Difficulty in establishing a connection between AWS Glue and MongoDB using the Glue connector due to configuration complexities and compatibility issues. The AWS Glue connector was ineffective due to issues with Java truststore and SSL MongoDB connection.

To address this challenge, we have leveraged AWS Glue for Spark to establish connectivity between MongoDB and AWS Glue. This allowed us to bypass the complexities and compatibility issues associated with the Glue connector. Instead, we utilized the script provided in the AWS Documentation within our ETL process.

This script configures the connection by specifying parameters such as connection name, database, collection, partitioner, partition size, partition key, and update URI status. It enables Glue to interact seamlessly with MongoDB, overcoming connection challenges effectively.

Configuring ETL Script with Manual Timestamp for Incremental Data Sync:

In the AWS Glue ETL script configuration, the Bookmark feature did not cater to our specific use case, causing limitations in achieving incremental data synchronization. Enabling the Bookmark feature resulted in pulling full data instead of solely incremental data. Furthermore, it was noted that Bookmarks are supported only in S3 and JDBC data sources, further restricting our options for incremental data synchronization.

Handling Duplicate Entries During Incremental Data Merge:

• During the merging of incremental data with existing datasets, the possibility of duplicate entries arose, particularly when updated rows were present in the incremental data. This duplication occurred post-merge, leading to redundancy within the merged dataset.

• To address this challenge and maintain the integrity of the merged dataset, a solution was devised to identify and remove duplicate entries based on the latest modified timestamp. By querying the dataset for the latest modified timestamp entry post-merge, redundant rows were pinpointed and eliminated, ensuring data consistency and eliminating duplication.

• After merging the incremental data with the existing dataset, a query was executed to retrieve the entry with the latest modified timestamp. This was accomplished using the following SQL query:

Solution

Solution Architecture.

The solution architecture details the orchestration of AWS services that collectively manage the data workflow. The structure ensures efficient data processing from the initial extraction in MongoDB to the final visualization in Amazon QuickSight.

Description

1. Extract data from MongoDB for visualization and analysis:

A secure connection between MongoDB and AWS is facilitated through AWS Glue for Spark. In AWS Glue version 4.0 and later, you can utilize AWS Glue to read from and write to tables in MongoDB and MongoDB Atlas.

2. Transform the data for compatibility with visualization tools:

• AWS Glue jobs are used to perform Extract, Transform, Load (ETL) processes on the extracted data, ensuring compatibility with visualization tools.
• Data undergoes transformation to prepare it for analysis, enabling seamless integration with visualization tools such as Amazon QuickSight.

3. Establish a data lake using S3:

• Post-transformation, the data is organized within an S3 bucket to create a data lake, structured to facilitate analytical needs, and enable in-depth data exploration.
• The data lake architecture ensures scalability and flexibility, allowing for the storage of large volumes of data in a cost-effective manner.

4. Visualize data in QuickSight:

• Amazon QuickSight utilizes AWS Glue Data Catalog tables as a metadata repository and leverages Amazon Athena for querying data stored in the S3 data lake. QuickSight connects to Athena as a data source, allowing users to create interactive dashboards and reports for business intelligence and data-driven decision-making.
• QuickSight's intuitive interface enables users to explore and analyze data effortlessly, empowering stakeholders to derive actionable insights from the data.
• The solution architecture orchestrates AWS services effectively to manage the data workflow, ensuring efficient data processing from MongoDB extraction to visualization in Amazon QuickSight.

Furthermore, the solution description provides detailed insights into the implementation of various functions within the ETL script:

• The ETL script performs a range of critical functionalities, including extracting collection and database information from a csv file stored in an S3 bucket, configuring environment-based settings dynamically based on the provided environment name as a job parameter, and seamlessly handling incremental data extraction, even in scenarios involving errors like schema mismatches. This comprehensive approach ensures efficient and reliable data processing across different environments.
• It establishes connections to MongoDB and orchestrates data extraction, leveraging AWS Glue to convert the extracted data into DataFrame format for further processing. We opted not to use DynamicFrame for this purpose since some of the inbuilt functions offered in handling DataFrames are not available with DynamicFrame.
• Timestamp formatting and partitioning functionalities are incorporated to adjust timestamps to reflect the UTC+5:30 timezone and optimize data storage in S3 using Parquet format.
• Additional functions, such as sending emails, updating Glue crawler targets, and managing timestamp CSV files on S3, contribute to the robustness and efficiency of the data processing pipeline.

