How DevOps and Rapid Containerization Saved 70% Development Time and Reduced 45% Infra Cost

Case Study

How DevOps and Rapid Containerization Saved 70% Development Time and Reduced 45% Infra Cost Savings

Overview

Why product engineering teams need DevOps experts who are hands-on with rapid container deployments and orchestration?

The use of containers binds together software development and operational IT skills. It requires the ability to encapsulate code together with libraries and dependencies.  

Some Example Microservices and Dependencies:

  1. Customer Loan Account Management: This microservice handles account creation, modification, credit history mapping, collateral data, etc. It requires knowledge of data querying (e.g., PostgreSQL) to store or retrieve account information.
  2. Collateral Processing Microservice: This microservice manages collateral processing, including credit-check analysis, bill payments, and transaction history retrieval. It may utilize messaging queues (e.g., Apache Kafka) for asynchronous communication.
  3. Authentication Microservice: This microservice handles user authentication and authorization. It may rely on authentication libraries (e.g., OAuth 2.0) for identity management.

Application containerization is effective for recurring background processes involving batch jobs and database jobs. With application containerization, each job can run without interrupting other data-intensive jobs happening simultaneously.

Skills required for containerization?

Along with expertise in handling platforms like Kubernetes for container orchestration, you also need hands-on experience in container distribution management and in enabling hardened API endpoints.

Our ability to spin up new container instances helps run multiple application testing projects in parallel. Our DevOps Engineers are adept at standing-up similar runtime environments – mirroring and production without impacting any other process. Container orchestration is also the key to maintaining uniformity in development, test, and production environments. Our knowledge of code reusability ensures components are used multiple times in many different applications thereby also speeding up developers’ ability to build, test, deploy, and iterate.

The Challenge

Monolithic Architecture

  • The legacy IT system was a rigid monolith running on legacy programming language that did not support new age experiences and struggled to meet compliance
  • The existing monolithic architecture posed challenges in deployment, scalability, and reliability
  • Deploying updates or new features required deploying the entire application, leading to longer release cycles and increased risk of downtime

Limited Scalability

  • Scaling the monolithic application horizontally was difficult, as the entire application had to be replicated to handle increased load.
  • This resulted in inefficiencies and higher infrastructure costs.

Reliability Concerns

  • Monolithic applications are more prone to failures, as a single bug or issue in one part of the application can affect the entire system
  • It can lead to service disruptions and customer dissatisfaction.

Migration planning and high availability

  • Migrating a specific function to an individual microservice requires expert assessment of reusable components, code libraries, and other dependencies that can be clubbed together
  • It is essential to monitor containerized environments to ensure peak performance levels by collecting operational data in the form of logs, metrics, events, and traces

Solution

Decomposition of Monolith: Identified and decomposed monolithic components into smaller, loosely coupled microservices based on business capabilities, allowing for independent development, deployment, and scaling.

Containerization of Microservices: Packaged each microservice and its dependencies into separate containers using Docker, ensuring consistency and portability across development, testing, and production environments.

Orchestration with Kubernetes: Deployed microservices on a Kubernetes cluster to automate container orchestration, scaling, and management, enabling seamless deployment and efficient resource utilization.

Service Mesh Implementation: Implemented a service mesh to manage inter-service communication, monitor traffic, enforce security policies, and handle service discovery, improving reliability and fault tolerance.

CI/CD Pipeline Integration: Established CI/CD pipelines to automate the build, test, and deployment processes for microservices, ensuring rapid and reliable software delivery while minimizing manual intervention.

 

How we Helped

  • Our domain-driven design approach helped define the boundaries of the microservice from a business point of view
  • As each microservice was getting assigned to a different container resulting in a large modular architecture, we structured its management and orchestration
  • Managed Kubernetes enabled optimal pod distribution amongst the nodes
  • Observability generated data to show how much resources would any container optimally need
  • Enabled visualization on the number of clusters, nodes, pods, and other resources for each container
  • Imparted training sessions to learn about containerization tools like Docker and Kubernetes, fostering teamwork across departments
  • The shift to containerization encouraged staff to try new methods, share insights, and continuously learn from each other
  • Regular feedback sessions allowed teams to voice concerns, suggest improvements, and refine containerization strategies over time
  • Milestones in containerization progress leading to new application feature release is speeding modernization initiatives

Impact

  • Weeks, not months is the new normal for launching new applications
  • 70% decrease in the time taken for testing and infrastructure provisioning
  • Zero downtime experienced when releasing a new feature in the live environment
  • USD 40,000 saved in operating costs through optimized infrastructure management
  • 45% cost savings in infrastructure and IT operations costs other spent on expensive resources
  • 999% uptime enabled for the applications with use of optimized container orchestration

Benefits

Simplified Deployment: With microservices, deploying updates became easier. Each service can be updated independently, cutting release times and downtime.

Enhanced Scalability: Microservices allow for flexible scaling of services, reducing costs and optimizing resources as needed. Improved Reliability: By separating services and using a service mesh, the system became more reliable, with fewer disruptions and better user experiences.

Agility and Innovation: Microservices and CI/CD enable quick experimentation and deployment of new features, keeping the customer competitive. Cost Efficiency: Microservices and containerization save costs by using resources more efficiently and reducing downtime expenses.

