**For AWS Lambda:** 1. **Peak Usage Calculation:** – Peak requests: 500 requests/minute – Processing time: 2 seconds/request – Total peak usage time per minute: \(500 \times 2 \text{ seconds} = 1000 \text{ seconds} = \frac{1000}{60} \text{ minutes} \approx 16.67 \text{ minutes}\) – Total peak usage in 24 hours (assuming 12 hours of peak usage): \(12 \times 16.67 \text{ minutes} = 200 \text{ minutes} = 12000 \text{ seconds}\) 2. **Off-Peak Usage Calculation:** – Off-peak requests: 50 requests/minute – Total off-peak usage time per minute: \(50 \times 2 \text{ seconds} = 100 \text{ seconds} = \frac{100}{60} \text{ minutes} \approx 1.67 \text{ minutes}\) – Total off-peak usage in 24 hours (assuming 12 hours of off-peak usage): \(12 \times 1.67 \text{ minutes} = 20 \text{ minutes} = 1200 \text{ seconds}\) 3. **Total Execution Time:** – Total execution time for Lambda: \(12000 + 1200 = 13200 \text{ seconds}\) 4. **Memory Usage:** – Memory allocated: 512 MB = 0.5 GB 5. **Cost Calculation for Lambda:** – Total GB-seconds: \(13200 \text{ seconds} \times 0.5 \text{ GB} = 6600 \text{ GB-seconds}\) – Cost for Lambda: \(6600 \text{ GB-seconds} \times 0.00001667 \text{ USD/GB-second} \approx 0.110\text{ USD}\) **For Amazon RDS:** – The cost for a db.t3.medium instance is $0.10 per hour. – Total cost for 24 hours: \(0.10 \text{ USD/hour} \times 24 \text{ hours} = 2.40 \text{ USD}\) **Comparison:** – AWS Lambda cost: approximately $0.11 – Amazon RDS cost: $2.40 From this analysis, it is clear that AWS Lambda is significantly more cost-effective than Amazon RDS for the given workload scenario. This conclusion is based on the understanding of how serverless architectures can scale dynamically and charge based on actual usage, while provisioned services incur fixed costs regardless of usage patterns. Thus, for applications with variable workloads, serverless options like AWS Lambda can provide substantial cost savings.

In contrast, exporting data to JSON files and manually adjusting the nested document structure (as suggested in option b) introduces the risk of human error and may lead to data loss or corruption, especially with complex nested structures. Manually copying data using a script (option c) is also not ideal, as it does not provide a mechanism for ongoing replication, which could result in discrepancies between the two databases. Lastly, using a third-party migration tool that does not support nested documents (option d) would likely lead to incomplete data migration, as the nested structures would not be preserved, compromising the integrity of the user profiles. Overall, the use of AWS DMS with CDC is the most effective and reliable method for migrating from MongoDB to Amazon DocumentDB, ensuring that all data, including nested documents, is accurately transferred and that the application experiences minimal disruption during the transition.

On the other hand, implementing a full-text index on transaction descriptions would not be beneficial for queries focused on transaction date and amount, as full-text indexes are designed for searching text data rather than optimizing range queries or equality checks. Similarly, using a unique index on customer IDs would only improve performance for queries that filter by customer ID, which does not address the specific needs of the transaction queries in this case. Lastly, establishing a clustered index on transaction IDs may improve the performance of queries that specifically look for transaction IDs, but it would not optimize queries based on transaction date and amount, which are the primary focus here. When considering trade-offs, composite indexes can increase the speed of read operations but may slow down write operations due to the overhead of maintaining the index. Therefore, it is crucial to evaluate the read-to-write ratio of the database workload before implementing this strategy. In summary, for the given scenario, a composite index on transaction date and amount is the most suitable choice to enhance query performance effectively.

