Salesforce ETL: 2 Easy Methods

Organizations are constantly on the lookout for simple solutions to integrate their company data from several sources into a centralized location, then analyze it to make informed decisions. This process is termed Data Integration. One of the most popular Data Integration techniques is ETL (Extract, Transform and Load). You will get to know more about the ETL procedure in the further sections.

This article aims to help you discover various approaches to perform Salesforce ETL. It also highlights the limitations and challenges that might come your way. The objective is to share enough knowledge to help you choose the best approach for your use case. You will also explore the Salesforce ETL tools in the further sections.

Introduction to Salesforce

Salesforce is the most widely used cloud-based CRM platform. It stores all your customer data, contact data, marketing leads, and many more. Combining this with data from many other sources in your Data Warehouse can deliver valuable insights for your business. The first step, however, would be to get all the data from Salesforce into a Data Warehouse of your choice. In the further section, you will get to know the methods used for Salesforce ETL.

To know more about Salesforce, visit this link.

Methods to Set up Salesforce ETL

Users can use one of the two methods for setting up the Salesforce ETL:

Method 1: Using an automated Data Pipeline such as Hevo Data

Step 1: Configure your Salesforce Source details 

Step 2: Configure your Destination details

For this demonstration, I will choose Snowflake as my destination; you can choose any other destination.

Step 3: Check your Pipeline

Your pipeline from Salesforce to Snowflake has been set up, and your tables will be ingested to the destination shortly.

Method 2: Manual Method to Copy data from Salesforce to Warehouse 

For this Method, I will show you how to connect your data from Salesforce to BigQuery. If you want to connect Salesforce to Snowflake using the manual method, read our blog on Data Sharing Between Snowflake and Salesforce Data Cloud. 

Prerequisites

• Salesforce account
• Billing enabled BigQuery account
• Salesforce API

Step 1: Select one of the Salesforce APIs that suits your needs

Here are a few API’s that you can look at:

• Streaming API
• REST API

Step 2: Extract data from Salesforce 

After successful authentication, fetch the desired data from Salesforce using the chosen API.

Step 3: Format the Data before loading into BigQuery

Ensure the data extracted is in the correct format before loading it into BigQuery. The formats that are supported by BigQuery are CSV JSON. You can check out the other supported data formats.

Step 4: Load the data into BigQuery

Step 4. a) Use tools like BigQuery API (enable the BigQuery API if it is not already enabled)

Step 4. b) Create a new table in BigQuery where you want to load the data or append it to an existing table.  

Step 4. c) Load the export file directly from your local environment to BigQuery.

Challenges of Setting up Salesforce ETL Using Custom ETL Scripts

Administrators and engineers write scripts to solve problems. simplify processes and improve performance. Unfortunately, this often evolves into an over-reliance on scripting. Your entire scripting universe can grow into an unsustainable burden. Below are the challenges that can be faced while setting up Salesforce ETL:

1. Error Handling: Glaring errors that are found in your scripts get fixed early, but there are more insidious errors that often are not discovered until a process is well underway. What do you do when an error causes part of or even your entire data flow to stop?
2. Real-time Salesforce ETL: The steps mentioned above only help you load the data one-time or periodically. The process to get your Salesforce data to your warehouse is cumbersome and effort-intensive.
3. Schema Changes: What is your procedure for updating schema at the warehouse in response to changes in the Salesforce API?
4. Source/Destination Unreachable: How do you plan to prevent data loss if Salesforce or your Data Warehouse is not reachable? 
5. Documentation: Are your scripts documented and cataloged? When engineering resources move in and out of the organization, this becomes a key aspect for the organization.
6. Addition Data Requests: Would you have ready engineering resources to modify and update the scripts when there is a request for more data? Given this data is supercritical for your business teams, it would be smart to account for update requests.

All the above limitations can be handled by opting for a Salesforce ETL platform that works out of the box. Here is how Hevo can help:

Choosing the Best Salesforce ETL Tool

Choosing the best tool from the plenty of Salesforce ETL is one of the difficult tasks. However, there are some features that you should look for in the Salesforce ETL tools. Some of these features include:

• No code platforms should be preferred that do not require technical backgrounds.
• The ETL tool should provide an adequate number of integrations with Data Warehouses, Databases, CRMs, etc.
• An appealing and easy-to-use interface should be present.
• The ETL platform should be secured enough and should comply with regulations established by CCPA, GDPR, and other laws

Conclusion

Salesforce ETL tool like Hevo is key to enabling data agility, performance, and reliability in an organization. Hevo’s fault-tolerant, dependable data integration platform gives you a trouble-free environment in which you can move data from any source to any destination.

Businesses can use automated platforms like Hevo Data to set this integration and handle the ETL process. It helps you directly transfer data from a free data source such as Salesforce, etc., to a Data Warehouse, Business Intelligence tools, or any other desired destination in a fully automated and secure manner without having to write any code and will provide you a hassle-free experience.

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