Phased migration of Boomi APIs to Apigee helps a leading logistics company improve performance and scale without business disruption
Client
A leader in truck transportation and logistics services
Goal
Migrate Boomi-based logistics APIs to improve performance and scalability
Technology Tools
Apigee, Boomi, Swagger, JMeter, Postman, GCP
Business Challenge
The client's existing Boomi Atom platform with logistics APIs had lifecycle and monitoring issues, with frequent and elongated downtimes, causing customer experience challenges.
The system did not support the logging of events, and API transactions were untraceable. Identifying the number of customers facing issues and incidents when APIs were not working was difficult. The absence of alerting mechanisms, scalability concerns, and the Boomi platform's high licensing costs were other critical challenges.
An optimized API governance system was required to provide an abstraction for the backend services, security, and efficiencies around rate limiting, quotas, and analytics.
Solution
Iris strategized the smooth transition of 250+ Boomi APIs, starting with 20 in the pilot phase. The entire migration was planned to occur in four waves.
First, the pseudocode of Boomi APIs was documented and reviewed. The team then developed proxies in Apigee X following a TDD (Test-driven Development) approach. A well-defined logging framework was provided to the client for capturing appropriate parameters for tracking API calls.
Seamless migration of API keys from Boomi API Management (APIM) to Apigee X apps was performed. Network routing at F5 for the individual proxies was implemented to transfer the traffic from Boomi to Apigee post migration in each wave. Process metering, monitoring, and adherence/compliance hooks were inserted into the system.
Outcomes
Our API migration solution delivered the following outcomes:
- Improved performance and scalability by 30%
- Centralized logging and alerting for both APIM and backend systems resulting in 40% MTTR (Mean Time for Ticket Resolution)
- Apigee analytics enablement for API traffic, request latency, response time, target errors, and transaction revenue analysis
- Enablement of API discovery, monetization, registration, partner onboarding, and governance
- Ability to integrate the system into the Apigee developer portal
- Eliminated Boomi licensing cost
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Order management platform transformation
Platform transformation and multi-cloud integration improve multinational publishing company's order management, time-to-market and performance.
Client
Multinational publishing, media, and educational company
Goal
Improve order management and transaction processing capabilities
Technology Tools
AWS EKS, Kong, Salesforce Commerce Cloud (SFCC), Salesforce CRM, Jenkins, Sumo Logic, DatadogBusiness Challenge
The client's order management platform was complex and had scalability issues, causing poor customer experience and loss of revenue. The platform was hosted on Oracle cloud, with data stored in different repositories. Services were also hosted in the Oracle cloud, which used the BICC extract to fetch information about order details from Oracle databases. The low performance of customer-facing applications was causing latency and very high transaction processing time.
Solution
Team Iris transformed Oracle-based SOA services into six microservices and migrated them to AWS EKS for autoscaling with self-healing and monitoring capabilities.
We developed services for publishing data to Salesforce CRM for quick order processing and conversions. The BICC system for diversified information and order history was enabled with real-time integration between Oracle Fusion and materialized views for data consumption.
Post migration, these services were registered in Kong for discovery, and a CI/CD pipeline was created for deployment using Jenkins. Sumo Logic was used for monitoring the logs, and Datadog was used to observe latency, anomalies and other metrics.
Outcomes
The order management platform transformation delivered the following benefits to the client:
- System performance improved by 70%
- Transaction processing capability increased by 4x
- Order processing capabilities were enhanced by 200%
- Total cost of ownership (TCO) was reduced by 30%
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Custom analytics enable faster business decisions
Brokerage, Wealth & Asset Management
Custom analytics enable faster business decisions
Optimized data and on-demand analytics deliver faster business insights and better user experience for asset management firm
Client
U.S.-based asset management company
Goal
Streamline and improve data and analytics capabilities for enhanced user experiences
Technology Tools
Java, React JS, MS SQL Server, Spring Boot, GitHub, JenkinsBusiness Challenge
The client captures voluminous data from multiple internal and external sources. The absence of quick, on-demand capabilities for business users was inefficient in generating customized portfolio analytics on attributes such as average quality, yield to maturity, average coupon, etc.
