Managing Large Data Volumes in Salesforce: Best Practices for Admins and Developers

Managing Large Data Volumes in Salesforce: Best Practices for Admins and Developers

As organizations grow, so does their data. For Salesforce admins and developers, managing large volumes of data can become a challenge. Large data volumes, if not managed effectively, can slow down performance, complicate data retrieval, and even risk hitting Salesforce’s governor limits. In this guide, we’ll cover best practices for managing, optimizing, and working with large data sets in Salesforce to ensure smooth and efficient operations.

Why Large Data Management Matters in Salesforce

Salesforce is designed to handle substantial amounts of data, but excessive volumes can strain resources and affect performance. Effective data management:

• Optimizes Performance: Efficient data handling speeds up load times and ensures data accessibility.
• Reduces Governor Limit Risks: Managing data effectively minimizes the chance of hitting Salesforce’s built-in limits.
• Enhances Reporting and Analytics: Structured data improves reporting, helping organizations make informed decisions.

1. Data Architecture and Planning

The foundation of managing large data volumes begins with a well-thought-out data architecture. This involves understanding your data structure, identifying critical records, and planning data flow.

Key Steps in Data Architecture:

• Identify Essential Fields: Limit fields to only those necessary for business processes and reporting.
• Dene Relationships Carefully: Avoid unnecessary relationships between objects. Use lookup fields over master-detail where feasible to reduce dependency.
• Plan for Archive and Delete: Have a strategy for archiving or deleting old records to keep active data volumes manageable.

Example: Using Lookup Relationships for Better Performance

If your organization handles extensive client and case data, using lookup relationships between clients and cases (instead of master-detail relationships) provides flexibility and minimizes processing requirements.

2. Data Indexing for Faster Retrieval

Data indexing can greatly improve the performance of SOQL queries. Indexed fields help Salesforce retrieve records faster, which is crucial for large data sets.

• Primary Indexed Fields: Salesforce automatically indexes certain fields, such as Record ID, Name, and OwnerID.
• Custom Indexing: Request custom indexing for fields that are frequently queried, especially on high-volume objects.

Example: Using Indexing for Frequently Queried Fields

Suppose your sales team frequently queries accounts by industry type. Indexing the Industry field
on the Account object can help retrieve results faster, reducing query times significantly.

3. Efficient Use of SOQL Queries

SOQL (Salesforce Object Query Language) is fundamental to data retrieval, and optimizing SOQL queries is essential for performance. Here are some techniques:

• Use SELECT with Specific Fields: Avoid SELECT *; retrieve only the fields you need.
• Use WHERE Filters: Filter records early to narrow down results.
• LIMIT Records: Use the LIMIT clause to prevent returning unnecessary records.

Example: Optimized SOQL Query for Accounts
SELECT Name, Industry FROM Account WHERE Industry = ‘Technology’ LIMIT 100

This query retrieves only the Name and Industry fields for Technology accounts, limiting the results to 100 records.

4. Optimizing Data Load and Batch Processing

When working with large volumes of data, avoid performing mass updates in a single transaction. Instead, use Batch Apex or Data Loader tools that process records in smaller chunks.

• Batch Apex: Processes records in batches, reducing the load on the system.
• Data Loader: Ideal for bulk data uploads and updates outside of real-time processing.

Example: Using Batch Apex for Bulk Data Updates

Suppose you need to update 10,000 accounts. Instead of a single operation, Batch Apex can process these records in chunks of 200, reducing the risk of hitting governor limits and improving performance.

5. Archiving and Deleting Old Data

Regularly archiving or deleting unused data keeps active data manageable. Salesforce provides Big Objects for archiving large historical datasets that don’t need frequent access.

• Archiving Strategy: Move old records to a Big Object or an external system.
• Automated Deletion: Set up scheduled jobs to delete outdated records that no longer serve business purposes.

Example: Archiving Closed Cases

For an organization with a high volume of cases, you could archive all closed cases older than two years, moving them to a Big Object to maintain access while keeping active case data streamlined.

6. Managing Data Skew and Ownership Skew

Data Skew occurs when a large number of child records are associated with a single parent, or a high volume of records is owned by a single user. This can lead to performance issues.

• Ownership Skew: Avoid assigning too many records to a single user. Distribute ownership across users or use automated ownership assignment tools.
• Parent-Child Skew: Limit the number of child records per parent object.

Example: Avoiding Ownership Skew

f one account owner manages 20,000 accounts, consider reassigning some of these accounts to another owner to reduce skew and improve performance.

7. Using Asynchronous Processing for Complex Tasks

For tasks that don’t require immediate completion, using asynchronous processing methods like Future Methods, Queueable Apex, and Scheduled Jobs helps spread the load over time.

• Future Methods: Useful for tasks that can be completed later, such as integrating with external systems.
• Queueable Apex: Provides more control than Future Methods for complex jobs.
• Scheduled Jobs: Execute tasks during o-peak hours.

Example: Using Queueable Apex for Real-Time Updates

Suppose you need to process a large number of records based on a user action. Queueable Apex can handle this in the background, reducing the load on system resources during peak times.

8. Streamlining Reports and Dashboards

Running complex reports on large data volumes can be slow. To improve performance:

• Use Report Filters: Narrow down the data displayed in reports with filters.
• Limit Historical Snapshots: Avoid keeping extended histories if they aren’t necessary for analysis.
• Use Dashboard Refresh Intervals: Set dashboards to refresh at intervals to avoid real-time strain on the system.

Example: Filtering Reports by Date

Instead of running a report on all opportunities, filter by a specific date range (e.g., opportunities closed within the last quarter) to reduce data retrieval time and improve report performance.

9. Externalize Data When Necessary

For organizations with extremely large data volumes, consider externalizing some of the data to an external database or data warehouse.

• External Objects: Use Salesforce Connect to link external data sources with Salesforce, allowing access to data without storing it in Salesforce.
• External Data Storage: For less critical data, an external storage solution can reduce data load on Salesforce.

Example: Externalizing Archived Data

If an organization has over 10 years of sales data, they might store historical records in an external database and connect it to Salesforce through Salesforce Connect, maintaining access without straining Salesforce resources.

10. Best Practices for Maintaining Large Data Volumes

1. Regularly Review Data Usage Conduct periodic audits to ensure only relevant data is stored.
2. Limit Data Visibility with Sharing Settings Use sharing rules and role hierarchies to limit data access, which can improve performance.
3. Optimize Triggers for Large Data Use bulk-safe code in triggers and test with large data volumes to prevent performance issues.
4. Utilize Salesforce’s Health Check Tools Salesforce offers tools to monitor system health and performance. Use these tools to identify.
5. Stay Updated with Salesforce Releases Salesforce regularly updates its platform with performance improvements. Stay informed to utilize new features for better data management.

Conclusion

Managing large data volumes in Salesforce requires a combination of strategic data architecture, optimized queries, asynchronous processing, and regular data maintenance. By applying these best practices, admins and developers can ensure their Salesforce orgs perform efficiently, even with substantial data.

With careful planning and the right tools, you can create a scalable, responsive Salesforce environment that meets both current and future data needs. From data indexing to asynchronous processing, these techniques empower you to handle large data volumes seamlessly.