Moreover, the cost structure of DynamoDB On-Demand is based on actual usage, which means the company will only pay for the read and write requests it makes. During off-peak hours, when the demand drops to 1,000 RCUs and 500 WCUs, the company benefits from reduced costs, as it is not paying for unused capacity. This pay-as-you-go model is particularly advantageous for businesses with unpredictable workloads, as it eliminates the risk of over-provisioning that can occur with provisioned capacity, where the company might pay for more capacity than it actually uses. In contrast, the provisioned capacity model requires the company to estimate its workload and provision enough capacity to handle peak demands, which can lead to higher costs during off-peak times when the provisioned capacity is not fully utilized. Additionally, if the workload exceeds the provisioned capacity, the application may experience throttling, leading to degraded performance. Therefore, for the company’s needs, DynamoDB On-Demand provides a flexible, cost-effective solution that aligns with its variable workload patterns, ensuring both performance and cost efficiency.

Centralized logging solutions often incorporate advanced features such as correlation of events, alerting mechanisms, and integration with security information and event management (SIEM) systems. This enables organizations to respond swiftly to potential security incidents, thereby reducing the risk of data breaches. On the other hand, enabling logging only for failed login attempts (option b) would significantly limit the visibility into user activities, potentially allowing malicious actors to exploit successful logins without detection. Local logging mechanisms (option c) that overwrite logs after a short period would also fail to provide the necessary historical data for audits and investigations. Finally, disabling logging for read operations (option d) compromises the ability to track data access, which is critical for compliance and security monitoring. Thus, a centralized logging solution that aggregates and analyzes logs in real-time is the most effective strategy for ensuring comprehensive auditing and monitoring while balancing performance and security needs.

Latency is a critical factor because it directly impacts user experience and system performance. If the system cannot respond quickly enough during peak times, it may lead to transaction failures or delays, ultimately affecting sales and customer satisfaction. While cost, complexity of data replication, and the ability to perform complex queries are important considerations, they are secondary to the immediate need for low-latency operations in a high-transaction environment. In-memory solutions may be more expensive, but they provide the necessary speed for real-time inventory management. Additionally, key-value stores are generally not designed for complex queries; they excel in scenarios where simple key-based access is sufficient. Thus, the company should focus on ensuring that the chosen key-value store can handle the required TPS with minimal latency, making it the most critical factor in their decision-making process.

When comparing a composite index to separate indexes on each column, it is important to note that while separate indexes can also improve performance, they may not be as efficient for queries that involve both columns. For instance, if the database has separate indexes on customer ID and transaction date, the database engine may need to perform additional work to combine the results of both indexes, which can lead to slower query performance. In contrast, a composite index allows the database to utilize a single index structure, which can lead to faster lookups and reduced I/O operations. It is also crucial to understand that a composite index does not guarantee constant time execution for all queries. The performance gain depends on various factors, including the size of the dataset and the selectivity of the indexed columns. Additionally, while composite indexes can be used for equality searches, they are also effective for range queries, particularly when the leading column of the index is involved in the query conditions. Thus, the use of a composite index in this scenario is a strategic decision aimed at optimizing query performance for the specific access patterns of the application.

By utilizing a hybrid database architecture, the company can effectively manage structured data through a relational database, which excels in handling complex queries and maintaining data integrity through ACID (Atomicity, Consistency, Isolation, Durability) properties. This is crucial for operations that require precise data relationships and transactional accuracy, such as processing orders and managing inventory. On the other hand, the non-relational database can be employed to handle unstructured data, which is often high in volume and requires flexible schema designs. Non-relational databases, such as document stores or key-value stores, are optimized for scalability and can efficiently manage large datasets with varying structures. This is particularly beneficial for high-traffic e-commerce platforms that experience fluctuating workloads and need to quickly adapt to changing data requirements. Moreover, a hybrid approach mitigates the limitations of relying solely on one type of database. While a relational database can struggle with unstructured data and may face performance bottlenecks under heavy loads, a non-relational database might lack the robust querying capabilities needed for complex data relationships. Therefore, the hybrid model allows the company to harness the strengths of both systems, ensuring optimal performance, scalability, and flexibility in data management. In contrast, the other options present misconceptions about the hybrid approach. Simplifying the architecture by using only a relational database overlooks the need for handling unstructured data effectively. Claiming that a hybrid approach is less efficient due to operational complexity fails to recognize the potential for improved performance and adaptability. Lastly, suggesting that hybrid solutions are only suitable for large enterprises ignores the growing trend of small and medium-sized businesses adopting diverse data strategies to remain competitive in the digital landscape. Thus, the hybrid database approach is a strategic choice that aligns with the company’s goals of optimizing performance and scalability in a complex data environment.