Containerization

 

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Impact

How to Ensure HIPAA Compliance When Migrating to Office 365 from Google Workspace, Box, GoDaddy, Citrix or any Similar Platform

Case Study

How to Ensure HIPAA Compliance When Migrating to Office 365 from Google Workspace, Box, GoDaddy, Citrix or any Similar Platform

Overview

Incorrect configuration of Microsoft 365 can lead to non-compliance of HIPAA or Health Insurance Portability and Accountability Act. Ensuring complete adherence to Family Educational Rights and Privacy Act (FERPA) regulations is another important checkbox that a migration plan must cover.

Here are some key steps to ensure HIPAA Compliance with Microsoft 365:

Data Encryption: Encrypt data at rest and in motion on the server

Use a valid SSL certificate: Ensure the Exchange Server has a valid SSL certificate from a trusted authority

Enable Outlook Anywhere: Ensure Outlook Anywhere is enabled and configured properly

Ensure Autodiscover works: Ensure Autodiscover works

Use Microsoft Entra ID: Use Microsoft Entra ID to implement HIPAA safeguards

Check Microsoft 365 subscription: Ensure the Microsoft 365 subscription includes the necessary HIPAA compliance features

Configure security and compliance settings: Configure the necessary security and compliance settings in the Compliance Center

Your migration partner must be mindful of documenting all movement, handling, and alterations made to the data while the migration is underway.

The Challenge

Storage limitations, limited archiving capabilities, and moving over to Microsoft 365 from on premise email exchange are some a key reasons to migrate. End-Of-Life (EOL) and Microsoft Exchange On-premise protocols getting phased are also a big motivation factor.

The constant need to calculate what it costs to support massive volumes of email traffic is influencing migration decision making. But no matter what, the reasons,

Let’s take a look at the other technical challenges often encountered with Office 365 Migration:

  • Many special character from platforms such as Google Workspace are unsupported in Microsoft 365
  • Errors can arise if the folder names and files are unsupported in Microsoft 365
  • Challenges arise when transfer file size packages exceed limits set by Microsoft 365
  • Request limits and API throttling needs to be understood before starting any migration
  • File permission access and user data access requires rigorous permission mapping exercise

Migration Methodology & Approach

Assessment and Planning:

    • Our Migration Specialists will conduct a comprehensive assessment of the existing platform environment, including user accounts, data volume, configurations, and integrations.
    • Develop a detailed migration plan outlining the sequence of tasks, timelines, resource requirements, and potential risks.
    • Coordinate with stakeholders to gather requirements and expectations for the Office 365 environment.

Data Migration:

    • Transfer user emails, calendars, contacts, and other relevant data from platforms like Google Workspace to Office 365 using appropriate migration tools and methods.
    • Migrate shared drives, documents, and collaboration spaces to corresponding Office 365 services (e.g., SharePoint Online, OneDrive for Business, Teams).

Configuration and Customization:

    • Configure Office 365 tenant settings, user accounts, groups, and permissions to mirror the existing Google Workspace setup.
    • Implement custom configurations, policies, and security settings as per client’s requirements.
    • Integrate Office 365 with existing IT infrastructure, applications, and third-party services as necessary.

Training and Support:

    • Provide training videos and documentation (Microsoft content) to familiarize users with Office 365 applications, features, and best practices.
    • Offer ongoing support and assistance to address user queries, issues, and feedback during and after the migration process.

Testing and Validation:

    • Conduct thorough testing of the migrated data and functionalities to ensure accuracy, completeness, and integrity.
    • Perform user acceptance testing (UAT) to validate that all required features and functionalities are working as expected.
    • Address any discrepancies or issues identified during testing and validation.

Deployment and Go-Live:

    • Coordinate with the client’s IT team and stakeholders to schedule the deployment of Office 365 services and finalize the transition.
    • Monitor the migration process during the go-live phase and address any issues or concerns in real-time.
    • Provide post-migration support and follow-up to ensure a successful transition to Office 365.

Key Considerations for Maintaining HIPAA Compliance

Business Associate Agreement (BAA): Ensure your Microsoft migration partner signs a Business Associate Agreement (BAA). This agreement establishes the responsibilities of Microsoft as a HIPAA business associate, outlining their obligations to safeguard protected health information (PHI).

Data Encryption: Utilize encryption mechanisms, such as Transport Layer Security (TLS) or BitLocker encryption, to protect PHI during transmission and storage within Office 365.

Access Controls: Implement strict access controls and authentication mechanisms to ensure that only authorized personnel have access to PHI stored in Office 365. Utilize features like Azure Active Directory (AAD) for user authentication and role-based access control (RBAC) to manage permissions.

Data Loss Prevention (DLP): Configure DLP policies within Office 365 to prevent unauthorized sharing or leakage of PHI. DLP policies can help identify and restrict the transmission of sensitive information via email, SharePoint, OneDrive, and other Office 365 services.

Audit Logging and Monitoring: Enable audit logging within Office 365 to track user activities and changes made to PHI. Regularly review audit logs and implement monitoring solutions to detect suspicious activities or unauthorized access attempts.

Secure Email Communication: Implement secure email communication protocols, such as Secure/Multipurpose Internet Mail Extensions (S/MIME) or Microsoft Information Protection (MIP), to encrypt email messages containing PHI and ensure secure transmission.

Data Retention Policies: Define and enforce data retention policies to ensure that PHI is retained for the required duration and securely disposed of when no longer needed. Use features like retention labels and retention policies in Office 365 to manage data lifecycle.

