How to load data from salesforce to snowflake: definitive guide 

How to load data from salesforce to snowflake: definitive guide 

October 9, 2023
October 9, 2023
How to load data from salesforce to snowflake: definitive guide 
After finishing this comprehensive tutorial, you'll have the ability and know-how to seamlessly load your Salesforce data into Snowflake, allowing your business to realize the full potential of its data assets and make well-informed, data-driven decisions that lead to success. 

In today's data-centric market, the ability to move data between platforms smoothly and effectively is not only necessary; it's a strategic advantage. Among the many data integration issues that businesses face, one critical activity stands out: moving data from Salesforce to Snowflake. We will completely cover this procedure in this guide, offering you a step by step guide for importing data from Salesforce into Snowflake.

Salesforce serves as a primary center for many businesses, storing vital data about customers, leads, opportunities, and more. Snowflake, a powerful cloud-based data warehousing system, is praised for its scalability and analytical capabilities. Combining these two powerful platforms can lead to new insights, more analytical capacity, and better reporting.

Nonetheless, getting from Salesforce to Snowflake isn't always easy. It includes understanding the complexities of data extraction, transformation, and loading (ETL) to ensure that your data is accurately and safely transferred into Snowflake. Whether you're an experienced data engineer or a company executive looking to equip your team with data-driven decision-making capabilities, our comprehensive guide will walk you through each step of the process.

After finishing this comprehensive tutorial, you'll have the ability and know-how to seamlessly load your Salesforce data into Snowflake, allowing your business to realize the full potential of its data assets and make well-informed, data-driven decisions that lead to success. 


Introduction to salesforce

Salesforce is a leading cloud-based software-as-a-service (SaaS) platform among Customer Relationship Management (CRM) tools in modern corporate operations. Salesforce, known for its versatility, provides a wide range of integration capabilities and a wealth of features that are as broad as they are amazing. Indeed, it frequently appears to have more functionalities than one can simply keep track of.

This dynamic platform enables businesses to adjust their CRM procedures to their own requirements. It is more than just a CRM tool; it is a versatile solution that can help with a wide range of duties, from thorough account planning to effective time management and promoting collaborative teamwork.

Salesforce's popularity and widespread acceptance attest to its adaptability and applicability in a wide range of industry situations. Because of its user-friendly interface and rich feature set, it has been the go-to choice for organizations of all kinds, allowing them to streamline operations, improve customer interactions, and promote productivity.

Introduction to snowflake 

In the field of modern data management, Snowflake is an outstanding example. In terms of how businesses store, manage, and use their data, it represents a paradigm change rather than just another data warehousing platform. Snowflake is primarily a cloud-based data warehousing solution, but it does so much more.

The architecture of Snowflake is unique. Snowflake runs fully in the cloud, unlike conventional data warehouses, which demand a substantial upfront investment in hardware and difficult infrastructure administration. Because of the inherent security and redundancy of the cloud, users may grow their data storage and processing capacities as needed, paying only for what they really use.

Additionally, because to Snowflake's distinctive architecture, data may be saved and processed separately, separating storage from computing. In addition to improving efficiency, this "multi-cluster, shared data architecture" makes it easier for businesses to share data and collaborate.

Another feature of Snowflake is its simplicity of use. It is used by both business users and data specialists thanks to its user-friendly interface. Thanks to its support for ANSI SQL and interaction with well-known business intelligence tools, querying and analyzing data is simple.

Methods to load data from salesforce to snowflake 

Loading data from Salesforce to Snowflake involves several steps, and there are multiple methods you can use to accomplish this task. Here are some common methods:

Method 1 : using fivetran tool 

Step 1: turn off IP session locking

If you have Session IP Locking enabled or encounter an INVALID_SESSION_ID error, go to the Session Settings page and uncheck the box that says "Lock sessions to the IP address they originated from." You probably won't need to do this often (less than 1% of the time) because this setting is usually already turned off for most users.

Step 2: unlock your session

To set up a Salesforce connector, you can use any Salesforce user in your organization who has permission to read data from Salesforce's APIs. However, we recommend creating a special user just for this purpose and limiting their access to only the data you want to sync. You can do this by creating a profile in Salesforce and assigning it to the user.

Step 3: control data access (if needed)

Fivetran syncs data based on the permissions of the user connected to Salesforce. If there's certain data you don't want Fivetran to sync to your destination, you can limit that user's permissions.

Here are two ways to control data access when using Fivetran:

Option 1: Disable Tables in Fivetran Dashboard

  • Go to your Fivetran dashboard and find the Salesforce connector details page.
  • Click on the schema tab.
  • Here, you can disable specific tables and columns that you don't want to synchronize.
  • If you're worried about accidentally syncing sensitive data to your destination, you can click on the gear icon to open the Schema Change Settings menu and choose "Allow columns."

Option 2: Limit the Access of the Connecting User in Salesforce

  • Fivetran connects to your Salesforce account using the credentials of a specific user.
  • To restrict our access to certain data, you should limit that user's permissions within Salesforce.
  • It's a good practice to set these limitations before you initially connect the user through our setup form. If you restrict the user's permissions after connecting, some data in Salesforce may become outdated and won't be updated in your destination anymore.

By using these options, you can ensure that only the data you want is synchronized, and you can control access to sensitive information, enhancing the security and efficiency of your data integration process.

Step 4: finish fivetran setup

Fivetran offers two services for Salesforce: one for the production environment and another for the sandbox environment. 

  • In the connector setup form, enter the name you want for your destination schema.
  • Click "Authorize" to give Fivetran permission to access the API. This will take you to the Salesforce login page.
  • Log in to your Salesforce account to let Fivetran connect to it.
  • Return to the Fivetran dashboard and click "Save & Test." Fivetran will take care of the rest, syncing your data from your Salesforce account.

