1.? ?Scale Up Database Storage: Increase storage capacity to accommodate growing data, ensuring sufficient space for data growth. 2.Partition Data by Service: Implement data partitioning (sharding) to distribute data across multiple servers, reducing the load on individual servers. 3.Cache Layer: Implement a caching layer (e.g., Redis, Memcached) to store frequently accessed data, reducing database queries. 5.Indexing: Implement indexing on frequently queried columns to improve query performance. 6.Database Clustering: Implement database clustering for high availability and scalability. 7.Data Archiving: Regularly archive historical data to reduce active data volume.

There are several methods to deal with high volume and velocity data for enabling enhanced performance of infrastructure. 1.) compress and aggregate data to remove redundant data. 2.) use distributed computing capabilities such as Spark, VM, Containers to process data across multiple nodes to reduce dedicated workload traffic. 3.) Utilize cloud / distributed storage capacity to store heavy volumes for operation. 4.) partition data to limit volume 5.) streaming techniques can ensure continuous and real time analytics for domain such as finance software 6.) Utilize platforms such as Databricks, Apache, Hadoop 7.) setup recovery systems/nodes to safeguard data engineering operations. 8.) Optimize the code and monitor workload

To manage a sudden surge in data volume, it’s crucial to identify whether the performance challenge lies in ingesting data quickly or processing it efficiently. A classic approach to handling large datasets is database partitioning—e.g., segmenting data by criteria like weekdays ensures fast access and enables quick deletion. If the spike was anticipated, a well-prepared admin should have allocated resources accordingly. If unexpected, such as during a potential cyberattack or poor requirement planning, immediate action is critical. Continuous monitoring, forecasting, and adjusting resources are essential to maintaining system performance.

Scale Infrastructure Dynamically: Use cloud services like auto-scaling to adjust resources in real time based on data load. Optimize Data Storage: Implement efficient data storage solutions like partitioning, indexing, and compression to reduce latency. Leverage Caching: Use caching mechanisms to store frequently accessed data for quicker retrieval, reducing system load. Distribute Workloads: Utilize distributed processing systems like Apache Spark to manage large datasets across multiple nodes. Monitor and Tune Performance: Continuously monitor system performance and adjust configurations as needed to handle increasing data volumes.