Mobile Device Management (MDM): Implement MDM solutions to enforce security policies on mobile devices accessing Office 365 services. Use features like Intune to manage device encryption, enforce passcode policies, and remotely wipe devices if lost or stolen.

Training and Awareness: Provide HIPAA training and awareness programs to employees who handle PHI in Office 365. Educate them about their responsibilities, security best practices, and how to identify and respond to potential security incidents.

Regular Risk Assessments: Conduct regular risk assessments to identify vulnerabilities and risks associated with PHI in Office 365. Address any identified gaps or deficiencies promptly to maintain HIPAA compliance.

Proven Migration Experience

  • 100+ Migration projects involving 50 to 10,000 users
  • 80% reduction in time and costs
  • 8TB to 30TB data migration volumes handled
  • 80% elimination of expensive backups and migration cost
Cloud Migration

 

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How We Enabled 50% Reduction in Product Release Cycles with Our DevOps and DataOps Services

Case Study

How We Enabled 50% Reduction in Product Release Cycles with Our DevOps and DataOps Services

Overview

Lack of DataOps skills can become an impediment for release engineers who have to manage tight deployment windows. The release engineers of one of our Banking Clients faced a similar situation and were constantly challenged by errors arising from automated release of a database and related application codes.

Without knowledge of automated tools, Developers have to make backups manually before releasing any new change, while storing data in the event of a failure. With growing volumes of data these Data Operations can get immensely expensive and time consuming. The need of the hour was to reduce valuable time, money, and effort spent on error-handling and rollbacks. This also meant onboarding experienced DevOps engineers who can write software extensions for connecting new digital banking services to the end customer. The skills involved included knowledge of continous automated testing and the ability to quickly replicate infrastructure for every release.

Our Solution: Conquering DevOps for Data with Snowflake

  • Reduces schema change frequency
  • Enables development in preferred programming languages
  • Supports SQL, Python, Node.Js, Go, .NET, Java among others
  • Automates Data Cloud implementation automates DevOps tasks
  • Helps build ML workflows with faster data access and data processing
  • Powers developers to easily build data pipelines in Python, Java, etc.
  • Enables auto-scale features using custom APIs for AWS and Python

Challenge

Automated release of database and related application code were building up several challenges, including:

Data Integrity Issues: Automated releases may lead to unintended changes in database schema or data, causing data integrity issues, data loss, or corruption.

Downtime and Service Disruption: Automated releases may result in downtime or service disruption if database migrations or updates are not handled properly, impacting business operations and customer experience.

Performance Degradation: Automated releases may inadvertently introduce performance bottlenecks or degrade database performance if changes are not thoroughly tested and optimized.

Dependency Management: Automated releases may encounter challenges with managing dependencies between database schema changes and application code updates, leading to inconsistencies or deployment failures.

Rollback Complexity: Automated releases may complicate rollback procedures, especially if database changes are irreversible or if application code relies on specific database states.

Security Vulnerabilities: Automated releases may introduce security vulnerabilities if proper access controls, encryption, or data protection measures are not implemented or properly configured.

Compliance and Regulatory Risks: Automated releases may pose compliance and regulatory risks if changes are not audited, tracked, or documented appropriately, potentially leading to data breaches or legal consequences.

Testing Overhead: Automated releases may require extensive testing to validate database changes and application code updates across various environments (e.g., development, staging, production), increasing testing overhead and time-to-release.

Version Control Challenges: Automated releases may encounter challenges with version control, especially if database changes and application code updates are managed separately or if versioning is not synchronized effectively.

Communication and Collaboration: Automated releases may strain communication and collaboration between development, operations, and database administration teams, leading to misalignment, misunderstandings, or conflicts during the release process.

How We Helped

  • Our Developers helped stand-up multiple isolated, ACID-compliant, SQL-based compute environments as needed
  • Toolset expertise eliminated the time and effort spent on procuring, creating, and managing separate IT or multi-cloud environments
  • We helped automate the entire process of creating new environments, auto-suspend idle environments
  • Enabled access to live data from a provider account to one or many receiver/consumer accounts
  • Our solution creates a copy of the live data instantly in metadata, without the need to duplicate

The Impact

  • 40% improvement in storage costs and time spent on seeding preproduction environment
  • 80% reduction in time spent on managing infrastructure, installing patches, and enabling backups
  • 80% of time and effort saved in enabling software updates so that all environments run the latest security updates
  • 80% elimination of expensive backups required to restore Tables, Schemas, and Databases that have been changed or deleted
DevOps with Snowflake

 

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Integration Challenges Solved: Contract Driven Development and API Specifications to Fulfill Executable Contracts

Case Study

Integration Challenges Solved: Contract Driven Development and API Specifications to Fulfill Executable Contracts

Overview

There are several challenges to integration testing that can be solved using Contract-Driven Development and API Testing. Using this methodology, our experts ensure testing of integration points within each application are performed in isolation. We check if all messages sent or received through these integration points are in conformance of the documentation or contract.

A contract is a mutually agreed API specification that brings consumers and providers on the same page. What however makes contract-driven API development complex is the way data is often interpreted by both the provider and consumer.

Let’s consider an example where two microservices, Order Service and Payment Service, need to exchange data about an order. The Order Service provides the order details, including the total amount and customer information, while the Payment Service processes payments.