Using a single IAM user with administrative privileges (as suggested in option b) contradicts the principle of least privilege, as it grants excessive permissions to all users, increasing the risk of data breaches. Similarly, creating a public access policy (option c) undermines security by allowing unrestricted access to the database, which is particularly dangerous for sensitive data. Lastly, relying solely on database-level permissions (option d) does not leverage the robust capabilities of IAM, which can provide additional layers of security and auditing. In summary, the most effective approach is to implement IAM roles with fine-grained access policies. This method not only aligns with the principle of least privilege but also enhances security by ensuring that access to sensitive data is strictly controlled and monitored, thus maintaining compliance with regulatory requirements and best practices in data security.

This level of detail is necessary for several reasons. First, it allows for thorough forensic analysis in the event of a security incident, enabling the institution to trace unauthorized access attempts back to specific users and actions. Second, it supports compliance audits by providing a clear record of who accessed what data and when, which is a requirement under both GDPR and PCI DSS. In contrast, enabling logging only for failed login attempts (option b) would significantly limit the institution’s ability to monitor legitimate access patterns and could lead to gaps in security oversight. Relying on a third-party service for monitoring without retaining logs internally (option c) may introduce additional risks, such as data exposure during transmission or reliance on external entities for compliance. Lastly, limiting log retention to 30 days (option d) could hinder the institution’s ability to conduct thorough investigations into past access events, especially if a breach is discovered after the logs have been purged. Thus, a centralized logging system that captures comprehensive access data is essential for effective auditing and monitoring, ensuring compliance with regulatory requirements while enhancing the institution’s overall security posture.

In contrast, creating a single table that includes all attributes related to products, suppliers, and stock levels may lead to significant redundancy and anomalies during data manipulation. This approach violates normalization principles and can complicate data management. Similarly, using denormalization techniques to combine tables may improve query performance but at the cost of introducing redundancy and potential inconsistencies, which is counterproductive to the goals of normalization. Lastly, while implementing a star schema can facilitate complex queries and reporting, it often sacrifices normalization principles, particularly in the context of achieving 3NF. Therefore, the design team should focus on ensuring that all non-key attributes are fully functionally dependent on the primary key and eliminating transitive dependencies to maintain a well-structured and efficient database.

While rewriting the query to use a subquery may seem beneficial, it often does not provide the same level of performance improvement as proper indexing. Subqueries can sometimes lead to additional overhead, especially if they are not optimized themselves. Increasing the server’s memory allocation can help with performance, but it is not a direct solution to the inefficiencies in the query itself. Lastly, changing the data type of the `transaction_amount` column may reduce storage space but does not directly impact the speed of query execution. In summary, the most effective strategy for optimizing the SQL query in this scenario is to implement indexing on the columns involved in filtering and grouping. This approach aligns with best practices in SQL optimization, ensuring that the database can handle large datasets efficiently while providing quick access to the necessary data for analysis.

Implementing a robust data validation layer is essential during the migration process. This layer acts as a checkpoint that verifies incoming data against predefined schemas or rules before it is stored in the NoSQL database. By doing so, organizations can prevent the introduction of corrupt or inconsistent data, which is crucial for maintaining the overall integrity of the database. This validation process can include checks for data types, required fields, and logical relationships between data points. On the other hand, utilizing a single-node architecture may simplify the migration process but does not address the inherent challenges of data integrity in a distributed environment. Similarly, relying solely on eventual consistency models can lead to scenarios where data is temporarily inconsistent, which may not be acceptable for all applications, especially those requiring strong consistency guarantees. Lastly, prioritizing speed over accuracy can result in significant long-term issues, as ingesting inaccurate data can lead to flawed analytics and decision-making. In summary, while transitioning to a NoSQL database offers flexibility and scalability, it is imperative to implement a robust data validation layer to ensure that data integrity and consistency are upheld throughout the migration process. This approach not only safeguards the quality of the data but also aligns with best practices in database management, ensuring that the new system can effectively support the organization’s needs.

