Salesforce to BigQuery: 2 Easy Methods

Bringing your key sales, marketing and customer data from Salesforce to BigQuery is the right step towards building a robust analytics infrastructure. By merging this information with more data points available from various other data sources used by your business, you will be able to extract deep actionable insights that grow your business. Before we jump into the details, let us briefly understand each of these systems.

Introduction to Salesforce

Salesforce is one of the world’s most renowned customer relationship management platforms. Salesforce comes with a wide range of features that allow you to manage your key accounts and sales pipelines. While Salesforce does provide analytics within the software, many businesses would want to extract this data, combine it with data from other sources such as marketing, product, and more to get deeper insights on the customer. By bringing the CRM data into a modern data warehouse like BigQuery, this can be achieved.

Key Features of Salesforce

Salesforce is one of the most popular CRM in the current business scenario and it is due to its various features. Some of these key features are:

• Easy Setup: Unlike most CRMs, which usually take up to a year to completely get installed and deployed, Salesforce can be easily set up from scratch within few weeks only.
• Ease of Use: Businesses usually have to spend more time putting it to use and comparatively much lesser time in understanding how Salesforce works.
• Effective: Salesforce is convenient to use and can also be customized by businesses to meet their requirements. Due to this feature, users find the tool very beneficial.
• Account Planning: Salesforce provides you with enough data about each Lead that your Sales Team can customize their approach for every potential Lead. This will increase their chance of success and the customer will also get a personalized experience.
• Accessibility: Salesforce is a Cloud-based software, hence it is accessible from any remote location if you have an internet connection. Moreover, Salesforce has an application for mobile phones which makes it super convenient to use.

Introduction to Google BigQuery

Google BigQuery is a completely managed cloud data warehouse platform offered by Google. It is based on Google’s famous Dremel Engine. Since BigQuery is based on a serverless model, it provides you high level of abstraction. Since it is a completely managed warehouse, companies do not need to maintain any form of physical infrastructure and database administrators. BigQuery comes with a pay-as-you-go pricing model and allows to pay only for the queries run. is also very cost-effective as you only pay for the queries you run. These features together make BigQuery a very sought after data warehouse platform. You can read more about the key features of BigQuery here.

This blog covers two methods of loading data from Salesforce to Google BigQuery. The article also sheds light on the advantages/disadvantages of both approaches. This would give you enough pointers to evaluate them based on your use case and choose the right direction.

Methods to Connect Salesforce to BigQuery

There are several approaches to migrate Salesforce data to BigQuery. Salesforce bigquery connector is commonly integrated for the purpose of analyzing and visualizing Salesforce data in a BigQuery environment. Let us look at both the approaches to connect Salesforce to BigQuery in a little more detail:

Method 1: Move data from Salesforce to Google BigQuery using Hevo

Hevo, a No-code Data Pipeline, helps you directly transfer data from Salesforce and 150+ other data sources to Data Warehouses such as BigQuery, Databases, BI tools, or a destination of your choice in a completely hassle-free & automated manner.

Get Started with Hevo for free

Hevo Data takes care of all your data preprocessing needs and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. 

Hevo can integrate data from Salesforce to BigQuery in just 2 simple steps:

• Step 1: Authenticate and configure your Salesforce data source as shown in the below image. To learn more about this step, visit here.

Learn more about configuring Salesforce from our documentation page.

• To configure Salesforce as a source, perform the following steps;
  • Go to the Navigation Bar and click on the Pipelines.
  • In the Pipelines List View, click the + CREATE button.
  • Select Salesforce on the Select Source Type page.
  • Specify the necessary information in the Configure your Salesforce account page 
  • Click on the Continue button to complete the source setup and proceed to configuring data ingestion and setting up the destination.

• Step 2: Complete Salesforce to BigQuery Migration by providing information about your Google BigQuery destination such as the authorized Email Address, Project ID, etc.
• To configure Google BigQuery as your  Destination, follow the steps;
  • Go to the Navigation Bar, and click the Destinations button.
  • In the Destinations List View, click the + CREATE button.
  • On the Add Destination page, select Google BigQuery as the Destination type
  • Specify the necessary information in the Configure your Google BigQuery warehouse page 
  • Click on the Save & Continue buttons to complete the destination setup.

Learn more about configuring BigQuery as destination here.

Hevo’s visual interface gives you a hassle-free means to quickly and easily migrate from Salesforce to BigQuery and also for free. Without any coding, all your Salesforce data will be ready for analysis within minutes.

Advantages of using Hevo

• Wide Range of Connectors – Instantly connect and read data from 150+ sources including SaaS apps and databases, and precisely control pipeline schedules down to the minute.
• In-built Transformations – Format your data on the fly with Hevo’s preload transformations using either the drag-and-drop interface, or our nifty python interface. Generate analysis-ready data in your warehouse using Hevo’s Postload Transformation 
• Near Real-Time Replication – Get access to near real-time replication for all database sources with log-based replication. For SaaS applications, near real-time replication is subject to API limits.   
• Auto-Schema Management – Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo automatically maps source schema with destination warehouse so that you don’t face the pain of schema errors.
• Transparent Pricing – Say goodbye to complex and hidden pricing models. Hevo’s Transparent Pricing brings complete visibility to your ELT spend. Choose a plan based on your business needs. Stay in control with spend alerts and configurable credit limits for unforeseen spikes in the data flow.
• 24×7 Customer Support – With Hevo you get more than just a platform, you get a partner for your pipelines. Discover peace with round-the-clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day free trial.
  Security – Discover peace with end-to-end encryption and compliance with all major security certifications including HIPAA, GDPR, SOC-2.

