How to Migrate Marketo to BigQuery | 2 Easy Methods

Marketo is a well-known Marketing Automation tool that helps businesses automate and track marketing engagement, tasks, and workflow. With its clever automation, it can handle all of your marketing campaigns and generate quality leads for you. Marketo Reports also makes it simple to track your marketing campaign by offering a variety of visualizations in the form of reports and dashboards.

Due to its unequaled query performance, Google BigQuery has become an internationally trusted Cloud Data Warehouse & Analytics solution. On-demand scalability, low price, and capacity to handle variable workloads make it a trustworthy and secure Cloud platform for businesses of all sizes.

This article talks about the different methods you can use to transfer data from Marketo to BigQuery seamlessly. It also gives a brief introduction to Marketo and Google BigQuery before diving into the Marketo to BigQuery data transfer methods.

What is Marketo?

Marketo is one of the most widely used Software-as-a-Service systems for measuring and automating marketing processes. It offers a wide range of marketing-related services. Marketo is one of the market’s enterprise leaders, with solutions that prioritize scale and sophisticated workflows. It is built on a modular architecture, with each module available for purchase and usage separately. These modules can be bundled into bundles or used in tandem to give a complete marketing automation experience. E-mail marketing, lead management, customer-based marketing, revenue attribution, and account-based marketing are just a few of Marketo’s modules.

Marketo doesn’t have its own CRM (Customer Relationship Management) module, but it does link with popular CRMs like Salesforce, Microsoft Dynamics, and SAP. Marketo’s products make it possible to automate various stages of digital advertising across several channels, including email, online, mobile, and others. As a result, Leads may be sourced, cultivated, and tracked 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:

• Lead Nurturing: Marketo allows firms to segment their leads based on Target Industries, Marketing Personas, or the type of interactions that are occurring. It also allows you to build consumer relationships and increase conversion rates.
• SEO Tools: Search Engine Optimization is one of the most crucial parts of excellent marketing (SEO). Marketo combines a number of SEO tools in one location to assist you in raising your marketing standards.
• Account-Based Marketing (ABM): This technology enables businesses to construct a smart list of characters that characterize the personas they want to target, improve their website experience for those Leads, and engage them across different channels such as the web, email, and advertisements.
• A/B Testing: Marketo offers E-Mail Testing to help you figure out what kind of content to put in your emails and when to send them to boost conversion rates.

To know more about Marketo, visit this link.

When Should you use Marketo: Key Use Cases

You get an API to link Marketo with other platforms and services. Webhooks can let you do the following after a successful integration:

• Analyze and calculate the Return on Investment (ROI) of digital marketing.
• Increase the reach of media programs by using audience, media source, landing sites, and other factors.
• Improve lead quality by sharing insights on data challenges.
• Save time and resources by automating the uploading of tax returns.
• Creating assistance tickets in third-party customer support systems.  
• Sending consumers personalized messages. 
• Fetching data from an open-source database.

What is Google BigQuery?

Google BigQuery is a Cloud-based data warehouse that includes a Big Data Analytic Web Service that can handle petabytes of data. It’s made for analyzing big amounts of data. There are two aspects to it: Storage and Query Processing. The Colossus File System is utilized to store data while the Dremel Query Engine is used to process queries. These two components can be scaled independently and on-demand.

In 2010, Google announced Google BigQuery, a cloud-based data warehouse service. It’s built to handle petabytes of data and scales up and down as your business expands. Google engineers devised a system that divides storage and processing power. Because you can grow them individually without compromising performance, you have more query freedom.

Since there is no physical infrastructure to manage and maintain, as there is in traditional server rooms, you can focus all of your time and effort on important business goals. Using conventional SQL, you may inspect your data precisely and run complex queries from several users at the same time.

Cloud service providers have complete control over Google BigQuery. There are no resources that must be deployed, such as CDs or Virtual Machines. It’s designed for read-only data. Dremel and Google BigQuery both leverage columnar storage for quick data scanning, as well as a tree design for conducting ANSI SQL queries and aggregating results over massive computer clusters. Because of its rapid deployment cycle and on-demand pricing, Google BigQuery is also server-less and designed to be extremely scalable.

For further information about Google BigQuery, follow the Official Documentation.

