Organizations often struggle with data silos and inconsistencies due to customer data being dispersed across multiple systems. Such scattered data can hinder the ability to make informed, data-driven decisions. Platforms like Salesforce and Snowflake help address these challenges by unifying customer data and robust analytics.

A Snowflake Salesforce integration offers real-time access to data for enhanced decision-making in marketing, sales, and customer service. It also provides an economical solution for massive datasets and allows you to gain deeper insights into customer behaviors and trends.

Let’s look into the different methods for Snowflake Salesforce integration and its use cases.

What Is the Need to Share Data between Snowflake and Salesforce?

The Snowflake Salesforce integration allows you to merge CRM data from Salesforce with Snowflake’s analytical capabilities. You can integrate your Salesforce data with enterprise data in Snowflake using Salesforce’s Bring Your Own Lake (BYOL) feature. 

Here are some reasons to consider a Snowflake Salesforce integration:

  • Such an integration helps enhance the value of the Salesforce data while leveraging Snowflake’s impressive analytical capabilities for improved insights.
  • You can utilize actionable, valuable customer data from the Salesforce Data Cloud within Snowflake to generate actionable insights. These insights can help optimize operations, improve customer engagement, and drive revenue growth.
  • The integration allows you to obtain a deeper understanding of consumer behavior, industry trends, and operational efficiencies. As a result, you can predict customer needs more accurately, respond to market changes quickly, and streamline your processes.

Let’s look into some of the more specific reasons a Snowflake Salesforce integration might be beneficial:

  • Instant access to Salesforce data, together with finance and ERP data in Snowflake, enables you to generate real-time end-of-month, quarterly, or yearly predictions.
  • You can combine the finance data in Snowflake with click data from Salesforce to maximize campaign performance by channel.
  • With Snowflake, you can create native AI and machine learning models to calculate the purchase probability of customers for particular product categories. You can also obtain useful information by combining Snowflake’s product category data with Salesforce objects, such as profiles and website visits.

What Is the Bring Your Own Lake (BYOL) or Zero ETL Approach?

The BYOL (Bring Your Own Lake), or zero-ETL approach, enables effortless access and utilization of data without the traditional ETL processes. This approach is particularly useful for integrating third-party platforms like Snowflake and Google BigQuery with Salesforce environments.

The key features of Bring Your Own Lake include:

  • Simplified Data Access: BYOL provides direct and secure access to external data sources, enabling real-time querying.
  • Enhanced Integration: The zero-ETL method guarantees simplified data integration by eliminating the need for standard ETL operations. This reduces the time and complexities associated with data migration.
  • Cross-Platform Empowerment: BYOL enables enterprises to fully utilize their data assets across various platforms and ecosystems more effectively. This results in comprehensive analytics and better decision-making.

How to Share Data between Snowflake Salesforce Using Zero ETL Approach

Here are the steps involved in sharing data between Snowflake to Salesforce using the zero ETL approach.

  • Set up a Salesforce Data Cloud instance.
  • Choose which Salesforce application cloud objects (such as those from the Marketing, Sales, and Service Clouds) to make available in Snowflake, and then establish a Data Share.
  • Select a Snowflake target account where the data will be accessed.
  • Establish a Data Share in Snowflake for the selected account.
  • Validate the Snowflake Data Share target and implement an OAuth authentication to connect Salesforce and Snowflake securely.
  • Link the chosen Salesforce objects with the Snowflake Data Share; configure this connection through the Salesforce interface.

After the process is completed, the shared data is automatically available as secure views within the Snowflake target account.

What New Opportunities Are Generated by the Zero ETL Approach in Different Organizational Roles?

With increasing data volumes and the need for real-time analytics, the zero-ETL approach is expected to be widely adopted, especially in data warehouses like AWS and Snowflake. This trend is driven by the need for instantaneous data availability in cloud data warehouses without the delays of conventional ETL processes.

Here are some organizational roles that can benefit from zero-ETL:

  • Business Analysts: Your organization’s analysts can access the most recent data without any delays, enabling faster insights and decisions. This is critical for real-time responses to customer behavior or market trends.
  • Data Scientists: Your organization’s data scientists can benefit from real-time data streaming to build and refine predictive models. This helps enhance their ability to respond to dynamic market conditions.
  • Salesperson: By integrating CRM and third-party data into Snowflake, your organization’s salespersons can take advantage of performance analytics and intelligent forecasting. They can focus on customer-focused success, optimize performance, and develop strategy.
  • Marketer: Marketing professionals in your organization can improve segmentation and customize campaigns by combining non-CRM and CRM data. By utilizing this data in unique AI models, they can forecast consumer preferences, leading to hyper-targeted advertising and increased engagement.

Apart from revolutionizing data integration, zero ETL also increases the connectivity between data lakes and warehouses. This creates a seamless data environment that supports diverse data sources and formats for enhanced data-driven decisions.

The interaction between zero ETL and open-source frameworks, as well as orchestration tools, is expected to streamline data pipelines. This can improve the efficiency of data workflows to handle evolving business demands.

You can also integrate zero ETL with machine learning technologies to enhance data quality checks and for intelligent data management.

Limitations of Using Data Cloud Share and Zero ETL Approach

Despite the many benefits offered by the zero ETL approach, it also presents some limitations, including:

  • Absence of Robust Data Governance: Standard ETL processes often have built-in safeguards and controls to ensure data accuracy and integrity. In contrast, zero ETL may lack these rigorous data governance mechanisms, resulting in issues with data quality and compliance.
  • Limited Data Transformation Capabilities: Zero ETL relies on data movement with minimal transformation efforts. This can be limiting when complex data transformations are needed to improve the data for effective analytics or BI purposes.
  • Requires High Precision: Zero ETL implementation requires high precision to guarantee accurate and secure data transfer, particularly when working with legacy systems or a variety of data formats.
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What Is Zero Copy Data Share?

