Big Objects vs Data Lake

Developers should use Big Objects when dealing with massive datasets that exceed standard Salesforce storage limits or require long-term archival, such as audit trails, IoT sensor data, or historical transaction logs meets developers should learn about data lakes when working with large volumes of diverse data types, such as logs, iot data, or social media feeds, where traditional databases are insufficient. Here's our take.

Big Objects

Nice Pick

Developers should use Big Objects when dealing with massive datasets that exceed standard Salesforce storage limits or require long-term archival, such as audit trails, IoT sensor data, or historical transaction logs

Pros

• It is particularly valuable for compliance-driven use cases where data must be retained for years without impacting the performance of core Salesforce operations
• Related to: salesforce-platform, apex

Cons

• Specific tradeoffs depend on your use case

Data Lake

Developers should learn about data lakes when working with large volumes of diverse data types, such as logs, IoT data, or social media feeds, where traditional databases are insufficient

Pros

• It is particularly useful in big data ecosystems for enabling advanced analytics, AI/ML model training, and data exploration without the constraints of pre-defined schemas
• Related to: apache-hadoop, apache-spark

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Big Objects is a platform while Data Lake is a concept. We picked Big Objects based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Big Objects wins

Based on overall popularity. Big Objects is more widely used, but Data Lake excels in its own space.