-level security can also be used to limit access to individual rows of a dataset. Data is encrypted both before it is written to disc and while it is in transit. It also allows you to manage the encryption keys for your files.

For further information on Google BigQuery, you can check the official website here.

What is Marketo?

Marketo is a very well-known SaaS tool for monitoring and automating marketing activities. It offers a variety of marketing-related services. Marketo is a global leader in enterprise solutions that value scale and advanced processes. It is designed as a modular system, with each module sold and utilized independently. These modules can be used singly or in combination to give a full marketing automation experience. Email marketing, lead management, revenue attribution, customer-based marketing, account-based marketing, and more modules are available through Marketo.

Marketo does not have its own CRM (Customer Relationship Management) module; instead, it can communicate with major CRMs such as Salesforce, Microsoft Dynamics, and SAP. Marketo’s products enable the automation of various stages of digital advertising across several channels such as email, web, mobile, and others. Leads may therefore be sourced, nurtured, and monitored in real-time.

Key Features of Marketo

Marketo offers a variety of services that can help organizations and individuals generate income and improve user experience. Marketo has a number of significant characteristics, including:

• A/B Testing: Marketo provides services such as E-Mail Testing to assist you to identify what sort of content to include in your emails and when to send them in order to boost conversion rates.
• Engagement Engine: Using this tool, you can conduct research and get insights into your consumers’ behavior, as well as design industry-specific E-Mail Campaigns for them.
• Lead Nurturing: Marketo allows businesses to segment their leads based on Marketing Personas, Target Industries, or the type of interactions that are taking place. It also allows you to build customer connections and partnerships while increasing conversion rates.
• SEO Tools: Search Engine Optimization is a critical component of efficient marketing. Marketo combines a number of SEO tools in one location to assist you in boosting your marketing standards.
• Account-Based Marketing (ABM): This technology allows businesses to develop a smart list of characters that characterize the personas they want to target, improve their Leads’ experience on their website, and engage them across numerous channels such as adverts, online, and emails.

To know more about Marketo, you can visit the following link.

Why Connect Google BigQuery to Marketo?

BigQuery is a prominent Enterprise Data Warehousing & Analytics software for managing and analyzing data. It gives you a comprehensive view of all your customer data, allowing you to acquire relevant business insights.

Marketo is a marketing automation platform that leverages behavioral data and built-in intelligence to assist your team in engaging with prospects and customers more successfully. These automated marketing efforts will help businesses translate positive customer experiences into sales.

You may quickly sync all of your critical data linked to your potential leads by integrating Google BigQuery to Marketo. Moreover, you can ou y instantly connect your contacts with more precise information about your prospects and leads, consequently enhancing the success of your marketing initiatives. You can even run SQL queries rapidly in Google BigQuery by utilizing your leads’ data and sending the results to Marketo members’ data. This will not only save you time but will also boost your efficiency dramatically.

As Google BigQuery is your Single Source of Truth, all of your organization’s data will be saved in a single location. And you don’t have to establish new tables or databases every time you want to feed something to Marketo. Instead, you may perform queries on your aggregated data saved in Google BigQuery to filter out the information that is truly required. And you can load this data into Marketo.

Methods for Setting Up Connection from Google BigQuery to Marketo

The different methods for setting up a connection from BigQuery to Marketo are as follows:

• Method 1: Using CSV Files to Manually connect Google BigQuery to Marketo 
• Method 2: Sync Data from Google BigQuery to Marketo using Hevo Activate

Method 1: Using CSV Files to Manually connect Google BigQuery to Marketo

You cannot directly export data from Google BigQuery to Marketo. You first need to export data specific to your leads or customers from Google BigQuery as CSV files. You can then import the CSV files to Marketo.

The steps involved are as follows:

• Step 1: Exporting data from Google BigQuery in CSV Format
• Step 2: Importing CSV Files into Marketo

Step 1: Exporting data from Google BigQuery in CSV Format

There are 3 main methods to export data from Google Bigquery in CSV format.

• Method 1: Using Google Cloud Console
• Method 2: Using bq extract command in the bq command-line tool  
• Method 3: Submitting an Extract Job via Client Libraries (APIs)

Method 1: Using Google Cloud Console

You can go through the following steps to download CSV files from Google BigQuery using Google Cloud Console.

• Log in to your Google BigQuery account.
• Navigate to Google Cloud Console. 
• Go to the Explorer panel on the left side and select the desired table from your project.
• Go to the Details panel. Click on the “Export” option and select the “Export to Cloud Storage” option.

• An Export table to Google Cloud Storage dialog box appears on the screen. You can fill in the required parameters. Select the desired destination bucket, folder, or file for the data you want to export in CSV format.
• In the Export Format field, from the drop-down select “CSV” as the format.
• Select “None” in the Compression field.
• Click on the Export button to export the data from BigQuery in CSV format.