Typical Scenario: When the Order Service sends the order amount as a floating-point number (e.g., 99.99), but the Payment Service expects the amount as an integer representing cents (e.g., 9999).

Expertise Required:

API Contract: Define the API contract specifying that the order amount is sent as a string representing the amount in cents (e.g., “9999”).

Data Transformation: Implement a data transformation layer that converts the floating-point number to the expected integer format before sending the data to the Payment Service.

Validation: Add validation checks to ensure that the order amount is in the correct format before processing payments.

Our Solution: Enabling API Specifications as Executable Contracts

  • Enabled adherence of API specification as an executable contract
  • Defined API specifications at a component level for consumer and provider applications
  • Deployed API specifications as contract test cases
  • Leveraged Automation Testing Tools to check backward compatibility with existing API Consumers/Clients
  • Automated creation of new connections and test cases on introduction of new environment
  • Built API Specifications that are machine learning parsable codes stored in a central version control system

Challenge

Semantic Differences:

  • Microservices may have different interpretations of the same data types, leading to semantic mismatches.
  • For example, one service may interpret a “date” as a Unix timestamp, while another may expect a date in a specific format.

Data Serialization:

  • When services communicate over the network, data must be serialized and deserialized.
  • Different serialization frameworks or libraries may handle data types differently, causing mismatches.

Language-Specific Data Types:

  • Microservices may be implemented in different programming languages with their own data type systems.
  • For example, a string in one language may not map directly to the string type in another language.
  • Versioning and Evolution:
  • Changes to data types over time can lead to compatibility issues between different versions of microservices
  • Adding new fields or changing existing data types can break backward compatibility

Null Handling:

  • Null values may be handled differently across services, leading to unexpected behavior
  • Some services may expect null values, while others may not handle them gracefully

How We Helped

API Contract and Documentation:

  • Clearly defined and document API contracts with agreed-upon data types
  • Specify data formats, units, and constraints in API documentation to ensure consistency

Use Standardized Data Formats:

  • Adopt standardized data formats like JSON Schema or OpenAPI to describe API payloads.
  • Standard formats help ensure that all services understand and interpret data consistently.

Data Transformation Layers:

  • Implement data transformation layers or microservices responsible for converting data between different formats
  • Use tools like Apache Avro or Protocol Buffers for efficient data serialization and deserialization

Shared Libraries or SDKs:

  • Develop and share libraries or SDKs across microservices to ensure consistent handling of data types
  • Centralized libraries can provide functions for serialization, validation, and conversion

Schema Registry:

  • Use a schema registry to centrally manage and evolve data schemas
  • Services can fetch the latest schema from the registry, ensuring compatibility and consistency

Schema Evolution Strategies:

  • Implement schema evolution strategies such as backward compatibility
  • When introducing changes, ensure that older versions of services can still understand and process data from newer versions

Validation and Error Handling:

  • Implement robust validation mechanisms to catch data type mismatches early
  • Provide clear error messages and status codes when data types do not match expected formats

Testing:

  • Conduct thorough testing, including unit tests, integration tests, and contract tests
  • Test scenarios should include data type edge cases to uncover potential mismatches

Versioning and Compatibility:

  • Use versioning strategies such as URL versioning or header versioning to manage changes
  • Maintain backward compatibility when making changes to data types

Code Reviews and Collaboration:

  • Encourage collaboration between teams to review API contracts and data models
  • Conduct regular code reviews to identify and address potential data type mismatches

Runtime Type Checking:

  • Some programming languages offer runtime type checking or reflection mechanisms
  • Use these features to validate data types at runtime, especially when integrating with external services

The Impact

Improved Interoperability: Ensures seamless communication between microservices regardless of the languages or frameworks used.

Reduced Errors: Minimizes the chances of runtime errors and unexpected behavior due to data type inconsistencies.

Faster Integration: Developers spend less time resolving data type issues and can focus on building features.

Easier Maintenance: Centralized data transformation layers and standardized contracts simplify maintenance and updates.

Contract Driven Development

 

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API Integration for Automating Payments, Underwriting, and Orchestrating New Banking Process Workflows

Case Study

API Integration for Automating Payments, Underwriting, and Orchestrating New Banking Process Workflows

Overview

API integration can help automate Payment Backoffice tasks involving underwriting, collateral management, credit checks, and various other processes. It requires careful consideration of various factors to ensure the bank’s workflow orchestration is efficient, secure, and compliant.

At Sun Technologies, our API integration experts use a proven checklist to manage critical aspects of API development that includes – Error Handling, Data Validation, Performance and Scalability, Transaction Processing, Webhooks and Notifications, Monitoring and Logging, Integration with Payment Gateways, Testing, Backup and Disaster Recovery, Legal, and Compliance.

By considering these aspects, our developers are creating robust, secure, and efficient interfaces that streamline payment processes and enhance the overall user experience.

Payment Process that is Now Automated: Powered by No-Code API Integration

 Initiate Payment:

Back-office system sends a POST request to /payments API with payment details.

API validates the request, processes the payment, and returns a response with payment status and transaction ID.

Check Payment Status:

Back-office system periodically checks the payment status using GET /payments/{id}.

API returns the current status of the payment (pending, completed, failed).

Refund Process:

If needed, the back-office system initiates a refund by sending a POST request to /payments/{id}/refunds.

API processes the refund and updates the payment status accordingly.

Transaction History:

To reconcile payments, the back-office system retrieves transaction history using GET /transactions.

