Traditional vs. Modern Technology Approaches

Traditional Batch Relational Database Framework	Streaming Real-time Immutability Framework
Data Updates
Batch: Data is processed in batches. Changes to records are typically collected over a period and then applied in bulk during scheduled batch jobs. Batch processing typically involves complex mechanisms (such as record- locking) for synchronizing data across different parts of the system, which can lead to latency and potential data inconsistencies.	Streaming: In FundGuard's streaming architecture approach, data is processed and ingested as a continuous stream. Events are pushed to the system in real time, enabling near-instantaneous processing and analysis. Data updates are processed as they arrive, creating an immutable stream of data records. Each update results in a new version of the data.
Record Edits/Overrides: In this model, records are mutable, meaning they can be updated in place. Changes are directly applied to the existing records, which can lead to issues like overwriting data or inconsistencies during concurrent updates. This model uses less disk space due to fewer records in tables and smaller table structures. Historical data is either captured to a warehouse at specific points in time, or heavy processing is required to un- process balances back to prior dates. Back-dated changes can be difficult.	Record Immutability: In FundGuard's streaming architecture, records are immutable. Once a data entry is created, it cannot be modified. Any changes or updates are represented as new entries with new versions to balance tracking records. This guarantees a clear audit trail and simplifies data integrity. Immutable streaming systems are suitable for scenarios where data audit trails, historical analysis, and real-time analytics are important, such as accounting data, audit logs, and investment event-driven processes.
Controls & Rules
Controls Decoupled from Processes: Controls and exception rules are often applied to batches of data before and after bulk processing. Data violations are only known at the time of the bulk update and not when data entries were made. Control and rule evaluation might be less granular, as batch update designs tend to lower processing time and use less data, rather than individual records.	Streaming Evaluations: Controls and exception rules are applied as data arrives in the stream. Each data record is evaluated against the rules in real time. Controls/rules are applied at the record level, allowing for rapid assessment and action. Controls and rule evaluations tend to be highly granular, as they are applied to more detailed individual big-data records or events as they are ingested.
Processing Time, Speed & Scale
Slower; Must Source Calculation Settings & Edit Records: Processing speed will likely be slower compared to streaming immutable systems due to the relational searching nature of configuration fields, and overhead of record “edit” updates as opposed to record adds. Latency is higher, and updates occur in discrete intervals.	Faster; Record Adds; No Sourcing Overhead: Processing is faster with updates being record adds rather than edits, and applied as soon as they are received. Configuration settings are stored at calculation points. Processing updates are small and frequent in contrast to traditional systems that are mostly stationary and waiting for bulk activities to execute.
Limits: Processing times can suffer with high volumes of data as server CPUs hit their maximums and processing/database calculations are less distributed horizontally. Hardware specs must be based on peak processing usage periods that only occur at a very small period of the day. Changes in business operations (up or down) can require large hardware adjustments/costs.	Near Endless: Streaming systems like FundGuard are highly scalable, distributing processing across multiple nodes or microservices to handle high data volumes. Each microservice can process a specific type of event, leading to flexibility on processing power (elasticity) and helps to alleviate high volume concerns. Cloud-native frameworks like FundGuard can harness the cloud's near endless scalability, allowing client organizations to quickly and easily scale resources up or down as needed.
Testing & Debugging
Many Platform Components; Few Testing Tools: Testing record-edit systems can lead to complex investigations. Discovering what event(s) caused incorrect rolling balances can be difficult due to a lack of easy balance version history. This model tends to require large testing efforts by operational staff whenever new releases are introduced. Getting platform components to work together again after updating one or more of them, can be time- consuming.	All In One Platform; Embedded Testing Tools: Testing event-driven systems can be more complex than traditional systems. Ensuring that events are correctly processed using different sets of configurations, and the system behaves as expected, requires a specialized Test Center where testing tools and approaches let users load and reload their own test scenarios with their desired configurations, and includes an auto verification of test results. FundGuard’s single platform that includes components like: file mapping, exceptions, recon, report center, and test center makes end to end testing/debugging easier.
Data Privacy & Security
Security & Control via Isolation: It is a common misconception that on- premise systems offer more direct control over data and therefore security mechanisms. But employing an on-prem approach requires organizations to manage the security of their physical infrastructure, which is often solved by database isolation and can have negative impacts on resilience measures and longer times-lapses in disaster recovery situations.	More Secure; More Resiliency: Cloud- native systems provide flexibility and scalability with much more focus on third-party access, and employ the best of the public cloud provider's security measures, availability zones and replication standards. Ensuring data privacy and security, especially in multi-tenant environments, includes robust access control mechanisms and data at rest and data in transit encryption.
Connectivity
Brittle Integration Patterns: Often rely on point-to-point integrations or lack robust APIs, where custom code or middleware connects directly to specific third-party systems. Often tightly coupled to specific infrastructure, making it challenging to move or scale.	Active Integration Patterns: Cloud- native applications like FundGuard favor a more modular and decoupled approach. FundGuard uses API gateways, message queues, and event-driven architectures to integrate with third-party applications. FundGuard is containerized, orchestrated, and deployed independently.
Narrow Observability: Monitoring and tracing legacy integrations can be challenging and customized. Outdated security practices often exist, such as hardcoding credentials.	Wide Observability: FundGuard includes robust identity and access management for secure communications. FundGuard is designed with monitoring and observability in mind, making it easier to collect and analyze metrics, logs, and traces.