Method 2: Using bq extract command in the bq command-line tool  

In the bq command-line tool, you can use the bq extract command to export data from Google BigQuery in CSV format.

bq --location=location extract 
--destination_format format 
--compression compression_type 
--field_delimiter delimiter 
--print_header=boolean 
project_id:dataset.table 
gs://bucket/filename.ext

The above code snippet has the following parameters:

• Location: The geographical location of your BigQuery data. Suppose  you are in the United States, then your location is ‘US.’
• Destination Format: The file format required in Google Cloud Storage (GCS).
• Compression Type: The file format’s specified compression type.
• Delimiter: To denote the border between columns in CSV, the delimiters t and tab are utilized.
• Boolean: The default value is true, which allows header rows to be printed to the exported data.
• Project ID, Dataset, Table: These include information about the Table from which you are exporting data.
• Bucket, Filename.ext: The location of your file in GCS, as well as the filename in the appropriate format.

Method 3: Submitting an Extract Job via Client Libraries (APIs)

You can export data from Google BigQuery in CSV format using a variety of computer languages and environments, including C#, Go, Java, Node.js, PHP, Python, and Ruby. To begin, install the Client Libraries and then start writing queries. For example, you may use the Python code below to export a BigQuery table to CSV:

from google.cloud import bigquery
client = bigquery.Client()
bucket_name = 'bucket-one'
project = "project-one"
dataset_id = "one"
table_id = "onefile"

destination_uri = "gs://{}/{}".format(bucket_name, "onefile.csv")
dataset_ref = bigquery.DatasetReference(project, dataset_id)
table_ref = dataset_ref.table(table_id)

extract_job = client.extract_table(
    table_ref,
    destination_uri,
    # Location must match that of the source table.
    location="US",
)  # API request
extract_job.result()  # Waits for job to complete.

print(
    "Exported {}:{}.{} to {}".format(project, dataset_id, table_id, destination_uri)
)

For more details on how to submit an Extract Job using the API and the Client Libraries, click here.

Step 2: Importing CSV Files into Marketo

In the process of migrating data from Google BigQuery to Marketo, the steps involved in importing CSV files containing the information about your leads into Marketo are as follows:

• Login to your Marketo account.
• Next, click on the “Members” tab.

• Click on the “Import Members” option.
• In the Import List dialog box that appears, in the first step, select the CSV file from your local system and fill in all the details. Then, click on the “Next” button.

• In the next step, check the import preview, and match the data of different column lists to the corresponding Marketo Field data. You can also choose to ignore the fields that you don’t want to import by selecting the “Ignore” option in the drop-down menu. Then, click on the “Next” button.

• In the final step, select the appropriate Member Status. Then click on the “Import” button.

• After the successful import of the Members in CSV files, you can navigate to the “Members” tab to check if your list has been added.

Method 2: Sync Data from Google BigQuery to Marketo using Hevo Activate

Hevo Activate helps you directly transfer data from Google BigQuery, and various other sources to CRMs such as Marketo, various SaaS applications, and a lot more, in a completely hassle-free & automated manner. Hevo Activate is fully managed and entirely automates the process of not only importing data from your selected source but also enriching and converting the data into an analysis-ready format without the need to write a single line of code. It allows you to move data from Google BigQuery to Marketo for free.  Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

Hevo Activate covers all of your data preprocessing requirements, allowing you to focus on key business operations and get a better understanding of how to generate more leads, retain customers, and propel your company to new heights of profitability. It provides a reliable and consistent solution for real-time data management and always has analysis-ready data at the destination of your choice. Hevo Activate will streamline the process of connecting Google BigQuery to Marketo.

You can establish a connection from Google BigQuery to Marketo using Hevo Activate with the following steps:

• Step 1: Configure Google BigQuery as your Data Source
• Step 2: Configure Marketo as your Target Destination

Step 1: Configure BigQuery as your Data Source

• Navigate to the Asset Palette. Now, select the “ACTIVATE” option.
  • In the ACTIVATIONS Tab that appears, you can select the “+ CREATE ACTIVATION” button.

• The Select Warehouse page will appear on the screen. Scroll down and then click on the “+ ADD WAREHOUSE” button at the bottom.

• In the “Select Warehouse Type” page that appears, select “Google BigQuery” as your source data warehouse from the drop-down menu.
• Navigate to the Asset Palette. Then select the “DESTINATIONS” option.
  • Click on the “CREATE” button in the Destinations List View.
  • The Add Destination Page appears on the screen. Here, you can select “Google BigQuery” as the destination type.
• In the “Configure your Google BigQuery account” page that appears, you can select the type of account you want to use to connect to Google BigQuery.

• Here, you can select any of the 2 pathways. Either you can choose the “User Account” or “Service Account”.
• If you want to connect through a User Account, select the “User Account” option. Then, select the “+ADD GOOGLE BIGQUERY ACCOUNT” button.