API returns a list of transactions with details like amount, date, status, etc.

Automated Reporting:

The back-office system exports transaction data from the API in CSV format for reporting.

API supports filtering by date range and other parameters to generate specific reports.

Challenges

  • Reducing manual effort and streamlining payment processes
  • Reducing the risk of human error in payment handling.
  • Ensuring faster payment processing with real-time status updates
  • Enabling API integration with payment gateways, accounting systems, and other platforms
  • Ensuring APIs handle large volumes of transactions and scale as the business grows
  • Ensuring adherence to security standards and regulatory requirements
  • Enabling real-time status updates and transaction history
  • Providing visibility into payment workflows

How we Helped: Our Process Involving Underwriting Automation

  1. Requirement Analysis: Identify payment workflows, user roles, and data requirements
  2. API Design: Define endpoints for payment initiation, status checks, refunds, etc.
  3. Security Implementation: Implement OAuth 2.0 for authentication, data encryption, and RBAC
  4. Data Validation: Validate payment data for correctness and completeness
  5. Error Handling: Define error codes and messages for different scenarios
  6. Performance Optimization: Optimize endpoints for speed, implement caching, and rate limiting
  7. Webhooks: Provide webhooks for real-time payment updates
  8. Documentation: Create detailed API documentation with examples and tutorials
  9. Testing: Conduct unit, integration, load, and security testing
  10. Monitoring: Set up monitoring for API usage, performance metrics, and alerts
  11. Compliance: Ensure compliance with financial regulations and industry standards
  12. Release: Gradually release the API with proper versioning and support mechanisms

The Impact

100% Secure User Data

API Tokens provide secure access to user data without exposing credentials

3X Efficiency

We reduced the need for repeated user authentication by 300%

Faster User Experience

Seamless access to banking services within applications

100% Auditability

Tokens are logged and audited for security and compliance purposes

Payment API Integration

 

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Reimagining Lending Process: Automated Data Streaming Using Kafka and Snowflake

Case Study

Real-Time Data Streaming, Routing, and Processing Using Kafka and Snowflake

Overview

A top-tier bank’s legacy messaging infrastructure posed multiple challenges in handling growing data volumes – Transaction Data, Customer Data, New Loan Application Requests, KYC Data, etc. Hence, activating any new digital experience using the existing legacy infrastructure meant enabling high volumes of asynchronous data processing. Traditional messaging middleware like Message Queues (MQs), Enterprise Service Buses (ESBs), and Extract, Transform and Load (ETL) tools were unable to provide the necessary support that modern applications demand.

Modern Applications Require Asynchronous, Heterogeneous Data Processing

What is Asynchronous Data Processing?

Asynchronous processing allows the system to handle multiple loan applications simultaneously without waiting for each application to complete. This means that while one application is being reviewed, others can continue to be processed in parallel.

For example, when a borrower applies for a mortgage loan through an online lending platform, the backend must be capable of collecting required documents and information, such as income statements, tax returns, credit reports, property details, and employment history.

When the borrower submits their application, the system immediately acknowledges receipt and starts the process. Meanwhile, in the background, the system also asynchronously verifies employment history, orders a credit report, and assesses property value.

Why Enable Data Streaming, Routing, and Processing Using APIs?

With the implementation of a Digital API Hub, the Legacy Messaging Middleware gets integrated with modern event streaming automation tools. It can then be taken enterprise-wide to enable new services or functionalities using the existing data.

How are Reusable Microservices Built Using a Modern Messaging layer?

The new messaging layer helps create reusable components from existing topics or data. Hence, launching any new digital service or feature can consume existing topics or data. A topic here implies code that is inside of a Terraform module which can be reused in multiple places throughout an application.

Why Choose Kafka and Snowflake for Real-Time Data Streaming

Snowflake as a data warehousing architecture and Kafka as a platform was chosen to automate different data stream lanes. Our developers were able to used Snowflake to enable event-driven consumption using Snowpipe. By integrating this cloud-based system, we were able to provide easy access to more cloud-based applications for different banking processes and teams.

  • We set up a Java application for data producing teams to scrape an API and integrating it with the data routing platform.
  • Using Kafka as a buffer between data producers and Snowflake allowed for decoupling of ingestion and processing layers, providing flexibility and resilience.
  • Information on different topics is then pushed into further processing for sending our event-driven notifications.
  • We also set up different event-driven data streams that achieves close to real-time fraud detection, transaction monitoring, and risk analysis.

Our Solution: Enabling Modern Experiences Using APIs for Asynchronous Data Processing

At Sun Technologies, we bring you the expertise to integrate event-driven automation that works perfect well with traditional messaging middleware or iPaaS.