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Method 2: Move Data from Salesforce to BigQuery using Custom Scripts

• The first step would be to decide what data you need to extract from the API and Salesforce has an abundance of APIs.
  • Salesforce REST APIs
  • Salesforce Bulk APIs
  • Salesforce SOAP APIs
  • Salesforce Streaming APIs
    You may want to use Salesforce’s streaming API, so your data is always current.
• Transform your data:
  Once you have extracted the data using one of the above approaches, you would need to do the following:
  • BigQuery supports loading data in CSV and JSON formats. If the API you use returns data in formats other than these (eg: XML), you would need to transform them before loading. 
  • You also need to make sure your data types are supported by BIgQuery. Use this link BigQuery data types to learn more about BigQuery data types.
• Upload the prepared data to Google Cloud Storage
• Load to BigQuery from your GCS bucket using BigQuery’s command-line tool or any cloud SDK.

Salesforce to BigQuery: Limitations in writing Custom Code

When writing and managing API scripts you need to have the resources for coding, code reviews, test deployments, and documentation.

Depending on the use cases developed by your organization, you may need to amend API scripts; change the schema in your data warehouse; and make sure data types in source and destination match. You will also need to have a system for data validation. This would help you be at peace that data is being moved reliably. Each of these steps can make a substantial investment of time and resources. In today’s work setting, there are very few places with ‘standby’ resources that can take up the slack when major projects need more attention.

In addition to the above, you would also need to:

1. Watch out for Salesforce API for changes
2. Monitor GCS/BigQuery for changes and outages
3. Retain skilled people to rewrite or update the code as needed

If all of this seems like a crushing workload you could look at alternatives like Hevo. Hevo frees you from inspecting data flows, examining data quality, and rewriting data-streaming APIs. Hevo gives you analysis-ready data so you can spend your time getting real-time business insights.

Use cases for migrating Salesforce to BigQuery

Organizations use Salesforce’s Data Cloud along with Google’s BigQuery and Vertex AI to enhance their customer experiences and tailor interactions with them.  Salesforce BigQuery Integration enables organizations to combine and analyze data from their Salesforce CRM system with the powerful data processing capabilities of BigQuery. Let’s understand some real-time use cases for migrating salesforce to bigquery.

• Retail: Retail businesses can integrate CRM data with non-CRM data such as real-time online activity and social media sentiment in BigQuery to help you understand the complete customer journey and subsequently when you implement customized AI models to forecast customer tendency. The outcome involves delivering highly personalized recommendations to customers through optimal channels like email, mobile apps, or social media.
• Healthcare Organizations: CRM data, including appointment history and patient feedback, can be integrated with non-CRM data, such as patient demographics and medical history in BigQuery. The outcome is the prediction of patients who are susceptible to readmission, allowing for the creation of personalized care plans. This proactive approach enhances medical outcomes through preemptive medical care.
• Financial institutions: Financial institutions have the capability to integrate CRM data encompassing a customer’s transaction history, credit score, and financial goals with non-CRM data such as market analysis and economic trends. By utilizing BigQuery, these institutions can forecast customers’ spending patterns, investment preferences, and financial goals. This valuable insight informs the provision of personalized banking services and offers tailored to individual customer needs.

Use cases for migrating Salesforce to BigQuery

Organizations use Salesforce’s Data Cloud along with Google’s BigQuery and Vertex AI to enhance their customer experiences and tailor interactions with them.  Salesforce BigQuery Integration enables organizations to combine and analyze data from their Salesforce CRM system with the powerful data processing capabilities of BigQuery. Let’s understand some real-time use cases for migrating salesforce to bigquery.

• Retail: Retail businesses can integrate CRM data with non-CRM data such as real-time online activity and social media sentiment in BigQuery to help you understand the complete customer journey and subsequently when you implement customized AI models to forecast customer tendency. The outcome involves delivering highly personalized recommendations to customers through optimal channels like email, mobile apps, or social media.
• Healthcare Organizations: CRM data, including appointment history and patient feedback, can be integrated with non-CRM data, such as patient demographics and medical history in BigQuery. The outcome is the prediction of patients who are susceptible to readmission, allowing for the creation of personalized care plans. This proactive approach enhances medical outcomes through preemptive medical care.
• Financial institutions: Financial institutions have the capability to integrate CRM data encompassing a customer’s transaction history, credit score, and financial goals with non-CRM data such as market analysis and economic trends. By utilizing BigQuery, these institutions can forecast customers’ spending patterns, investment preferences, and financial goals. This valuable insight informs the provision of personalized banking services and offers tailored to individual customer needs.

Conclusion

The blog talks about the two methods you can use to move data from Salesforce to BigQuery in a seamless fashion.

The idea of custom coding with its implicit control over the entire data-transfer process is always attractive. However, it is also a huge resource load for any organization.

A practical alternative is Hevo – a fault-tolerant, reliable Data Integration Platform. Hevo gives you an environment free from any hassles, where you can securely move data from any source to any destination (www.hevodata.com/integrations).

See how easy it is to migrate data from Salesforce to BigQuery and that too for free.

Visit our Website to Explore Hevo

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