Traditional Batch Relational Database Framework

Streaming Real-time Immutability
Framework

Data Updates

Batch: Data is processed in batches. Changes to records are typically collected over a period and then applied in bulk during scheduled batch jobs.

Batch processing typically involves complex mechanisms (such as record- locking) for synchronizing data across different parts of the system, which can lead to latency and potential data inconsistencies.

Streaming: In FundGuard's streaming architecture approach, data is processed and ingested as a continuous stream. Events are pushed to the system in real time, enabling near-instantaneous processing and analysis.

Data updates are processed as they arrive, creating an immutable stream of data records. Each update results in a new version of the data.

Record Edits/Overrides: In this model, records are mutable, meaning they can be updated in place. Changes are directly applied to the existing records, which can lead to issues like overwriting data or inconsistencies during concurrent updates.

This model uses less disk space due to fewer records in tables and smaller table structures.

Historical data is either captured to a warehouse at specific points in time, or heavy processing is required to un- process balances back to prior dates.

Back-dated changes can be difficult.

Record Immutability: In FundGuard's streaming architecture, records are immutable. Once a data entry is created, it cannot be modified.

Any changes or updates are represented as new entries with new versions to balance tracking records. This guarantees a clear audit trail and simplifies data integrity.

Immutable streaming systems are suitable for scenarios where data audit trails, historical analysis, and real-time analytics are important, such as accounting data, audit logs, and investment event-driven processes.

Controls & Rules

Controls Decoupled from Processes: Controls and exception rules are often applied to batches of data before and after bulk processing.

Data violations are only known at the time of the bulk update and not when data entries were made.

Control and rule evaluation might be less granular, as batch update designs tend to lower processing time and use less data, rather than individual records.

Streaming Evaluations: Controls and exception rules are applied as data arrives in the stream.

Each data record is evaluated against the rules in real time. Controls/rules are applied at the record level, allowing for rapid assessment and action.

Controls and rule evaluations tend to be highly granular, as they are applied to more detailed individual big-data records or events as they are ingested.

Processing Time, Speed & Scale

Slower; Must Source Calculation Settings & Edit Records: Processing speed will likely be slower compared to streaming immutable systems due to the relational searching nature of configuration fields, and overhead of record “edit” updates as opposed to record adds.

Latency is higher, and updates occur in discrete intervals.

Faster; Record Adds; No Sourcing Overhead: Processing is faster with updates being record adds rather than edits, and applied as soon as they are received. Configuration settings are stored at calculation points.

Processing updates are small and frequent in contrast to traditional systems that are mostly stationary and waiting for bulk activities to execute.

Limits: Processing times can suffer with high volumes of data as server CPUs hit their maximums and processing/database calculations are less distributed horizontally.

Hardware specs must be based on peak processing usage periods that only occur at a very small period of the day.

Changes in business operations (up or down) can require large hardware adjustments/costs.

Near Endless: Streaming systems like FundGuard are highly scalable, distributing processing across multiple nodes or microservices to handle high data volumes.

Each microservice can process a specific type of event, leading to flexibility on processing power (elasticity) and helps to alleviate high volume concerns.

Cloud-native frameworks like FundGuard can harness the cloud's near endless scalability, allowing client organizations to quickly and easily scale resources up or down as needed.

Testing & Debugging

Many Platform Components; Few Testing Tools: Testing record-edit systems can lead to complex investigations.

Discovering what event(s) caused incorrect rolling balances can be difficult due to a lack of easy balance version history.

This model tends to require large testing efforts by operational staff whenever new releases are introduced.

Getting platform components to work together again after updating one or more of them, can be time- consuming.

All In One Platform; Embedded Testing Tools: Testing event-driven systems can be more complex than traditional systems.

Ensuring that events are correctly processed using different sets of configurations, and the system behaves as expected, requires a specialized Test Center where testing tools and approaches let users load and reload their own test scenarios with their desired configurations, and includes an auto verification of test results.

FundGuard’s single platform that includes components like: file mapping, exceptions, recon, report center, and test center makes end to end testing/debugging easier.

Data Privacy & Security

Security & Control via Isolation: It is a common misconception that on- premise systems offer more direct control over data and therefore security mechanisms.

But employing an on-prem approach requires organizations to manage the security of their physical infrastructure, which is often solved by database isolation and can have negative impacts on resilience measures and longer times-lapses in disaster recovery situations.

More Secure; More Resiliency: Cloud- native systems provide flexibility and scalability with much more focus on third-party access, and employ the best of the public cloud provider's security measures, availability zones and replication standards.

Ensuring data privacy and security, especially in multi-tenant environments, includes robust access control mechanisms and data at rest and data in transit encryption.

Connectivity

Brittle Integration Patterns: Often rely on point-to-point integrations or lack robust APIs, where custom code or middleware connects directly to specific third-party systems.

Often tightly coupled to specific infrastructure, making it challenging to move or scale.

Active Integration Patterns: Cloud- native applications like FundGuard favor a more modular and decoupled approach.

FundGuard uses API gateways, message queues, and event-driven architectures to integrate with third-party applications.

FundGuard is containerized, orchestrated, and deployed independently.

Narrow Observability: Monitoring and tracing legacy integrations can be challenging and customized.

Outdated security practices often exist, such as hardcoding credentials.

Wide Observability: FundGuard includes robust identity and access management for secure communications.

FundGuard is designed with monitoring and observability in mind, making it easier to collect and analyze metrics, logs, and traces.