Advantages of using fivetran 

Advantages of using fivetran for your data platform integration

  • Boosted efficiency: Fivetran prioritizes efficiency by simplifying data integration, eliminating time-consuming tasks. It automates processes like data extraction, transformation, and loading (ETL), freeing up your resources for data exploration and insights, rather than getting bogged down in complex integration procedures.
  • Seamless connectivity: Fivetran effortlessly connects various data sources, making integration straightforward. With its wide range of pre-built connectors covering databases, cloud apps, and marketing platforms, you can examine your data comprehensively and effortlessly.
  • Real-time data sync: Fivetran provides nearly real-time data synchronization, ensuring you always have the latest updates from your data sources. This keeps your data accurate and up-to-date.
  • Simplified data transformation: Fivetran simplifies data transformation with built-in processes and careful schema mapping. This ensures smooth changes in data structures, removing complexity and ensuring compatibility and consistency. This streamlined transformation process allows organizations to unlock the full potential of their data.
  • Scalability for frowth: Fivetran's scalable architecture can handle increasing data volumes as your business expands. It not only accommodates growing demands without sacrificing speed but also provides a solid foundation for data integration, ensuring efficient operations as your data ecosystem evolves.

Method 2 : using custom ETL scripts 

Step 1: configure salesforce and snowflake

To make Salesforce and Snowflake work together using the Sync Out connector, you need to do a few things. First, create some Snowflake objects. Then, in Salesforce, enable Sync Out and set up the connection to Snowflake. This section explains these steps in detail. Below, you'll find a script to help you create the Snowflake objects.

Step 2: Enable sync out

Now, let's enable Sync Out and set up the connection to Snowflake in Salesforce:

  • In Salesforce, click on "Setup."
  • In the search bar, type "Analytics" and click on "Settings."
  • Check the boxes for "Enable Data Sync and Connections" and "Enable Snowflake output connection."
  • If you want to connect to Snowflake in real-time, also check "Enable direct data for external data sources."

Step 3: configure snowflake output connection

The final setup step is configuring the Snowflake output connection that Sync Out will use. Here's how:

  • In Salesforce, open "Analytics Studio" and go to "Data Manager."
  • Select the "Connect" tab and click "Connect to Data" at the top-right.
  • Choose "Output Connections" and click "Add Connection."
  • Select the "Snowflake Output Connector."
  • Fill in all the necessary details for your connection, including Snowflake information (more info can be found in Salesforce documentation).
  • Test the connection by clicking "Save & Test."

Step 4: connect to local salesforce data and set up data sync

Now, let's connect to your local Salesforce data and configure data synchronization.

Connecting to local salesforce data

  • Click "Connect to Data" in the Salesforce Data Manager.
  • In the "Input Connections" tab, select the "SFDC_LOCAL" object for your local Salesforce data.
  • Choose the Salesforce object you want to sync and continue.
  • Select the specific fields you want to sync for that object and continue.
  • Save your choices on the "Preview Source Data" screen.

The objects and fields you've chosen will now be visible in the Data Manager. You can edit them anytime by clicking on the object.

Step 5: enabling sync out and setting a schedule

  • On the right side of "SFDC_LOCAL," click the arrow and select "Sync Out."
  • Check the box to "Enable Sync Out" and choose the Snowflake output connection name. Then, click "Save."
  • To run the data synchronization immediately, click the same arrow and select "Run Now." Alternatively, you can set a schedule for the data sync.
  • You can monitor your sync jobs by clicking on the "Monitor" tab.

Step 6: accessing data in snowflake

Your salesforce objects will now appear as tables in Snowflake, managed by the Sync Out connector. This connector takes care of incremental or full data syncs as specified earlier.

Now, you can directly access this data in Snowflake and combine it with other data sources, such as internal systems, partner data, or data from Snowflake Data Marketplace, to get a complete view of customer data.

Disadvantages of using ETL scripts 

Using ETL (Extract, Transform, Load) processes to integrate data from sources such as Google Sheets to Snowflake can present a number of obstacles and disadvantages, particularly for enterprises with limited resources or knowledge.

  • ETL techniques have flexibility limits. They may struggle to manage unstructured data or deliver real-time data streams, making them unsuitable for today's data requirements.
  • Scalability can also be a concern. ETL operations may become overburdened as data volumes increase, resulting in performance problems.
  • Data privacy is also a major concern. ETL operations entail vast volumes of data being collected, stored, and analyzed, generating concerns about data security and privacy compliance.
  • Complexity and maintenance: As you add more data sources and requirements, ETL scripts can get very complex. They become more challenging to manage as a result, which might result in mistakes and ineffectiveness.
  • Time-consuming: ETL script development, testing, and maintenance can take a lot of time. This can make it take longer to get data where it needs to go, which might slow down how quickly firms can act on it.
  • High initial costs: Writing or modifying ETL scripts can be pricey. To set them up and get them functioning, you need knowledgeable developers and a decent amount of funding.
  • Not Real-Time: Because traditional ETL procedures frequently involve working in batches, they cannot integrate data in real-time. This might be a challenge.


In conclusion, the migration from Salesforce to Snowflake is more than just a technical undertaking; it's a strategic decision that may give enterprises access to data-driven creativity and agility. We have examined the complexities of this data integration procedure throughout this essay, illuminating its revolutionary potential.

Snowflake provides a way for businesses to realize the full potential of their data because of its cloud-native architecture and unrivaled scalability. It offers a flexible and affordable platform where data is not just saved but also transforms into a priceless resource that supports development and insights.

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