  1. Integrated Intelligent Automation Plugins: Document AI for customer onboarding and underwriting
  2. Integrated Gen AI in Workflows: Workbots capture data from excel spreadsheets, ERP, chat messages, folders, and attachments.
  3. Configured Approval Hierarchy & Controls: Faster data access and cross-departmental decisioning for lending
  4. Automated Customer Support Workflows: Streamlined borrower relationship and account management

Challenge: Building a system that can handle up to 2 million messages per day

  • Legacy data is run on software and hardware housed in monolithic and tightly coupled environments
  • Massive cost incurred in hosting, managing, and supporting legacy messaging infrastructure
  • Difficult-to-find IT skills does not let non-technical staff to be participative in automating workflows
  • Legacy messaging services pose challenges of platform retirement and end-of-life
  • Legacy messaging systems built on batch-based architectures do not support complex workflow routing
  • Legacy architecture is designed for executing simple P2P request or reply patterns
  • The tightly coupled architecture does not support creation of new workflow patterns

How Our Solution Helped

  1. Our Cloud and Data architects examined the legacy data landscape to see how it can be made compatible with modern Intelligent Automation (IA) integrations
  2. We not only identified the right data pipelines but also launched them using No-Code App development
    • Replacing Legacy Messaging using Kafka or a similar event routing platform
    • Building and deploying applications that are always available and responsive
    • Integrating with multiple event brokers to enable new routing decisions
  3. Replaced manual processes with automated workflows in Trade Finance, Guarantee Management, Information Security, and Regulatory Compliance
  4. Our No-Code Change Management Consulting completely automats the building of Asynchronous, Heterogeneous Data Pipelines

The Possible Impact

  • 3X Speed of new event streaming adoption and workflow pipeline creation
  • Simple event streaming and publishing set-up takes 1 hour
  • New data pipelines can handle up to 2 million messages per day
  • New messaging layer capable of handling 5,000 messages per second
  • Cloud-Agnostic data streaming saving millions in licensing cost

 

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3PL Predictive Modelling Accelerator: Mailbox & Document Automation for Data-Driven Demand Planning and Empty Container Management

Use Case

3PL Predictive Modelling Accelerator: Mailbox & Document Automation for Data-Driven Demand Planning and Empty Container Management

Overview

Predictive modelling and Data-Driven Demand Planning systems are essential for ‘Empty Container Management’. Global Repositioning Teams use them to reduce logistics costs associated with empty container handling and relocation. However, the ability to predict demand and reposition depends on accuracy and speed of data capture.

What 3PL business therefore need is a solution that can overcome challenges of manual extraction of data from emails, attachments, invoices, shipping labels, delivery notes, CMR Consignment Notes, etc. And while the ever dependable excel spreadsheet is not going anywhere, we can optimize workflows around it.

However, it is largely dependent on the availability of real-time cargo tracking data and how it is used to give the complete picture –

How shipping container dimensions’ data is input into the system

How data is standardized when collaborating with external shipping lines

How data is used to arrive at Projected vs Accrued container storage cost

How data being used to generate a plan for empty load and discharge

What process is followed to create a summary of cargo to be discharged at each port

What process is followed to capture Information location, capacity, and heavy lifts

What type of format you follow for storing data about arrival timestamps

What process is followed to calculate optimal storage figures, and peak periods

Completely manual processes of data extraction, data inputs and maintaining excel-based records pose a big challenge for prediction and visibility. Hence you need system integration that can continuously share the latest data to provide consistency across departments and global divisions.

Our Solution: Award-Winning Mailbox Automation and No-Code Adoption for 3PL Business Users

Sun Technologies is a top-tier implementation partner for some the world’s leading No-Code, Low-Code platforms for Automation. Our experts can implement end-to-end automation of your mailbox operations using OCR (Optical Character Recognition) to streamline processes and improve efficiencies.

  1. Mailbox Automation: Automated email processing systems can automatically sort and categorize emails based on predefined rules, extract relevant information, and route them to the appropriate departments or systems for further processing.
  2. OCR for Document Processing: Automate the extraction of data from various documents, such as shipping labels, invoices, and receipts. OCR software can scan and convert paper-based or electronic documents into machine-readable text, extracting relevant information like product names, quantities, addresses, and tracking numbers.
  3. Order and Inventory Management: Automated OCR operations can be integrated with the order and inventory management systems. When new orders are received, OCR technology can extract essential information from shipping instructions, such as item details, quantities, customer addresses, and delivery requirements and feed it into your order management systems.
  4. Improved Accuracy and Cost Savings: By automating these operations with OCR technology, the risk of human errors is significantly reduced, leading to higher accuracy and improved overall quality. Furthermore, automated mailbox and OCR operations can save costs by reducing expenses associated with manual processing.
  5. Scalability and Flexibility: Handle a growing volume of emails and documents without the need for additional manpower. As the business expands, this scalability ensures consistent and timely processing of incoming information. Additionally, OCR technology can be customized to meet the specific needs and requirements of different 3PL tasks and processes.

Challenge: Unregulated data coming from various sources, including sales and purchase forms, invoices, delivery notes, CMRs, customs documents, etc.

  • What 3PL Leaders Aspire to Achieve:

    • Building an application that gives visibility needed to determine which of the ships are underutilized, or even wasteful
    • Giving all concerned stakeholders the ability to prepare scenario-based models based on repositioning of empty containers
    • Integrating anticipatory shipping data that accurately reports or shows availability of products in a nearby hub or warehouse
    • Integrating trend analysis into a common application interface that will show various patterns of demand trends
    • Placing event-based triggers in the Transport Management System (TMS) to raise alerts on future disruptions and plan accordingly
    • Automating data input to show how many last-mile delivery drivers are required at a given time based on shipment status information

