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

Financial company: create an inventory of all users across all managed and cloud applications Collect users and permissions from all 50+ internal and external systems. Read the case study	"For auditing and reconciliation purposes, they need to periodically collect the information about all the users in all the systems, together with permissions, and store it in the database. They use an internally developed web portal as a dashboard to reassign the roles and send notifications to the users."
Professional social network: load data into the Amazon Redshift from multiple sources The ETL must be able to load data into the Redshift from SQL Server and Postgres databases, Salesforce, Marketo, Smartsheet, and some other SaaS applications. Read the case study	"The customer needed to load data from Salesforce, Marketo, and Smartsheet. In Etlworks, the connectors for all these SaaS sources are actually regular database connectors, so the Database to Redshift Flow type can be used to configure the source-to-destination transformations."

 

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.