The client teams were spending enormous amounts of manual effort and elapsed time (approximately 12-15 hours) to respond to requests for proposals from their respective clients.
Solution
Iris implemented a data acquisition and analytics system with pre-processing capabilities for grouping, classifying, and handling historical data.
A data dictionary was established for key concepts, such as asset classes and industry classifications, enabling end users to access data for analytical computation. The analytics engine was refactored, optimized, and integrated into the streamlined investment performance data infrastructure.
The team developed an interactive self-service capability, allowing business users to track data availability, perform advanced searches, generate custom analytics, visualize information, and utilize the insights for decision-making.
Outcomes
The solution brought several benefits to the client, including:
- Simplified data access to generate custom analytics for end users
- Eliminated manual processing and the need for complex queries
- Enhanced the stakeholder experience
- Reduced response time to client RFPs by over 50%
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Quality engineering for Blockchain-DLT platform
Banking & Financial Services
Next-gen Quality Engineering for Blockchain-DLT platform
Quality engineering implementation helps a digital financial services client smooth the legacy migration of its Blockchain-DLT (Digital Ledger Technology) platform by advancing automation coverage and patch delivery efficiencies.
Client
A leading digital financial services company
Goal
Blockchain- DLT platform assurance with improved automation coverage
Tools and technologies
Amazon Elastic Kubernetes Service (EKS), Azure Kubernetes Services (AKS), Docker, Terraform, Helm Charts, Microservices, Kotlin, Xray
BUSINESS CHALLENGE
The client's legacy DLT platform did not support cloud capabilities with the Blockchain-DLT tech stack. The non-GUI (Graphic User Interface) and CLI (Command Line Interface)-based platform lacked the microservices architecture and cluster resilience. The REST (Representational State Transfer) APIs-based platform did not support platform assurance validation at the backend. Automation coverage for legacy and newer versions of the products was very low. Support for delivery patches was insufficient, impacting the delivery of multiple versions of R3 products each month.
SOLUTION
Iris developed multiple CorDapps to support automation around DLT-platform functionalities and enhanced the CLI-based & cluster utilities in the existing R3 automation framework. The team implemented the test case management tool Xray to improve test automation coverage for legacy and newer versions of the Corda platform, enabling smooth and frequent patch deliveries every month. The quality engineering process was streamlined for the team's Kanban board by modifying the workflows. Iris also introduced the ability to execute a testing suite that could run on a daily or as-needed basis for AKS, EKS, and Local MAC/ Windows/ Linux cluster environments.
OUTCOMES
The Blockchain-DLT reliability assurance solution enabled the client to attain:
- Improved automation coverage of the DLT platform with 900 test cases with a pass rate of 96% in daily runs
- Compatibility across AWS-EKS, Azure-AKS, Mac, Windows, Linux, and local clusters
- Increased efficiency in deliverables with an annual $35K savings in the test case management area
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Get in touchTest Automation Speeds Model Risk Management System
Client
Top international bank
Goal
Fully automate the model risk management system framework to improve quality and confidence in testing results
Tools and technologies
Java, Selenium, Maven, TestNG, Git
BUSINESS CHALLENGE
The client's existing model risk framework was inefficiently handling functional testing aspects and risk scenarios due to lack of an end-to-end testing framework. Built on redundant, hard-to-debug, and non-scalable code, the system was unreliable for model risk testing. Test cases and controls were maintained and executed in Excel, eliminating parallel workflow abilities, tempering testing results, contributing to increased testing efforts and even delaying production launches in some cases. Scalability of testing using automation, running data-driven, end-to-end test flows, and restoring confidence in test results were the client's prime challenges.