How We Can Help: Implement Our Predictive Modelling Accelerators

  1. Auto-populate data: Use of technologies such as OCR creates more robust processes for tracking of inventory levels by scanning barcodes or QR codes on incoming/outgoing shipments, enabling real-time visibility and better inventory control.
  2. Forecast Demand: Once, data related processes are automated, Predictive models can then be used to analyze historical data and other relevant factors to determine the future demand for empty containers. This can help shipping companies and container providers to proactively manage their inventory and ensure optimization of empty containers usage as well as available at the right time.
  3. Optimize Allocation: Our Predictive Modelling acceleration can help in determining the optimal allocation of empty containers to different locations or ports based on predicted demand. This can minimize the need for repositioning containers and reduce costs associated with empty container management.
  4. Optimize Route Planning: Build you customized predictive-modelling-based enterprise application that can analyze historical shipping patterns, trade routes, and other factors to optimize the routing of empty containers. By identifying the most efficient routes for repositioning empty containers, companies can reduce the time and cost required for managing their container inventory.
  5. Plan Maintenance Effectively: Analyze data related to the maintenance history of containers, environmental conditions, and other factors to predict when a container is likely to require maintenance or repairs. This can enable proactive maintenance planning, ensuring that containers are in optimal condition and reducing the likelihood of unexpected.

The Possible Impact

  • Reduce cost of maintaining manual processes by 50%
  • Automate 80% of all Transport Management System data input tasks
  • Automate 90% tasks related to empty container repositioning
  • Forecast demand for full containers and predict empty containers
  • Gain visibility into the future container shortages at locations
3PL Transformation

 

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How DevOps-As-A-Service Powered 500+ Application Feature Releases for a US-Based Credit Union

Case Study

How DevOps-As-A-Service Powered 500+ App Feature Releases for a Top US-Based Credit Union

Overview

Our dedicated DevOps support has enabled 500+ error-free feature releases for an application modernization project using our tried-and-tested code libraries and re-suable frameworks. Instead of having a siloed team of developers, database administrators, and operations teams, our DevOps orchestration has helped the client to accelerate innovation. Their IT teams and business users are now able to contribute more towards shaping new digital experiences rather than spending weeks on re-writing codes and testing of the apps before they go live.

Your DevOps consultant must be adept at creating two separate sets of hosts, a Live Side and a Deployment Testing Side. The deployment team needs to ensure that each side is scaled and able to serve traffic. The Deployment Testing Side is where changes are tested traffic is continually served to the Live Side. Sun Technologies’ DevOps Practice ensures creating a suitable environment where changes are tested manually before sending production traffic to it.

Based on the stage of the DevOps pipeline, our experts helped the client’s technical team to get trained and on-board automation tooling to achieve the following:

Continuous Development | Continuous Testing | Continuous Integration

Continuous Delivery | Continuous Monitoring

Our Solution: A Proven CI/CD Framework

Testing Prior to Going Live: Get a secure place to test and prepare major software updates and infrastructural changes. 

Creating New Live Side: Before going all in, we will first make room to test changes on small amounts of production traffic

Gradual Deployments at Scale: By rolling deployment out to production by gradually increasing the percentage served to the new live side

DevOps Challenges

  • Siloed teams of Developers, Database Administrators, and Operations Team
  • Frequent file changes, inconsistencies in deployments
  • Lack of knowledge and expertise in maintaining capacity for Testing requirements
  • Inept deployment strategy for a gradual rollout for a new version of the older applications
  • Inability to ensure all search services only ever talked to other search services of the same version
  • Prior to DevOps support, client required three developer engineers, one operation engineer, and a production engineer on standby

How Our DevOps Practice Ensures Zero Errors

During the Test Phase 

  • Dedicated Testing Team: Prior to promoting changes to production, the product goes through a series of automated vulnerability assessments and manual tests
  • Proven QA Frameworks: Ensures architectural and component level modifications don’t expose the underlying platform to security weaknesses
  • Focus on Security: Design requirements stated during the Security Architecture Review are validated against what was built

In the Deployment Phase

  • User-Acceptance Environments: All releases first pushed into user-acceptance environments and then, when it’s ready, into production
  • No-Code Release Management: Supports quick deployment of applications by enabling non-technical Creators and business users
  • No-Code platform orientation and training: Helps release multiple deploys together, increasing productivity while reducing errors

The Impact

  • Close to $40,000 saved in development and testing of APIs
  • APIs enabling close to 80 million USD transactions per month
  • Automated Clearing House and Guarantee management Systems delivered in record time.
  • 100% uptime in 50+ apps rolled out in 12 months

 

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Data-Compliant & Data Secure No-Code App Development with Legacy Migration for Federal Companies

Case Study

Data-Compliant & Data Secure No-Code App Development with Legacy Migration for Federal Companies

Overview

For Federal Agencies and Federal Contractors, Data Security is of paramount importance especially when it comes to doing business on the cloud. Also, companies that fall in the category of highly regulated industries such as – Insurance, Financial Services, Public Sector, and Healthcare need to pay special attention to Data Security.

Our data security experts are helping some of the largest US Federal Banks to stay compliant with Federal Information Processing Standards (FIPS) while migrating legacy applications or building new Codeless Applications.

While delivering Microservices Applications, our Federal customers want us to rapidly build, design, and test new API services that help connect with legacy data sources. To fulfill Federal Data Compliance requirements, our data security specialists use SaaS products and platforms that are AICPA-certified. Essentially, these are platforms that are certified by third-party auditors to maintain security compliance with SOC 2 Type II and mandated standards like HIPAA.

These platforms that are also listed on the Federal Risk & Authorization Management Program (FedRAMP®) are once again evaluated based on the suitability based on the different regional requirements.

