JRD Systems

Hadoop to Azure migration

Client
A leading digital marketing and technology company, which partners with a global Automobile OEM and dealerships to provide data-driven marketing, advertising, and retailing solutions. It helps dealerships enhance customer engagement, boost sales, and improve operations through advanced insights. Client utilizes customized technology solutions to optimize dealer reach and customer experience.

JRD Context
Provided Consulting, Technical Solution and Implementation services to the Client. Participated in End to End planning and execution of migration project, making it a success.

Solution

• Automated Data Pipelines: Implemented Azure Data Factory for automation, streamlining ETL processes and improving efficiency.
• Scalable Data Processing: Utilized Databricks and Apache Spark for efficient and scalable data processing.
• Centralized Data Governance: Integrated Unity Catalog for metadata management and secure data access across Azure Blob Storage/Data Lake and Databricks.
• Testing, CI/CD, and DR: Conducted comprehensive testing, integrated Azure DevOps for CI/CD, and implemented Disaster Recovery (DR) with IaC to ensure high availability and resilience across cloud environments.

Key Benefits:
Efficiency Gains

• Data processing time reduced by 80% (5 hrs to 30 min).
• ETL speed improved by 70%, failure rates reduced by 90%.
• 95% of manual tasks now automated with Azure ADF.

Data Quality & Accuracy

• Data discrepancies and duplicates reduced by 50%.
• 98% pass rate in automated validation checks.
• Inconsistencies across systems reduced by 75%.

Cost Savings & ROI

• Costs reduced by 60% in infrastructure and staffing.
• Handling 4x more data with no significant cost increase.
• Downtime decreased by 85%.

Technologies:

• Azure Data Factory for automating pipelines and Databricks for scalable data processing.
• Blob Storage/Data Lake for storing structured and unstructured data.
• Unity Catalog for unified data governance, metadata management, and access control.
• Azure Monitor for system health monitoring and diagnostics.
• Active Directory, Key Vault, and DevOps for security, access, and CI/CD management.

Industry / Domain

• Automotive and Digital Marketing

Automating Invoice Distribution with Python

Client
A rapidly growing Healthcare organization that provides specialty pharmacy services for patients receiving complex medications for chronic illnesses and complicated diseases. The company was challenged by a complex, manual invoicing process that was time-consuming, error-prone and required significant human intervention.

JRD Context
Analyze current process and data, design and implement an automated system to improve the process.

Solution
A Python script was developed to:

• Read Invoice Data: Extract invoice details from structured data and static extracts.
• Interrogate CRM System: Query a complex CRM structure to determine the appropriate recipients based on predefined business rules.
• Automate Email Distribution: Generate and send invoices to the correct recipients via email, ensuring timely and accurate delivery.

Key Benefits

• Saved 32 person hours per invoice run by removing the manual consolidation, send, and troubleshooting efforts.
• Eliminated dependency on human knowledge by automating business rules for consistency and efficiency.
• Improved efficiency with automated email sending.
• Enhanced tracking and accuracy of invoice delivery.

Technologies

• Python (Pandas for data processing, SQLAlchemy for database interactions, Requests for API communication, smtplib for email automation)
• CRM Integration (Queries to determine recipients based on complex client organization)
• Email Automation

Industry / Domain

• Healthcare pharmacy and infusion services

Data Analytics Solution in Automotive Production Plants Using AWS Cloud

Client Overview
A leading financial information, analytics, and ratings provider offers essential insights and data to businesses, governments, and investors, helping them make informed decisions in global financial and automotive markets. Their services include credit ratings, benchmarks, and risk assessments across finance, energy, transportation, and commodities sectors. The organization plays a crucial role in driving transparency, growth, and efficiency through diverse platforms in the global market.

Scope of work
This Project empower Automotive and OEM customers to assess and predict the physical risks and financial implications linked to their plants. This project predicts potential hazards such as coastal flooding, wildfires, flooding, drought, water stress, temperature extremes, extreme heat and cold, cyclones, and other natural disasters that could affect plant operations.

Business Challenge

• This project came with a classic problem associated with distributed computing and working with multiple cross accounts as this project involved data from different data sources
• Managing dynamic workloads efficiently while optimizing compute instance types to balance cost and performance.
• Orchestrating complex workflows with dependencies between multiple batch jobs.
• Ensuring job retries and failure handling mechanisms to prevent cascading failures in multi-step processes.
• Ensuring data encryption at rest and in transit while maintaining IAM policies for fine-grained access control.

JRD Solution

• To address the challenges, we broke down the complexity into smaller, manageable functional units, tackling each one independently. We leveraged cloud-based event triggers to initiate and orchestrate various workflow functionalities, ensuring efficient and scalable execution.
• We worked closely with the client’s analysts and experts to develop a customized solution that aligns with both business and technical objectives. Our approach ensures compliance with global and industry standards, guaranteeing widespread acceptance.
• We implemented a cloud-based serverless architecture to optimize computing costs for the client, while ensuring high availability.
• Designed the AWS architecture to maximize efficiency and cost-effectiveness by leveraging scalable computing and automated workflows.
• Established a structured AWS cloud-based framework for seamless data storage, retrieval, and processing, ensuring smooth integration with existing enterprise systems. Advanced analytics and reporting tools were incorporated to generate valuable insights for informed decision-making.
• Implemented a comprehensive AWS disaster recovery and high-availability strategy, featuring automated backups and multi-region redundancy. Integrated AWS security best practices throughout the solution to uphold data protection and regulatory compliance.

