With a rapidly growing user base, it's critical that your data architecture can scale efficiently... Adopt a serverless architecture: Use serverless computing to automatically scale resources on demand, ensuring cost-effective and efficient scaling. Implement robust data governance: Establish clear policies to maintain data quality, security and compliance as the user base grows. Engage stakeholders: Involve key stakeholders in planning and decision making to ensure alignment and support for the growing data infrastructure.

It is essential to build scalable and load performant systems as the number of users increase. Adopting cloud solutions (aws/azure/gcp) with autoscaling options helps reduce downtime with increased network traffic and data load. Database sharding can help with scaling horizontally by improving memory efficiency and improved storage for increased load. Serverless architecture ensures quick adaptability of the model to scale automatically as the user base grows by provisioning infrastructure to pay as you go as needed and to effectively back down to lower compute/usage during low performant periods.

A rapidly growing user base demands a scalable and efficient data architecture. Using an S3-based data lake ensures cost-effective storage with seamless scalability. Integrate AWS Glue for serverless metadata management, simplifying schema management and cataloging. For querying, leverage Athena, which provides serverless, pay-per-query analytics optimized for varied workloads. Implement efficient data partitioning and compression to reduce costs and improve query performance. Use Glue ETL jobs for incremental data processing and transformations. Monitor with tools like AWS CloudWatch to proactively address bottlenecks. This architecture scales efficiently to handle growth while maintaining performance.

To build a scalable data architecture in Azure, you can use Azure SQL Database with auto-scaling and performance tuning to keep things running smoothly. For NoSQL workloads, Azure Cosmos DB is a great option since it offers automatic partitioning and global distribution. Data partitioning in Azure Synapse Analytics or Azure SQL helps improve query speed and reduce bottlenecks. To handle high traffic, Azure Load Balancer and Azure Kubernetes Service (AKS) can distribute the load effectively. Adding Azure Cache for Redis can further optimize database performance by reducing the load on primary storage. These steps help ensure your system remains efficient as it scales.

By design, the system should be performant. Hence, include the performance numbers including the growth expectation as part of the system design. This will ensure, your data architecture is capable and scalable.