How to Analyze PostgreSQL Data Using Salesforce Einstein Analytics

Wave Analytics, now known as Einstein Analytics, is a powerful analytics tool provided by Salesforce that allows for deep insights and visualization of data. It's particularly adept at handling data from various sources, including both Salesforce internal data and external databases like PostgreSQL. To handle data from external databases such as PostgreSQL, Einstein Analytics uses several methods to integrate, transform, and visualize this data effectively.

Integration of External Data (PostgreSQL) into Einstein Analytics

1. Salesforce Connect: This is one of the primary methods for integrating external data into Salesforce. Salesforce Connect allows you to access external relational data sources in real-time without copying the data into Salesforce. You can set up external data sources and define external objects in Salesforce that map to the tables in a PostgreSQL database. This approach uses OData and keeps the data in PostgreSQL while allowing it to be accessed as if it were native Salesforce objects.

2. Data Import via Connectors: Einstein Analytics provides built-in connectors to directly import data from external databases like PostgreSQL. This process involves copying data into Salesforce, where it can be used for analytics. The data import can be scheduled at regular intervals to keep the Einstein Analytics data up to date.

3. Heroku Connect: For users who run applications on Heroku and store their data in a PostgreSQL database, Heroku Connect provides a seamless data synchronization service between Heroku Postgres databases and Salesforce. This service is beneficial for users who need real-time data integration between their application databases on Heroku and Salesforce.

Data Preparation and Transformation

After integration, the data often needs to be transformed and optimized for analysis:

• Einstein Data Prep: A tool within Einstein Analytics that helps in cleaning, transforming, and augmenting data from external sources like PostgreSQL. It allows the creation of recipes for data transformation without the need for extensive coding.

• SAQL (Salesforce Analytics Query Language): For more complex transformations and analytics, SAQL can be used to program data flows in Einstein Analytics. It's similar to SQL and allows for detailed manipulation and querying of integrated data.

Visualization and Analytics

Once the data from PostgreSQL is integrated and prepared within Einstein Analytics:

• Dashboards and Apps: Users can create interactive dashboards and applications within Einstein Analytics to visualize and analyze the data. These tools provide a wide array of widgets and templates to display data effectively.

• AI and ML Capabilities: Einstein Analytics provides AI-driven insights and recommendations using Salesforce's AI technology, Einstein. This feature can analyze data from PostgreSQL to provide predictive analytics and trend analysis.

Security and Compliance

Salesforce ensures that data integration practices meet high standards of security and compliance:

• Encrypted Connections: Data transfers between PostgreSQL databases and Salesforce are secured through encrypted connections.

• Compliance Adherence: Salesforce complies with major regulations and standards, ensuring that data handling meets all necessary compliance requirements.

Conclusion

Einstein Analytics provides robust capabilities to integrate, transform, and visualize data from external databases like PostgreSQL. By leveraging tools such as Salesforce Connect, Heroku Connect, and built-in connectors, along with powerful data preparation and visualization features, businesses can gain valuable insights from their data residing in PostgreSQL or other external databases. This integration allows businesses to make informed decisions based on a comprehensive view of their data across different platforms.