Key Features of Google BigQuery 

Google BigQuery has improved over time and now includes some of the most user-friendly features:

• User-friendly: With just a few clicks, you can begin saving and analyzing your data in BigQuery. You won’t need to build clusters, determine storage size, or configure compression and encryption settings, so you can quickly set up your cloud data warehouse using an intuitive user interface with clear instructions at every step.
• Security: Google BigQuery allows administrators to set access rights for data by groups and individuals. Access to certain rows of a dataset can likewise be restricted using row-level security. Data is encrypted both before and after it is written on disc, as well as during transmission. It also allows you to manage the encryption keys for your files.
• Google Ecosystem: Google BigQuery benefits from Google’s easy and seamless integration with a variety of Google Ecosystem products. You may easily link to tools like Google Sheets and Google Data Studio for additional study.
• Storage Scaling on Demand: With ever-increasing data demands, you can rest assured that it will scale as needed. It’s built on Google’s Colossus (Global Storage System) and saves data in a columnar format, allowing users to work directly on compressed data without needing to decompress files on the fly.
• Real-time Data Transfers and Speedier Analytics: Google BigQuery distributes any number of resources optimally to give the best performance and results, allowing you to generate business reports as needed.
• Google BigQuery ML: You may construct and develop data models with machine learning capabilities using standard SQL commands. This eliminates the requirement for technical expertise in Machine Learning and allows your data analysts to immediately analyze ML models.
• Optimization Tools: Segmentation and clustering tools in Google BigQuery might help you receive faster responses to your queries. You can also change the default datasets and table expiration settings for better storage costs and utilization.

When Should you use Google BigQuery: Key Use Cases

You can use Google BigQuery Data Warehouse in the following cases:

• When you have queries that take more than five seconds to perform in a relational database, use it. BigQuery is meant for complex analytical queries, therefore running queries that do simple aggregation or filtering on it is worthless. BigQuery is made for “heavy” queries or queries that demand a lot of data. BigQuery is more likely to give better performance as the dataset grows larger.
• BigQuery is appropriate for scenarios when data does not change regularly and you want to utilize it since it has a built-in cache. It means that if you run the same query and the data in the tables haven’t changed (updated), BigQuery will use the cached results instead of rerunning it. BigQuery also does not charge for cached queries.
• BigQuery can assist you in reducing the load on your relational database. Analytical queries are “heavy,” and applying them in a relational database too frequently can cause issues. As a result, you might need to think about server scaling in the future. You can, however, use BigQuery to move these running queries to a third-party service, so they don’t affect your core Relational Database.

Methods to Connect Marketo to BigQuery  

Method 1: Connect Marketo to BigQuery via CSV Files/APIs 

Step 1: Exporting Data from Marketo

Exporting data from Marketo is the first step in Marketo to BigQuery data transfer. For users that want to export data programmatically, Marketo offers two types of REST APIs. Marketo personal records and associated object types such as Opportunities and Companies can be retrieved via Lead Database APIs. Asset APIs make marketing collateral and workflow records available to the public.

Visit the Admin panel and select LaunchPoint to utilize the APIs. Get an authorization token by creating a new service. Then select the API that corresponds to the data you require. For example, you could use Receive /rest/v1/opportunities.json to GET a list of opportunities. To customize the data Marketo returns, you can add a half-dozen alternative parameters.

Step 2: Sampling and Preparing Marketo Data

Marketo’s data is returned in JSON format. An example of data returned by an opportunities query is as follows:

{  
   "requestId":"e42b#14272d07d78",
   "success":true,
   "result":[  
      {  
         "seq":0,
         "marketoGUID":"dff23271-f996-47d7-984f-f2676861b5fa ",
         "externalOpportunityId":"19UYA31581L000000",
         "name":"Chairs",
         "description":"Chairs",
         "amount":"1604.47",
         "source":"Inbound Sales Call/Email"
      },
      {  
         "seq":1,
         "marketoGUID":"dff23271-f996-47d7-984f-f2676861b5fc ",
         "externalOpportunityId":"29UYA31581L000000",
         "name":"Big Dog Day Care-Phase12",
         "description":"Big Dog Day Care-Phase12",
         "amount":"1604.47",
         "source":"Email"
      }
   ]
}

You’ll need to construct a schema for your data tables if you don’t already have one for storing the data you obtain. Then, for each value in the response, you must identify a predefined datatype (INTEGER, DATETIME, etc.) and create a table to receive it. Marketo’s documentation should list the fields and datatypes available by each endpoint.

The fact that the records fetched from the source may not always be “flat” – some of the objects may really be lists – further complicates things. This means you’ll almost certainly need to create more tables to account for the variable cardinality of each record.

