Scaling Databases in the Cloud Questions and Answers for Viva

Answer-1: Database scaling refers to the process of increasing a database?s capacity to handle more load by adjusting resources in a cloud environment.

Answer-2: Vertical scaling (scaling up) and horizontal scaling (scaling out).

Answer-3: Vertical scaling involves increasing the resources (CPU, RAM, storage) of a single database server.

Answer-4: Horizontal scaling involves adding more database instances or nodes to distribute the load.

Answer-5: Vertical scaling is suitable for small-scale applications or legacy systems where adding nodes is not feasible.

Answer-6: Horizontal scaling is preferred for high-availability and distributed systems requiring fault tolerance and load balancing.

Answer-7: Sharding is a horizontal scaling technique where data is partitioned across multiple databases or servers.

Answer-8: Read replication allows creating read-only copies of a database to distribute read traffic and improve performance.

Answer-9: Write scaling refers to distributing write load, which is complex and often achieved using sharding or partitioned write services.

Answer-10: Amazon Aurora is a cloud-native relational database from AWS that provides high performance and scalability.

Answer-11: Aurora allows automatic read replica creation and storage scaling up to 128 TB.

Answer-12: Amazon RDS is a managed database service that supports multiple engines like MySQL, PostgreSQL, Oracle, and SQL Server.

Answer-13: RDS supports vertical scaling by resizing instances and horizontal scaling using read replicas.

Answer-14: Auto-scaling automatically adjusts resources based on demand without manual intervention.

Answer-15: Amazon Aurora and DynamoDB support auto-scaling.

Answer-16: DynamoDB is a fully managed NoSQL database service by AWS, optimized for key-value and document data structures.

Answer-17: DynamoDB automatically partitions and distributes data across servers to handle large workloads.

Answer-18: The partition key determines the logical division of data and affects how data is distributed for scaling.

Answer-19: Provisioned throughput sets the read/write capacity units which can be scaled as needed.

Answer-20: Google Cloud Spanner is a globally distributed, horizontally scalable relational database.

Answer-21: Cloud Spanner uses synchronous replication and horizontal sharding for global availability and performance.

Answer-22: Azure Cosmos DB is a globally distributed NoSQL database with horizontal scaling and multi-model support.

Answer-23: It offers five consistency levels: Strong, Bounded Staleness, Session, Consistent Prefix, and Eventual.

Answer-24: Caching reduces the load on the database by storing frequently accessed data in memory.

Answer-25: Redis, Memcached, and Amazon ElastiCache.

Answer-26: Multi-AZ (Availability Zone) deployment provides high availability and failover support across different zones.

Answer-27: Clustering involves grouping multiple database servers to act as a single system to improve performance and availability.

Answer-28: Data consistency, latency, partitioning logic, and synchronization between nodes.

Answer-29: CAP theorem states that a distributed system can only guarantee two of three: Consistency, Availability, and Partition Tolerance.

Answer-30: Cloud-native databases are designed with built-in scalability, redundancy, and failover mechanisms.

Answer-31: Connection pooling reuses database connections, reducing overhead and improving scalability.

Answer-32: Each microservice can use its own database, requiring independent scaling and isolation strategies.

Answer-33: Proper indexing improves query performance, reducing load and increasing throughput.

Answer-34: Load balancing distributes requests across multiple database nodes to avoid bottlenecks.

Answer-35: WAL logs all changes before applying them to the database, improving durability and supporting replication.

Answer-36: Aurora Serverless automatically adjusts compute capacity based on actual traffic patterns.

Answer-37: Partitioning splits large tables into smaller, manageable parts to improve performance and scalability.

Answer-38: Eventual consistency allows systems to remain available and scalable, accepting temporary data inconsistency.

Answer-39: Synchronous replication ensures consistency but may introduce latency, limiting scaling performance.

Answer-40: Changes are propagated with delay, offering better performance and scalability at the cost of potential data lag.

Answer-41: Services like Amazon RDS, Google Cloud SQL, and Azure SQL Database handle database management, scaling, and patching.

Answer-42: A scaling bottleneck is a limitation that restricts a database?s ability to handle increasing workload.

Answer-43: Cassandra, MongoDB, Google Cloud Spanner, Amazon DynamoDB.

Answer-44: Monitoring helps identify performance issues and resource limits, enabling proactive scaling decisions.

Answer-45: Cloud bursting dynamically shifts load from an on-premises database to cloud resources during high demand.

Answer-46: They combine on-premises and cloud databases to optimize costs, performance, and compliance.

Answer-47: It?s the delay between writing data on the primary node and its replication to secondary nodes.

Answer-48: Schema-less (NoSQL) databases allow flexibility and easier scaling with changing data structures.

Answer-49: Use auto-scaling, monitor usage, apply indexing, use replication, and design for horizontal growth.

Answer-50: Serverless databases like Aurora Serverless or Firebase scale automatically based on request volume.