• You can sign in as a user with BigQuery Admin and Storage Admin permissions. 
• Click on the “Allow” button to authorize Hevo to access your data.

• If you want to connect through a Service Account. Attach the Service Account Key in the given field. Click on the “Configure Google BigQuery Account” button. 

• On the “Configure your Google BigQuery Warehouse” page, you can specify the following details. The details include the Warehouse Name, Project ID, Dataset ID, and GCS Bucket.

• After filling the parameters, click on the “Test Connection” button to test connectivity with the Google BigQuery data warehouse.
• After the test is successful, click on the “SAVE WAREHOUSE” button. Now your BigQuery data warehouse is saved as a Source.

Step 2: Configure Marketo as your Target Destination

To set Marketo as your Target destination, you can follow the simple steps given below:

• Navigate to the Asset Palette. Now, you can click on the “Activate” button.
• Then, you can do any of the following:

1) Go to the Targets List View, then select the “TARGETS” button. Then, you can click on the “+ CREATE TARGET” button.

2) Go to the Activations List View, then select the “ACTIVATIONS” button. Then, you can click on the “+ CREATE ACTIVATION” button.

• The Select Warehouse page will appear on the screen. Select your Activate Warehouse i.e, Google BigQuery in this case, or click on the “+ ADD WAREHOUSE” button to add a new warehouse.
• The “Select a Target” page will appear on the screen. Now, you can click on the “+ ADD TARGET” button.

• On the “Select a Target Type” page, click on the “Marketo” tile.

• In the Configure your Marketo Target page, you can specify the required parameters.

• Finally, you can click on the “TEST & CONTINUE” button.

Check out what makes Hevo Activate amazing:

• Near Real-time Data Transfer: Hevo Activate, with its strong Integration with various sources, allows you to transfer data quickly & efficiently. This ensures efficient utilization of bandwidth on both ends.
• Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
• Secure: Hevo Activate has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
• Tremendous Connector Availability: Hevo Activate houses a large variety of connectors and lets you bring in data from numerous Data Warehouses and load it into Marketing & SaaS applications, such as Marketo, Salesforce, HubSpot, Zendesk, Intercom, etc. in an integrated and analysis-ready form.
• Simplicity: Using Hevo Activate is easy and intuitive, ensuring that your data is exported in just a few clicks.
• Completely Managed Platform: Hevo Activate is fully managed. You need not invest time and effort to maintain or monitor the infrastructure involved in executing codes.
• Live Support: The Hevo Activate team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

Sign up here for a 14-day free trial!

Advantages of connecting Google BigQuery to Marketo

Loading data from Google BigQuery to Marketo allows marketers to import data associated with users and marketing campaigns into Marketo.

Given below are some of the advantages of connecting Google BigQuery to Marketo: 

• Marketing teams can collect thorough information on their leads, prospects, and customers, allowing them to build targeted marketing campaigns for each category.
• Marketing teams would be able to provide more tailored and better consumer experiences.
• After connecting Google BigQuery to Marketo, marketing teams no longer have to rely primarily on data engineers to manually extract, convert, and load prospect information from Google BigQuery, saving time.
• Marketing and sales teams can gain a better understanding of their customers’ expectations and design their advertising efforts appropriately.

Conclusion

In this article, you have learned about Google BigQuery, and Marketo, their key features, and the need for syncing data from BigQuery to Marketo. You also learned how to manually connect BigQuery to Marketo using CSV files. But, this manual process of importing CSV files from BigQuery and exporting them to Marketo is a very inconsistent and time-consuming process. This is where you can use a fully-automated Reverse ETL tool like Hevo Activate to connect BigQuery to Marketo.

Analyzing data scattered across Databases can be challenging, and tricky sometimes. Wouldn’t it be easier to directly analyze data in your Business Applications with a 360-degree view available? Hevo Activate can help! It is a Reverse-ETL tool that is not only cost-efficient but also easy to learn

visit our website to explore hevo

Hevo Activate helps you sync data directly from a database or a data warehouse of your choice (like Google BigQuery) to Business Applications like Salesforce, HubSpot, Marketo, etc., in a fully automated and reliable fashion. Hevo Activate — a low/no-code platform — helps today’s Business & Operations teams become Data-Driven.

Now Sync Data into Business Applications from leading Cloud Data Warehouses like Snowflake, Redshift, BigQuery, and Relational Databases such as PostgreSQL in a hassle-free manner. Activate your data, yourself become, and empower your Business & Operations teams to become Data-Driven, and make Data-Informed decisions in a jiffy.

Want to take Hevo for a ride? sign up for a 14-day free trial and simplify your Data Integration process. Do check out the pricing details to understand which plan fulfills all your business needs.

Tell us about your experience of connecting Google BigQuery to Marketo! Share your thoughts with us in the comments section below.