Key Benefits

• Data-Driven Decisions: The provided solutions not only reduced costs but also enhanced the scalability and reliability of the system, empowering the client to make data-driven decisions with confidence.
• Architecture: Designed with a focus on scalability, availability, and maintainability, leveraging cloud solutions.
• Optimized Workloads: Our AWS cloud solution is architected for dynamic scaling, utilizing AWS Batch, Fargate, Lambda, DynamoDB, and Step Functions to efficiently manage fluctuating workloads. By aligning with the AWS Well-Architected Framework, the design ensures seamless scalability, adapting to evolving data volumes and processing demands without compromising performance.
• Cost Management: We implemented a cost-effective serverless architecture using AWS to minimize computing expenses while ensuring high availability. Additionally, AWS Budgeting and Cost Management tools were leveraged to optimize resource utilization and control spending.
• Robust Security and Compliance Framework: Our solution enforces AWS security best practices by integrating IAM, KMS, VPC, WAF, NACL, Security Groups, Secrets Manager, and end-to-end encryption (at rest and in transit). This ensures robust data protection, risk mitigation, and regulatory compliance, aligning with industry standards and best practices.
• Disaster recovery and Backup: We implemented AWS Disaster Recovery and AWS Backup solutions to ensure high availability and data resilience across multiple regions. Cross-region replication and automated backups were configured to safeguard against data loss and minimize downtime.

Core Differentiators

• The use of a microservices architecture is a key differentiator, enabling the project to break down complex workflows into smaller, manageable units. This approach provides flexibility, scalability, and ease of maintenance while addressing distributed computing challenges and enabling seamless integration across diverse data sources.
• The adoption of a serverless architecture is a significant differentiator, optimizing computing costs for the client.
• This event-driven approach ensures that the system can handle high volumes of data and requests efficiently, making it adaptable to growing customer needs

Implementation of Data Analytics solution for Carbon footprint tracking on AWS cloud

Client Overview
A leading financial information, analytics, and ratings provider offers essential insights and data to businesses, governments, and investors, helping them make informed decisions in global financial and automotive markets. Their services include credit ratings, benchmarks, and risk assessments across finance, energy, transportation, and commodities sectors. The organization plays a crucial role in driving transparency, growth, and efficiency through diverse platforms in the global market.

Scope of work
This project calculates the carbon emissions associated with both the production process and the entire life cycle of vehicles. It examines emissions from raw material extraction, manufacturing, transportation, and vehicle operation, including fuel consumption. Data from various stages is integrated to estimate the total carbon footprint of a vehicle. The findings aim to identify opportunities for reducing emissions and promoting more sustainable practices in the automotive industry.

Business Challenge

• This is an innovative concept in the automotive industry, as the global focus is on finding solutions to reduce carbon emissions at every stage of a vehicle's life cycle.
• Many states and countries have made it mandatory to provide detailed reports on emissions and implement measures to control them.
• Calculating vehicle emissions is complex due to the multifaceted life cycle, which includes the production of raw materials, vehicle manufacturing, and the usage of the vehicle throughout its lifetime.

JRD Solution

• We worked closely with the client’s analysts and experts to develop a customized solution that aligns with both business and technical objectives. Our approach ensures compliance with global and industry standards, guaranteeing widespread acceptance.
• Implemented an AWS cloud-based, serverless architecture to optimize computing costs while enhancing system availability through a resilient cross-region disaster recovery strategy.
• Designed the AWS architecture to maximize efficiency and cost-effectiveness by leveraging scalable computing and automated workflows.
• Established a structured AWS cloud-based framework for seamless data storage, retrieval, and processing, ensuring smooth integration with existing enterprise systems. Advanced analytics and reporting tools were incorporated to generate valuable insights for informed decision-making.
• Implemented a comprehensive AWS disaster recovery and high-availability strategy, featuring automated backups and multi-region redundancy. Integrated AWS security best practices throughout the solution to uphold data protection and regulatory compliance.
• Delivered a robust, scalable, and secure platform that adheres to industry standards while driving long-term sustainability and operational efficiency.