Step 3: Loading Data into Marketo

Google has a page that explains how to load data into BigQuery. To upload data to your datasets and establish schema and data type metadata, use the bq command-line tool, particularly the bq load command. The Quickstart guide for bq can teach you how to use it. Iterate through the process until you’ve loaded all of your tables into BigQuery.

Step 4: Maintaining Marketo Data

You might have transferred data from Marketo to BigQuery but it’s not a good idea to duplicate all of your data every time your records are updated. This would be a terribly slow and resource-intensive procedure.

Instead, select important fields that your script may use to save its progress through the data and return to them as it searches for updated data. For this, auto-incrementing fields like updated at and created at are ideal. You can set up your script as a cron job or continuous loop to acquire fresh data as it occurs in Marketo once you’ve added this functionality.

And, as with any code, you must maintain it once you’ve written it. You may need to change the script if Marketo changes its API or if the API sends a field with a datatype your code doesn’t understand. You will have to if your users require somewhat different information.

Limitations of manually connecting Marketo to BigQuery

The following are some of the drawbacks of manually connecting Marketo to BigQuery:

• Creating Pipeline: Building an in-house data pipeline necessitates a great deal of skill, time, and people, as well as a high risk of error.
• Time-Consuming: Building a data pipeline in-house takes a lot of time, effort, and labor, and there’s a lot of room for error.
• Hard Coding: Analysts must build code and manage infrastructure, yet they cannot get data within hours due to hard coding.
• Unreliable Application: You may never know whether or not a third-party application is trustworthy.

Method 2: Replicate Marketo to BigQuery using Hevo’s No-code Data Pipeline

Hevo helps you directly transfer data from various sources like Marketo to BigQuery Database, Business Intelligence tools, Data Warehouses, or a destination of your choice in a completely hassle-free & automated manner. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

Sign up here for a 14-day free trial!

The following steps can be implemented to implement Marketo to BigQuery using Hevo:

• Configure Source: Connect Hevo Data with Marketo providing a unique name for your Pipeline, along with details about your Data Source.

• Integrate Data: Establish a connection to Google BigQuery by providing information about your BigQuery Dataset and its credentials such as authorized account, destination name, dataset id, and project id. 

Here are more reasons to try Hevo:

• Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
• Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
• Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.
• Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
• Scalable Infrastructure: Hevo has in-built integrations for 100+ sources that can help you scale your data infrastructure as required.
• Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within Data Pipelines.
• Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

Migrating Data from Marketo to BigQuery: Best Practices to Follow

Below are the best practices to follow while migrating Data from Marketo to BigQuery:

• Apply the Principle of Least Privilege by granting API users only the permissions and workspaces needed to fulfill their assigned tasks. You’re effectively allowing the service using that API user to change/read your Marketo instance’s data in more ways than you initially planned by granting them more access than they need.
• Use specialized API users and API services for different integrations to enable fine-grained control/access over permissions and to aid in the auditing of API usage over time.
• Make concurrent API calls only if your use case absolutely requires it. Concurrent calls from a single service/integration could rapidly exceed the concurrency limit of 10 calls shared by all integrations.
• Nested and repeated data is one of the most significant ETL recommended practices for Google BigQuery. When the data is denormalized, Google BigQuery performs best. Denormalize the data instead of retaining relationships and take advantage of nested and repeated fields. Avro, Parquet, ORC, and JSON (newline delimited) formats support nested and repeated fields. STRUCT is the type that can be used to represent a nested object, while ARRAY is the type that can be used to represent a repeated value.
• Google BigQuery data is encrypted by default, and GCP manages the keys. Customers can also use the Google KMS service to handle keys.

Conclusion

In this article, you got a glimpse of how to connect Marketo to BigQuery after a brief introduction to the salient features, and use cases. The method talked over in this article is using API Migration. The process can be a bit difficult for beginners. Moreover, you will have to update the data each and every time it is updated and this is where Hevo saves the day!

Visit our Website to Explore Hevo

Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources such as Marketo and a wide variety of Desired Destinations such as Google BigQuery, with a few clicks. Hevo Data with its strong integration with 100+ sources (including 40+ free sources) allows you to not only export data from your desired data sources & load it to the destination of your choice, but also transform & enrich your data to make it analysis-ready so that you can focus on your key business needs and perform insightful analysis using BI tools.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.

Share your experience of learning about Marketo to BigQuery! Let us know in the comments section below!