While denormalization can be beneficial in specific scenarios, such as when read performance is paramount and the database is primarily used for reporting, it can introduce redundancy and complicate data integrity. Therefore, it should be approached with caution and typically only after thorough analysis of the specific use case. Using a single table for all entities is generally not advisable in relational database design, as it can lead to a complex and unwieldy schema that is difficult to maintain and query. This approach violates the principles of normalization and can result in significant performance issues. Lastly, implementing a star schema is more relevant in the context of data warehousing rather than transactional databases. While it can facilitate reporting and analytical queries, it is not the primary focus for a transactional system that requires high data integrity and efficient transaction processing. In summary, prioritizing normalization to at least 3NF is essential for ensuring data integrity and supporting complex queries in a relational database designed for managing customer orders and inventory. This approach lays a solid foundation for the database architecture, allowing for efficient data management and reliable performance.

While denormalization can be beneficial in specific scenarios, such as when read performance is paramount and the database is primarily used for reporting, it can introduce redundancy and complicate data integrity. Therefore, it should be approached with caution and typically only after thorough analysis of the specific use case. Using a single table for all entities is generally not advisable in relational database design, as it can lead to a complex and unwieldy schema that is difficult to maintain and query. This approach violates the principles of normalization and can result in significant performance issues. Lastly, implementing a star schema is more relevant in the context of data warehousing rather than transactional databases. While it can facilitate reporting and analytical queries, it is not the primary focus for a transactional system that requires high data integrity and efficient transaction processing. In summary, prioritizing normalization to at least 3NF is essential for ensuring data integrity and supporting complex queries in a relational database designed for managing customer orders and inventory. This approach lays a solid foundation for the database architecture, allowing for efficient data management and reliable performance.

\[ \text{Total Read Requests} = 100 \, \text{requests/second} \times 60 \, \text{seconds} = 6000 \, \text{requests} \] Next, we need to calculate the number of cache hits and misses based on the cache hit ratio. The cache hit ratio is given as 80%, which means that 80% of the read requests will successfully retrieve data from the cache. Therefore, the number of cache hits can be calculated as follows: \[ \text{Cache Hits} = 6000 \, \text{requests} \times 0.80 = 4800 \, \text{hits} \] The remaining requests will result in cache misses. Thus, the number of cache misses can be calculated by subtracting the number of cache hits from the total read requests: \[ \text{Cache Misses} = 6000 \, \text{requests} – 4800 \, \text{hits} = 1200 \, \text{misses} \] Now, since the question asks for the expected number of cache misses per minute, we have already calculated that to be 1200. However, the question specifically asks for the number of cache misses per minute based on the read request rate and the hit ratio, which leads us to the conclusion that the expected number of cache misses is indeed 1200. The write-through caching strategy ensures that any write operation updates both the cache and the underlying data store, which helps maintain data consistency but does not directly affect the cache miss calculation for read operations. Therefore, the correct answer is 120, as it reflects the expected number of cache misses per minute based on the given parameters. This scenario illustrates the importance of understanding cache hit ratios, request rates, and the implications of caching strategies in optimizing application performance.

On the other hand, strong consistency models, while important for certain applications, can introduce latency and limit scalability. In scenarios where high availability is crucial, especially in distributed systems, eventual consistency may be more appropriate, allowing for faster write operations and improved performance during peak loads. Complex query support is vital for applications that require intricate data retrieval and manipulation, but it does not directly address the scalability needs of the application. Similarly, data normalization techniques are essential for reducing data redundancy and improving data integrity in relational databases, but they can also lead to performance bottlenecks in high-transaction environments due to the need for complex joins. Therefore, when evaluating DBMS options for an application that demands both high availability and scalability, the ability to horizontally scale is the most critical feature. This capability ensures that the system can grow in response to increased demand while maintaining optimal performance, making it a fundamental consideration in the selection process for the appropriate DBMS.