Our Solution: Process Optimization Consulting and Data Security Evaluation

Solution to the above discussed problem lies in finding the best route to make use of existing process knowledge while using AI to optimize human efficiencies.

Process Optimization Consulting Practice: It helps identify the checks and balances that can be placed using a mix of human intervention and Automation tools.

Set Rules and Permissions: When integrating legacy systems with external APIs, customer integrations, or a third-party product, our expert guidance helps set access control rules and permissions.

RBAC-based swimlanes: Our data security specialists possess hands-on experience in orchestrating RBAC workflows across internal teams, clients, and service providers.

Enhanced Authentication: Our proven framework authenticates integrations through a multitude of methods while providing the means to apply TLS to further enhance security.

Applying Transport Layer Security (TLS): These encryption safeguards ensure eavesdroppers/hackers are unable to see the data that is transmitted over the internet.

Challenges of AI in industries Such as Banking and Insurance

Concerns of Federal CTOs: Federal Agency CTOs have voiced their concerns about the risks and losses that can occur due to data outages or data loss caused by Generative AI.

Data Poisoning: The use of AI and ML in banking transactions can go wrong when a mistaken context or understanding is fed into the system.

Chances of Bias: While AI scans millions of documents, it can also lead to forming erroneous and biased classifications that inconveniences customers.

Failed or Declined Transactions: When results delivered based on biased judgement of data, it can lead to customers getting blocked or declined services.

How we Helped

  • Our codeless practice makes it easy to migrate logic and data to a Migration factory from where it is extended using our recommended No-Code platform
  • It can successfully connect with legacy systems like ServiceNow, PEGA, APPWAY, AWD, etc., to build applications in a drag-and-drop interface
  • Queries are created from our expert-recommended No-Code platform that is used to get data feeds from legacy platforms
  • This data is used to create No-Code applications which can query with simple HTTP requests.
  • The recommended No-Code platform deployment ensures accurate extraction of business rules from legacy platform
  • CX, data model, and integrations are successfully extended to a modern frontend with significant improvements in application uptime and performance

The Impact

  • 100% accuracy in extraction of business rules
  • 600x Increase in developer productivity for client
  • 80% reduction in maintaining legacy applications
  • 500x reduced time spent on bug fixing
  • Reduced TCOs by close to 60%

 

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Automated Testing for Mainframe Modernization: 400% faster refactoring and testing of legacy applications

Case Study

Testing Automation for Mainframe Modernization: 400% faster refactoring and testing of legacy applications

Overview

One of our BFSI clients wanted to migrate components a critical mainframe application to the cloud. It required code refactoring from COBOL and PowerBuilder to modern programming languages like Java and to modern frameworks such as Angular. The key purpose of this modernization move was to enable cloud optimization of IT workloads while activating new-age digital experiences that could not be run on the monolithic mainframe. The Re-Platforming strategy required upgradation of Java applications to the most updated versions.

To execute these deliverables, there are two tools that were used extensively – Our in-house AI-powered Testing Tool ‘IntelliSWAUT’ and ‘AWS Blu Age’.

Almost 50+ applications were modernized using our AI-infused code refactoring and functional testing. The deliverables included the following tasks:

Re-platforming of Applications from PowerBuilder to Phoenix

Migration of Data from Oracle to Snowflake Data

Re-platforming of Applications from JBoss EAP to Phoenix

Crystal Report Upgrades to Enterprise Edition

ETL Upgrades from 3.2 to 4.2

Our Solution

  • Use of ‘IntelliSWAUT’ tool and AWS Blu Age automates conversion of complete legacy applications to new programming languages and frameworks
  • As soon as a developer commits changes to the source code, the automation automatically places it a downstream testing cycle
  • It enables testers to automate the iterations required to check the refactored codes, test sets, and finetune changes
  • Our combination of human testers and AI Testing Tools can quickly address differences in text content, text color, buttons, output data, etc.
  • Test scenarios are automatically created when testers use the application to perform required actions
  • It also automates testing of the visual appearance of any application/product to ensure it meets design specifications

Challenges

  • Legacy mainframe systems often have intricate architectures, making manual testing labor-intensive and error-prone
  • Changes in mainframe applications can cause unforeseen issues and require extensive regression testing
  • Mainframe modernization projects often involve frequent updates and releases to keep pace with evolving business needs
  • Manual testing often incurs data migration and in those cases validating data accuracy becomes cumbersome if performed manually
  • Mainframe modernization may involve transitioning to hybrid or multi-cloud environments which requires specialized cloud talent
  • Manual testing may overlook many test scenarios due to an over-dependency on human efforts
  • Integrating mainframe changes into the CI/CD pipeline for continuous testing becomes challenging with manual processes

How we Helped

  • We deployed a team of code specialist with our automated testing tools and unique frameworks
  • Our codeless tool creates resilient testing scripts that don’t break when objects such as icons, text, and other elements are moved on the user interface
  • Codeless test automation frameworks and scripts are used to check test quality, intent and integrity of applications
  • Our daily cyclical frameworks include converting the code and the data, running the tests, checking if it works, and finetuning when required

The Impact

  • Conversion and testing application with millions of lines of code take a few hours
  • We cut down scripting time from six hours per test case to just two hours
  • Complex migration processes have been almost entirely automated
  • Minimal turnaround time for workflows with no errors or delays
  • Upgraded 50+ Apps using automated functional testing
Testing Automation

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