Zero-copy data share is a method that allows you to transfer data across platforms without the need to duplicate it from one memory location to another. The primary location from where the data is transferred is referred to as the provider end; the data is then received at the consumer end. By minimizing the overhead of moving data across various system components, it can significantly enhance the performance of data-intensive applications.

Within Snowflake, rather than distributing numerous copies of your dataset, you can create a clone to which you can grant your providers and consumers secure and shared access. 

Some key features of Zero Copy Data Share in Snowflake are listed below:

  • Instant Cloning: You can utilize Snowflake’s Zero Copy Cloning to replicate tables, schemas, or databases quickly. This feature avoids the need for physical data duplication and reduces storage costs.
  • Real-time Updates: Any updates to the original dataset are automatically and instantly available to all consumers with access, ensuring real-time data consistency.
  • Access Management: You can configure permissions to control what data is accessible to different consumers, ensuring each consumer can only view data relevant to their needs.
  • Cost Efficiency: Since there is no data duplication, the consumers don’t incur any additional storage costs.

Limitations of Zero Copy Data Share

Let’s look into some of the limitations of zero-copy data share:

  • Performance Considerations: While the process of cloning may be cost-efficient in terms of storage and computation, extensive modifications to cloned data can increase storage and costs.
  • Extended Clone Durations: Cloning large datasets or highly complex schemas can be extremely time-consuming and require a lot of effort, especially post-cloning.
  • Unsupported Object Formats: Snowflake supports cloning only for specific types of objects such as schemas, tables, views, sequences, materialized views, and databases. Other object types can not be cloned using zero-copy data share.

Streamlining Snowflake Salesforce Integration Using Hevo

Methods like the zero-ETL approach and the zero-copy data share for Snowflake Salesforce integration, while beneficial, require intensive technical expertise. A simpler and more economical solution to achieve this data share can be by using Hevo Data.

Hevo is a no-code, user-friendly ELT platform that simplifies data integration by providing a cost-effective, real-time solution to automate your data pipeline workflows. With its library of over 150 built-in connectors, you can extract data from one or multiple sources and load it into the required destinations.

Let’s look at some of Hevo’s key features:

  • Data Transformation: Hevo Data allows you to prepare your data for analysis with simple drag-and-drop or Python-based data transformations. This functionality can reduce the computational resources required in Snowflake for data transformation.
  • Automated Schema Mapping: By identifying the format of incoming data and automatically adjusting it to the target schema, Hevo simplifies the complexity of schema mapping. You can choose between full and incremental mappings according to your requirements.
  • Incremental Data Load: Hevo’s incremental data loading skills make it easier to load freshly updated data into the target destination. Eliminating unnecessary data storage and queries can help save storage and compute resource utilization.

Industry Customer Use Cases for Data Sharing between Snowflake Salesforce

Let’s discuss some of the real-life use cases of sharing data between Snowflake to Salesforce. 

Goods Retail

  • Retailers can use data from Salesforce Commerce and Service Clouds along with Snowflake’s analytics on point-of-sale and supply chain data to identify issues with customer service and manufacturing faults.
  • By integrating ERP data with consumer insights in Snowflake, retailers can also run targeted promotions, offering discounts for excess inventory, thus optimizing inventory levels.

Media Organizations

  • Media and entertainment companies can combine Salesforce data in Snowflake to refine demographic targeting and enhance privacy-compliant data sharing using data clean rooms.
  • These clean rooms allow for secure data exchanges with partners and facilitate highly targeted marketing campaigns and advertising through leading media publishers.

Healthcare Analytics

  • Healthcare organizations can synchronize critical patient information from Salesforce with their Snowflake data platform for enhanced data analysis.
  • They can use Snowflake’s advanced analytics and third-party AI tool integration capabilities to develop predictive models, which can enhance patient outcomes and member experiences.

Financial Services

  • Financial service organizations can use Snowflake to enhance their customer data, run proprietary models, and forecast future returns for their clients by utilizing Salesforce Financial Services Cloud customer data.
  • They can also combine customer information from various marketing and sales apps in Snowflake with information about customers from the Salesforce apps they use.
  • By utilizing Snowflake’s AI capabilities, they can leverage the entire customer data to generate customized action recommendations that optimize service quality and increase customer loyalty.


A Snowflake Salesforce integration allows you to deal with complex and massive data, enabling advanced analytical queries for enhanced decision-making. Snowflake data warehouse, with its efficient data storage and real-time data access, enhances Salesforce’s performance and provides a cost-effective enhancement.

In this article, you have explored how to perform a Snowflake Salesforce integration and the need for this integration. The real-life use cases demonstrate how effective data sharing between these two platforms can enable you to gain deeper insights for more informed decisions.


Q1. How can you send real-time push updates from Snowflake to Salesforce?

To send real-time push updates from Snowflake to Salesforce, you can use Hevo Data. This tool provides a no-code, automated data integration service to connect Snowflake and Salesforce without much prior technical expertise.

Q2. What is a CRM Analytics Connector?

CRM Analytics connectors are built-in connectors within Salesforce CRM Analytics that facilitate data integration from various sources. You can use these connectors to seamlessly import data from local org databases within Salesforce and external systems into the Salesforce CRM Analytics environment.

Chirag Agarwal
Principal CX Engineer, Hevo Data

A seasoned pioneer support engineer with more than 7 years of experience, Chirag has crafted core CX components in Hevo. Proficient in lean solutions, mentoring, and tech exploration.

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