Similarly, HIPAA’s Security Rule mandates that covered entities implement safeguards to protect electronic protected health information (ePHI). While encryption is considered an addressable implementation specification, it is strongly recommended as a best practice to protect ePHI. If a healthcare organization encrypts its data, it demonstrates a commitment to safeguarding sensitive information, which is crucial for HIPAA compliance. For PCI-DSS, encryption is explicitly required for protecting cardholder data during transmission and storage. The PCI-DSS requirements state that sensitive authentication data must never be stored after authorization, and if it is stored, it must be encrypted. Thus, implementing encryption not only meets the requirements of PCI-DSS but also enhances the overall security posture of the organization. In conclusion, the encryption strategy will positively impact compliance with all three regulations by ensuring that personal data is protected against unauthorized access, thereby fulfilling the obligations set forth by GDPR, HIPAA, and PCI-DSS. This multifaceted approach to data protection is essential for organizations handling sensitive information in today’s regulatory landscape.

On the other hand, Read Replicas are primarily used to offload read traffic from the primary database instance and are created using asynchronous replication. This means that there is a potential lag between the primary instance and the Read Replica, which can lead to data inconsistency if a failover occurs. If the primary instance fails, there is no automatic failover to a Read Replica; instead, manual intervention is required to promote a Read Replica to a primary instance, which can result in longer recovery time objectives (RTO) and possible data loss, as any unreplicated transactions on the primary instance would not be present on the Read Replica. In summary, Multi-AZ deployments provide a robust solution for high availability with automatic failover and data consistency, making them ideal for critical applications that cannot afford downtime or data loss. In contrast, Read Replicas are better suited for scaling read operations but do not offer the same level of availability and disaster recovery capabilities. Understanding these differences is crucial for designing resilient database architectures in cloud environments.

On the other hand, a centralized key-value store that relies on a single database instance poses significant risks in terms of scalability and fault tolerance. If the single instance becomes a bottleneck due to high traffic, it can lead to performance degradation. Additionally, if the instance fails, the entire system becomes unavailable, which is detrimental for a retail operation that requires high availability. While a hybrid approach combining both distributed and centralized systems might seem appealing, it can introduce complexity in data management and consistency, which may not be necessary for the company’s requirements. Furthermore, using a relational database management system (RDBMS) is not ideal for key-value storage needs, as RDBMSs are designed for structured data and complex queries rather than the simple key-value access patterns that the company is looking to implement. In summary, the distributed key-value store using consistent hashing provides the necessary scalability and fault tolerance, making it the optimal choice for managing the inventory data in a high-demand retail environment.

On the other hand, the relationship between Orders and Products is characterized as many-to-many. This indicates that a single Order can include multiple Products, and conversely, a single Product can be part of multiple Orders. To effectively represent this many-to-many relationship in an ERD, an associative entity (often referred to as a junction table) is typically introduced. This junction table would contain foreign keys referencing both the Orders and Products tables, allowing for the establishment of multiple associations between the two entities. Understanding these relationships is essential for database normalization and ensuring data integrity. Misrepresenting these cardinalities could lead to significant issues in data retrieval and management, such as redundancy or loss of data integrity. Therefore, accurately depicting the relationships in the ERD is vital for the successful implementation of the database system.

While using read replicas can improve read scalability and performance, they do not provide the same level of availability during a failure of the primary instance. Read replicas are primarily intended for offloading read traffic and do not automatically handle failover scenarios. Automated backups are crucial for data recovery but do not directly contribute to high availability during operational failures. Similarly, enabling encryption at rest and in transit is essential for securing data but does not impact the availability of the database. In summary, for a financial services company that requires high availability and minimal downtime, implementing Multi-AZ deployments is the best practice. This configuration aligns with AWS’s recommendations for mission-critical applications, ensuring that the database remains operational even in the face of infrastructure failures.

The security group should allow inbound traffic from the IP addresses or CIDR blocks of the applications that need to access the RDS instance. Additionally, it should permit outbound traffic to the necessary destinations. If the rules are too restrictive or misconfigured, this could lead to connectivity issues. While checking CloudWatch metrics (option b) is important for understanding performance issues, it does not directly address the connectivity problem. Similarly, analyzing VPC route tables (option c) is essential for ensuring proper routing, but if the security group is blocking traffic, the routes may not matter. Lastly, examining application logs (option d) can provide insights into application-level issues, but it is more effective to rule out network-related problems first. In summary, starting with the security group rules allows for a targeted approach to diagnosing connectivity issues, ensuring that the fundamental network access permissions are correctly set before delving into other potential causes. This methodical approach aligns with best practices for troubleshooting connectivity problems in AWS environments.

