Get started with Salesforce

Etlworks Salesforce connectors provide fast, real-time access to Salesforce data. They support all standard and custom objects, as well as metadata (fields), available through the Salesforce API. Functionally, they work just like any other connector for a connected app—simple to configure and fully SQL-enabled.

What can you do with Salesforce in Etlworks

Connect to Salesforce Create a Salesforce connection to access and integrate Salesforce data. Read more	Extract data from Salesforce Extract data by connecting Salesforce as a source, just like with any relational database or connected app. Read more
Load data in Salesforce Load data into Salesforce by configuring it as a destination in a flow—no custom scripts or queries needed. Read more	Browse data in Salesforce Use the Etlworks Explorer to browse objects and metadata, and run SELECT and DML queries against your Salesforce connection. Read more
MERGE (UPSERT) with Salesforce When using the Salesforce connector in Etlworks, you can perform a MERGE operation to insert new records or update existing ones in a single step—commonly referred to as an UPSERT. Read more	Salesforce vs Salesforce Legacy connectors Etlworks includes two connectors for Salesforce: Premium and Legacy. 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 connected app 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

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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 connected app, and the destination is a Connection created in step 2, for example, a 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 connected app.

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

When using the Salesforce connector in Etlworks, you can perform a MERGE operation to insert new records or update existing ones in a single step—commonly referred to as an UPSERT. This operation uses the external ID field or record ID to determine whether a record already exists. 

MERGE works only with premium Salesforce connector and not supported by Salesforce Legacy connector.

Step 1. Create a Flow where the destination is Salesforce 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.