SOLUTION
Iris built a lightweight and scalable new framework, providing 100% automated regression testing of functional test cases. Using simplified, customizable code that separated automation utilities and test functions, Iris' solution brought multiple improvements. Among them was faster test execution due to significantly reduced manual efforts. It also resulted in better quality and stability from the early identification of testing issues, enabling immediate corrective actions to occur. Another advantage of the solution was adaptability to multiple application areas due to ease of maintainability and traceability of code employed.
OUTCOMES
The client experienced several positive effects from the new, fully-automated solution:
- Acquired a 100% stable, scalable, reusable test framework
- ROI of 72%; payback period of less than 8 months
- 20% reduction in testing efforts for faster time to market
- Significant decrease in time required for ongoing maintenance of test scripts
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Get in touchData warehouse enhances client communications
Client
A U.S.-based investment bank
Goal
Improve data collation and information quality for enhanced marketing and client reporting functions
Tools and technologies
Composite C1, Oracle DB, PostgreSQL, Vermilion Reporting Suite, Python, MS SQL Server, React.js
BUSINESS CHALLENGE
The client’s existing investment data structure lacked a single source of truth for investment and performance data. The account management and marketing teams were making significant manual efforts to track portfolio performance, identify opportunities and ensure accurate client reporting. The time-consuming and manual processes of generating marketing exhibits and client reports were highly error-prone.
SOLUTION
Iris implemented a comprehensive investment data infrastructure for a single source of truth and improved reporting capabilities for marketing content and client report generation. An automated Quality Assurance process was instituted to validate the information in critical marketing materials, such as fact sheets, snapshots, sales kits, and flyers, against the respective data source systems. Retail and institutional portals were developed to provide a consolidated view of portfolios, with the ability to drill down to underlying assets, AUM (Assets Under Management) trends, incentives, commissions, and active opportunities.
OUTCOMES
The new data infrastructure delivered a holistic, on-demand view of investment details, including performance characteristics, breakdowns, attributions, and holdings, to the client's marketing team and account managers with:
- ~95% reduction in performance data and exhibit information discrepancies
- ~60% improvement in operational efficiency in core marketing and client reporting functions
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Get in touchBrokerage platform transformation improves UX
Client
A leading U.S. brokerage firm with $1+ trillion in assets and serving 6,000+ RIAs
Goal
Resolve online platform accessibility, functionality and timeliness issues
Tools and technologies
Angular 9, Jenkins, Pivotal Cloud Foundry, Oracle, Kubernetes, Spring, Docker
BUSINESS CHALLENGE
Client’s existing brokerage platform supporting over 6,000 Registered Investment Advisors (RIAs) and containing information about assets valued at more than $1 trillion had accessibility issues. The high cost of owning and maintaining outdated technologies and time-to-market for new features were adding to the business challenges.
SOLUTION
Iris transitioned the client’s monolith applications to microservices to transform the RIA platform. An open-source, cloud technical stack was leveraged to develop a single-page, micro-UI-based application. BFF (Backend for Frontend) design was applied, and Angular 9 was used to achieve superior compatibility on mobile devices. Widgets were introduced to enable seamless transitions within third-party applications. Consolidated user views were created to track assets and their performance for a unified experience for the RIAs.
OUTCOMES
- Fully functional mobile views
- 100+ integrated third-party applications
- Instant and seamless access to client accounts
- Downtime for hot deployments of fixes brought to zero
- Technical debt decreased by 45%
- Release timelines shortened by 80%
- Issue resolution time reduced by 90%
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Get in touchCloud Data Lakehouse for Single Source of Truth
Client
A medical devices and fertility solutions company
Goal
Establish a cloud data warehouse for a single source of truth and timely month-end activities
Tools and technologies
Azure Data Factory, Azure Data Lake, Power BI, Synapse Analytics
BUSINESS CHALLENGE
The client had multiple instances of ERPs, sales systems, and warehouses built on obsolete technology and frameworks. The existing system siloed the data, resulting in inconsistent versions of the truth. The client's finance and sales teams were struggling to reconcile data offline and feed the same back into the ERPs, causing significant delays in month-end activities. As the record systems were also not synced and legacy reports were built on the interim warehouses, line managers and executive teams were not able to extract actionable and comprehensive insights. A solution to onboard and integrate new datasets on an ongoing basis was required to support the data merger and acquisition process.