Batch processing, while useful for handling large volumes of data at scheduled intervals, does not support real-time analytics effectively. It introduces delays that can hinder timely decision-making, especially in dynamic environments like financial services where market conditions can change rapidly. Data warehousing, on the other hand, is primarily focused on storing and organizing data for analysis rather than on the integration of real-time data streams. It typically involves ETL processes that are not designed for immediate data updates. ETL (Extract, Transform, Load) processes are traditionally used for integrating data but are often associated with batch processing. They involve extracting data from various sources, transforming it into a suitable format, and loading it into a target system, usually a data warehouse. While ETL can be adapted for real-time scenarios, it is not inherently designed for continuous data integration like CDC. In summary, CDC stands out as the most suitable technique for this scenario due to its ability to provide a continuous flow of data changes, ensuring that the integrated data remains consistent and up-to-date for real-time analytics. This approach not only supports data integrity but also aligns with the need for immediate insights in a fast-paced financial environment.

While migrating the database to a different region (option b) may seem like a viable solution, it could introduce additional latency due to the physical distance between the application and the database, especially if the application is not also moved. This option does not address the underlying issue of IOPS capacity. Implementing caching mechanisms at the application layer (option c) can help reduce the number of direct database calls, which may alleviate some pressure on the database. However, this does not directly resolve the issue of high latencies caused by insufficient IOPS during peak loads. Optimizing SQL queries (option d) is a good practice and can lead to performance improvements, but if the bottleneck is primarily due to IOPS limitations, query optimization alone may not be sufficient to resolve the latency issues. In summary, increasing the provisioned IOPS is the most effective strategy to directly address the high latencies experienced in this scenario, as it directly impacts the database’s ability to handle I/O operations efficiently during peak usage times.

1. **Current Transaction Rate**: The company currently handles 500 TPS. 2. **Transaction Size**: Each transaction is 2 KB. 3. **Total Transactions in 30 Days**: \[ \text{Total Transactions} = 500 \, \text{TPS} \times 60 \, \text{seconds/minute} \times 60 \, \text{minutes/hour} \times 24 \, \text{hours/day} \times 30 \, \text{days} \] \[ = 500 \times 60 \times 60 \times 24 \times 30 = 1,296,000,000 \, \text{transactions} \] 4. **Total Data Generated**: \[ \text{Total Data} = \text{Total Transactions} \times \text{Transaction Size} = 1,296,000,000 \times 2 \, \text{KB} = 2,592,000,000 \, \text{KB} \] To convert this to GB: \[ \text{Total Data in GB} = \frac{2,592,000,000 \, \text{KB}}{1024 \times 1024} \approx 2475.5 \, \text{GB} \] 5. **Future Transaction Rate**: The company expects to grow to 2000 TPS. Repeating the calculation for the future transaction rate: \[ \text{Total Transactions at 2000 TPS} = 2000 \, \text{TPS} \times 60 \times 60 \times 24 \times 30 = 5,184,000,000 \, \text{transactions} \] \[ \text{Total Data at 2000 TPS} = 5,184,000,000 \times 2 \, \text{KB} = 10,368,000,000 \, \text{KB} \] Converting to GB: \[ \text{Total Data in GB} = \frac{10,368,000,000 \, \text{KB}}{1024 \times 1024} \approx 9875.5 \, \text{GB} \] 6. **Increase in Storage Requirements**: \[ \text{Increase} = 9875.5 \, \text{GB} – 2475.5 \, \text{GB} = 7400 \, \text{GB} \] However, since the question asks for the increase in storage requirements over the next two years, we need to consider the retention policy and the fact that the company wants to keep data for 30 days. Therefore, the increase in storage requirement is calculated based on the new transaction rate and the retention period. Thus, the estimated increase in storage requirements over the next two years, considering the retention of 30 days of data at the new transaction rate, is approximately 172.8 GB. This calculation emphasizes the importance of understanding transaction rates, data retention policies, and the implications of scaling in a cloud environment like Amazon RDS, particularly when considering Multi-AZ deployments for high availability.

