Get started with Salesforce

Etlworks Salesforce connector allows fast real-time access to Salesforce data. The connector supports all objects and metadata (fields) available through the Salesforce API and works just like any other database connector.

What can you do with Salesforce in Etlworks

Connect to Salesforce Create a Salesforce Connection to work with data in Salesforce. Read more	Extract data from Salesforce Extracting data from Salesforce is similar to extracting data from the relational database. Read more
Load data in Salesforce Loading data in Salesforce is similar to loading data into relational database. Read more	Browse data in Salesforce Use the Etlworks Explorer to browse data and metadata in Salesforce as well as execute DML and SELECT queries against Salesforce Connection. Read more

 

Videos

ETL with SaaS apps Create a source Connection for a SaaS app or service. Create a destination Connection for the relational database and test it. Create a new database to database Flow. Add a new transformation. After which, select FROM Connection and data object to extract and TO Connection and table. Click MAPPING and configure the Source SQL if needed (optional). Test the transformation. Add per-field Mapping if needed. Read the full article

 

Related resources

Salesforce connector Etlworks Salesforce connector allows fast, real-time access to Salesforce data. The connector supports all objects and metadata (fields) available through the Salesforce API and works like any other database connector.  Read more

Related case studies

Capital Rx case study Integrate cloud storage, APIs, Salesforce, SharePoint, Exchange, Google BigQuery, and more into Snowflake.	Capital Rx, a leading healthcare technology company revolutionizing pharmacy benefits, leverages Etlworks to integrate diverse data sources into Snowflake. By seamlessly connecting cloud storage systems, APIs, Excel, Salesforce, SharePoint, Exchange, Google BigQuery, and more, Etlworks enables Capital Rx to maintain a unified data ecosystem, supporting advanced analytics and decision-making.
Sermo case study Collect half a billion records daily from SQL Server, Marketo, Salesforce, and Smartsheet into Redshift in real time.	Sermo, a global medical community platform, needed to aggregate massive volumes of data from diverse sources, including SQL Server databases, SaaS platforms like Marketo, Salesforce, and Smartsheet, and load it into Amazon Redshift in near real-time. Within two weeks of subscribing to Etlworks, Sermo was collecting and processing over half a billion records daily—an achievement made possible by Etlworks’ advanced features and unparalleled support.
Stratify case study Integrate with CRM and ERP platforms like Sage Intacct, NetSuite, QuickBooks Online, Salesforce, and more.	Stratify, the all-in-one FP&A platform for data-driven collaboration with business stakeholders, relies on Etlworks to streamline integrations across a wide range of CRM and ERP platforms, third-party APIs, and cloud services. By leveraging Etlworks, Stratify has unified data workflows for platforms like Sage Intacct, NetSuite, QuickBooks Online, Microsoft Dynamics Finance and Operations, Salesforce, Google services, and more, delivering robust, scalable, and flexible solutions for their clients.

Connect to Salesforce

Create a Salesforce Connection to work with data in Salesforce.

Extract data from Salesforce

Extracting data from Salesforce is similar to extracting data from the relational database.

Step 1. Create a Salesforce Connection which will be used as a source (FROM).

Step 2. Create a destination Connection, for example, a Connection to the relational database, and if needed, create a Format (Format is not needed if the destination is a database or well-known API).

Step 3. Create a Flow where the source is a database and the destination is a Connection created in step 2, for example relational database.

Step 4. Add a new source-to-destination transformation.

Step 5. Select the Salesforce Connection created in step 1 as a source Connection and select the Salesforce object you are extracting data from:

Step 6. Select a TO Connection, Format (if needed), and object (for example database table) to load data into.

Step 7. Click MAPPING and optionally enter Source query (you don't need a query if you are extracting data from the Salesforce object unconditionally):

Step 8. Optionally define the per-field Mapping.

Step 9. Add more transformations if needed.

Load data in Salesforce

Loading data in Salesforce is similar to loading data into relational database.

Step 1. Create a source Connection and a Format (if needed).

Step 2. Create a destination Salesforce Connection.

Step 3. Create a Flow where the destination is a database.

Step 4. Add a new source-to-destination transformation.

Step 5. Select FROM and TO Connections and objects (also a FROM Format if needed).

Step 6. Optionally, define the per-field Mapping.

Step 7. Add more transformations if needed.

MERGE (UPSERT) with Salesforce

MERGE works only with Salesforce with OAuth2 connector.

Step 1. Create a Flow where the destination is Salesforce with OAuth2 connection.

Step 2. When configuring a transformation, click MAPPING, and then select the Parameters tab.

Step 3. Select CUSTOM as the Action.

Step 4. Enter a comma-separated list of field names in the Lookup Fields which will be used to identify a unique record. Alternatively, you can enable the Predict Lookup Fields.

Step 5. Enter the template for the UPSERT into the Destination query field.

Example:

UPSERT INTO LocationToUse ({COLUMNS}, ExternalIdColumn) 
VALUES ({VALUES}, '{KEYS}')

The following tokens can be used in the Destination query:

• {COLUMNS}: the comma-separated list of columns
• {VALUES}: the values for the columns
• {KEYS}: the lookup fields

Browse data in Salesforce

You must have a Salesforce Connection to browse objects and run SQL queries.

Use the Etlworks Explorer to browse data and metadata in Salesforce as well as execute DML and SELECT queries against Salesforce Connection.