SOLUTION
A transformation strategy to transform data and BI applications to MS Azure was finalized. The transformation was executed in phases and included discovery, report rationalization and foundational build of a global system of reporting.
The solution included the data ingestion process with Azure Data Factory, data storage and processing using Azure Data Lake and Synapse Analytics, reports and dashboards with Power BI. A utility to accelerate the onboarding of new data entities was conceptualized and delivered to onboard and integrate new datasets to support mergers and acquisitions.
OUTCOMES
Iris data practitioners helped the client overcome key challenges and advanced data warehousing capabilities by:
- Establishing a "single version of the truth" that enabled data-driven decisions and timely completion of month-end and other critical activities
- Delivering analytics to cash business processes, including subject areas of sales, inventory, shipping, finance, etc., on Power BI
- Facilitating the generation of actionable, insightful reports and dashboards, allowing "self-service" consumption for the business leadership
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Get in touchDigital ledger secures trading integrity
Client
Global bank's trading operations
Goal
Resolve trading transaction breaks and related regulatory issues through expandable intra-company digital ledger system
Tools and technologies
Hyperledger Fabric 1.4/2.2, Java 8, Go Language 1.8, Kafka, Node JS, Microservices, OpenShift, Dockers, Kubernetes
BUSINESS CHALLENGE
A highly-manual, paper-dependent, trading and reconciliation process was causing the accumulation of a large number of daily transaction liquidity breaks, which had been cited by federal regulators and risked a billion dollar cost impact. The lack of a robust trade audit and reconciliation process to reduce liquidity breaks and operating costs led the bank to seek an immutable system that could record and unify financial practices and be expanded to other transaction areas.
SOLUTION
Iris' solution comprised a production-ready, configurable platform using microservices and blockchain-based digital ledger architecture. It employed Smart Contracts coded with requisite business rules to facilitate front office trade booking and trade reconciliation processes. RPA was utilized to automate data mapping and testing of transactions. Preventive controls were enabled by recording intra-company transactions at their initiation using uniform booking practices, and consequently guaranteeing the term of the trade. A multi-layered infrastructure was created to support real-time, batch streaming of differing file formats. The UX was enriched through Interactive UI and automated workflows.
OUTCOMES
Iris successfully introduced a global intra-company distributed ledger and trade reconciliation system that did not exist before. With self-executing contracts matching both sides of transactions prior to feeding downstream systems, the platform ensures complete integrity at the source and reduces time and cost for all transactions. The solution also achieved:
- 30% fewer liquidity breaks
- 70% improvement in operational efficiency due to the use of RPA
- 60% reduction in business-rules configuration time, due to the smart contracts
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Get in touchSoftware transformation gets compliance for bank
Client
A global investment bank
Goal
To have a unified functional validation system for FDIC compliance
Tools and technologies
SQL Server, Sybase, Data Lake, UTM, .NET, DTA, Control-M, ALM, JIRA, Git, RLM, Nexus, Unix, WinSCP, Putty, Python, PyCharm, Confluence, Rabacus, SNS, and Datawatch
BUSINESS CHALLENGE
SOLUTION
OUTCOMES
- Faster and more efficient internal analysis with highly accurate QFC open positions
- 100% compliance with timing and format of required daily QFC report submissions to the FDIC
- Significant decrease in exceptions before the platform went go-live and zeroed critical defect delivery post-go-live
- An intuitive UI dashboard reflecting the real-time status of critical underlying data volumes, leakages, job run, and other stats
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