Key Benefits

• Architecture: Designed with a focus on scalability, availability, and maintainability, leveraging cloud solutions. The project was successfully developed and completed on time.
• Scalability: We designed the AWS cloud architecture with scalability in mind, leveraging services like AWS Batch, Fargate, Lambda, DynamoDB, and Step Functions to handle dynamic workloads efficiently. The solution follows the AWS Well-Architected Framework, ensuring seamless scalability as data and processing demands grow.
• Cost Management: We implemented a cost-effective serverless architecture using AWS to minimize computing expenses while ensuring high availability. Additionally, AWS Budgeting and Cost Management tools were leveraged to optimize resource utilization and control spending.
• Compliance and Security: We implemented AWS security best practices using IAM, KMS, VPC, WAF, NACL, Security Groups, Encryption at Rest and Transit and Secrets Manager to ensure data and risk protection and compliance.
• Disaster recovery and Backup: We implemented AWS Disaster Recovery and AWS Backup solutions to ensure high availability and data resilience across multiple regions. Cross-region replication and automated backups were configured to safeguard against data loss and minimize downtime.

Core Differentiators

• We collaborated with the client throughout all project phases, offering implementation solutions. We provided an AWS cloud-based technical architecture that follows AWS Well-Architected Framework, best practices, ensuring scalability, maintainability, and seamless integration with other projects. Additionally, the solution was implemented with a strong focus on security and compliance.

Product Development Forecasting Global Vehicle Sales on AWS cloud

Client Overview
A leading financial information, analytics, and ratings provider offers essential insights and data to businesses, governments, and investors, helping them make informed decisions in global financial and automotive markets. Their services include credit ratings, benchmarks, and risk assessments across finance, energy, transportation, and commodities sectors. The organization plays a crucial role in driving transparency, growth, and efficiency through diverse platforms in the global market.

Scope of work
The Automotive forecasting application for automotive vehicles enables analysts to predict vehicle sales volume across different geographic locations using historical data and market trends. It provides an intuitive interface for analysts to adjust sales forecasts for future years based on industry insights and economic factors. Analysts can compare multiple forecasting scenarios and apply adjustments dynamically to reflect market shifts. The application supports granular forecasting at country, and global levels for better decision-making. Overall, it empowers automotive companies to make data-driven, strategic business decisions.

Business Challenge

• Scalability of Data Processing : Handling millions of data records efficiently while ensuring smooth performance for multiple users requires a scalable architecture.
• Data Isolation and Multi-Tenancy: Since each user requires a separate dataset, implementing strict data isolation while maintaining efficient storage and retrieval mechanisms is a challenge.
• Real-Time Data Slicing & Dicing: Users need the ability to filter, aggregate, and manipulate large datasets interactively.
• Data Security & Access Control: Implementing robust access control to ensure users only access their own data is essential.
• Cost Optimization for Storage & Compute: Storing and processing massive amounts of data can be expensive.
• Concurrency & Performance Bottleneck: With multiple users performing high-volume queries and transformations, preventing performance degradation due to concurrency issues requires caching, indexing strategies, and load-balancing techniques.

JRD Solution

• Scalable Data Processing Architecture: Successfully implemented an AWS-based scalable architecture to handle millions of data records efficiently, ensuring high-performance processing for multiple users.
• Multi-Tenant Data Management: Implemented secure data isolation for each user using AWS services (S3, DynamoDB, AWS Batch, Redis Cache, Lambda) to ensure each user’s data remains separate and secure while allowing seamless access.
• High-Concurrency Handling: Engineered a system capable of supporting multiple users performing complex queries simultaneously without performance degradation, utilizing caching strategies.
• User-Friendly Data Visualization: Developed interactive and dynamic data visualization features using High charts
• Separate workflow systems were established and orchestrated using AWS Step Functions, enabling smooth execution of batch jobs and nightly processes.
• We worked closely with the client’s analysts and experts to develop a customized solution that aligns with both business and technical objectives. Our approach ensures compliance with global and industry standards, guaranteeing widespread acceptance.
• Numerous microservices were developed to support the distributed architecture, enhancing flexibility and scalability using AWS Lambda and Batch.
• The user interface (UI) was built using multiple Angular applications, seamlessly integrated into a unified portal for an optimal user experience.

Key Benefits

• Optimized Data Query Performance: Implemented real-time data slicing and dicing capabilities using AWS Batch, Python, Polars, Lambda, and Elastic Cache enabling users to process large datasets with minimal latency.
• Advanced Security & Compliance: Enforced strong IAM policies, data encryption, and VPC configurations to protect user data and comply with industry security standards.
• Implemented a comprehensive AWS disaster recovery, high-availability strategy, and automated backups. Integrated AWS security best practices throughout the solution to uphold data protection and regulatory compliance.
• Cost Management: We implemented a cost-effective serverless architecture using AWS to minimize computing expenses while ensuring high availability. Additionally, AWS Budgeting and Cost Management tools were leveraged to optimize resource utilization and control spending.
• Implemented Comprehensive Monitoring & Logging for auditing and compliance requirements. Implemented continuous integration/deployment and implemented robust authorization and authentication mechanisms.

Core Differentiators

• A core differentiator of this project was the design of a scalable, distributed architecture that seamlessly integrated multiple modules, enhancing flexibility and performance. Additionally, we optimized data storage and workflow orchestration using AWS services, ensuring high availability, security, and an exceptional user experience.