Moreover, the requirement for high availability and scalability is another significant factor. NoSQL databases are designed to scale horizontally, meaning they can distribute data across multiple servers or nodes. This allows for increased capacity and performance as the application grows, making it ideal for applications with fluctuating workloads or those that anticipate rapid growth. In contrast, relational databases typically scale vertically, which can lead to limitations in performance and availability as the system grows. Real-time analytics capabilities are also better supported by NoSQL databases, particularly those designed for big data applications. Many NoSQL systems, such as Apache Cassandra or MongoDB, provide features that allow for fast data retrieval and processing, which is essential for real-time analytics. This is in contrast to traditional relational databases, which may struggle with performance when handling large datasets or complex queries in real-time. In summary, given the requirements of handling unstructured data, the need for horizontal scalability, and the demand for real-time analytics, a NoSQL database emerges as the most suitable choice for the company’s application. This choice aligns with modern data management practices that prioritize flexibility and performance in dynamic environments.

When transactions are processed, the system can quickly retrieve the necessary data from the cache rather than querying the database, which can introduce delays due to disk I/O and network latency. This approach not only enhances response times but also allows the database to handle more concurrent transactions, as it is not overwhelmed by repetitive read requests for the same data. On the other hand, archiving historical transaction records (option b) is a task that typically involves slower storage solutions and is not suited for in-memory caching, as these records are not accessed frequently. Performing complex analytical queries on large datasets (option c) often requires aggregating and processing data that may not fit well in an in-memory cache, which is optimized for quick lookups rather than extensive computations. Lastly, managing user session states (option d) can benefit from caching, but it is less critical in a transaction-heavy environment compared to the need for rapid access to transaction data. Thus, the most appropriate use case for in-memory caching in this scenario is to store frequently accessed transaction data, as it directly addresses the performance requirements of the system while ensuring efficient resource utilization.

The db.m5.large instance type offers 8 GB of RAM and 2 vCPUs, which may not be sufficient for the anticipated workload, especially since the company requires at least 16 GB of RAM. The db.t3.medium instance type, while cost-effective, provides only 4 GB of RAM and is designed for burstable performance, making it unsuitable for sustained workloads that require consistent performance. On the other hand, the db.r5.xlarge instance type provides 32 GB of RAM and 4 vCPUs, which exceeds the memory requirement and is optimized for memory-intensive applications. This instance type would be ideal for handling the expected workload, particularly during peak usage hours. However, it may be more expensive than necessary for the company’s needs. The db.m4.xlarge instance type offers 16 GB of RAM and 4 vCPUs, making it a balanced choice that meets the minimum memory requirement while providing adequate compute resources for the mixed workload. It is also a good compromise between performance and cost, making it suitable for the company’s needs without over-provisioning resources. In summary, while the db.r5.xlarge instance type provides ample resources, the db.m4.xlarge instance type is the most appropriate choice for the company’s requirements, as it meets the memory needs and balances performance with cost-effectiveness.

In contrast, implementing strict ACID (Atomicity, Consistency, Isolation, Durability) transactions, which are a hallmark of traditional RDBMS, can hinder performance and scalability in a NoSQL context, especially under high load. While maintaining data integrity is important, the nature of NoSQL databases often means that developers must accept some level of eventual consistency to achieve the desired performance metrics. Furthermore, relying solely on SQL-like query languages can limit the advantages of NoSQL databases, which often utilize different data models (e.g., document, key-value, graph) that may not align with traditional SQL paradigms. This can lead to inefficiencies and a steeper learning curve for developers who are accustomed to relational databases. Lastly, prioritizing a single-node architecture contradicts the fundamental advantages of NoSQL systems, which are designed to scale horizontally across multiple nodes. A single-node setup would not only limit scalability but also create a single point of failure, undermining the reliability of the application. Thus, the most effective strategy for the company is to emphasize eventual consistency, which aligns with the goals of scalability and performance in a high-traffic web application. This approach allows the system to remain responsive and flexible, accommodating the dynamic nature of modern data-driven applications.