In summary, the comprehensive solution architecture and ETL job showcase the seamless integration of AWS services and custom functionalities to address specific project requirements, enabling organizations to derive valuable insights from their data and drive informed decision-making processes.

Outcome

The implemented solution enables KareXpert to leverage their healthcare data effectively, leading to better outcomes such as improved patient care, operational efficiency, strategic planning, and compliance adherence. By harnessing the power of AWS services, KareXpert can stay ahead in the rapidly evolving healthcare landscape and continue to provide high-quality care to their patients.

Efficient Data Management:

By automating the extraction, transformation, and loading of data from MongoDB collections to an S3 data lake, customers can efficiently manage vast amounts of healthcare data. This streamlines the data management process, reducing manual effort and ensuring data accuracy and completeness.

Data-Driven Decision Making:

With the ability to perform SQL-based analysis using Amazon Athena, KareXpert can now provide its customers valuable insights into various aspects of their healthcare operations. This enables data-driven decision-making across departments, leading to informed strategies and actions.

Improved Operational Efficiency:

Through the analysis of operational metrics such as bed occupancy, appointment analysis, and inventory management, customers can identify areas for improvement and optimize resource allocation. This leads to increased operational efficiency and cost savings.

Strategic Planning:

By understanding their healthcare operations at a granular level through data analysis, customers can develop long-term strategic plans aimed at achieving organizational goals. This might involve expansion strategies, service enhancements, or targeted interventions to address specific challenges.

Leveraging AWS services like Glue, S3, Athena, and QuickSight, KareXpert can gain valuable insights into their healthcare operations, enhancing patient care, optimizing resource allocation, and improving overall efficiency across their network of hospitals.

Benefits

Previously, the data extraction process involved manual efforts, with individuals manually pulling data from each collection in MongoDB. Subsequently, they manually uploaded the extracted data into S3, where further transformation for analytics took place.

This manual process was time-consuming and relied heavily on human intervention. However, the current workflow has evolved to a more efficient and automated system. The entire process, from extracting data from around 150collections in MongoDB to loading it into the S3 data lake and subsequent transformation for analytics in QuickSight, now takes approximately less than 4 hours.

Currently, 30 hospitals actively utilize QuickSight dashboards for data-driven decision-making.

Customer Challenges

POS Malaysia faced several challenges, including the need to: 

1. Predict ETA for parcel deliveries accurately.
2. Process and analyze large volumes of parcel data for predictive analytics.
3. Modernize its data infrastructure for scalability and efficiency. 

AWS Service Selection Rationale: 

 Minfy recommended a tailored AWS solution to address POS Malaysia's challenges: 

1. Amazon DynamoDB: Chosen for its scalability and low-latency performance, DynamoDB became the primary database for storing parcel data.
2. Amazon Redshift: Utilized as the data warehouse to store and analyze vast volumes of structured data, facilitating predictive analytics.
3. AWS Glue: Employed for data integration, transformation, and preparation, enabling seamless analytics workflows. 

Solution Implementation

Minfy executed a comprehensive solution: 

1. Data Ingestion: Parcel data was ingested into Amazon DynamoDB from various sources, ensuring real-time availability.
2. Data Warehousing: Amazon Redshift stored and organized parcel data, making it readily accessible for predictive analytics.
3. Data Integration: AWS Glue was used for data integration, ensuring seamless workflows for analytics.
4. Predictive Analytics: Machine learning models were developed to predict ETA for parcel deliveries, leveraging the data stored in DynamoDB and Redshift. 

Results & Benefits

POS Malaysia's partnership with Minfy and the strategic implementation of AWS services yielded significant benefits: 

1. Accurate ETA Predictions: The predictive analytics solution, powered by AWS services, provides precise ETA predictions for parcel deliveries, enhancing customer satisfaction.
2. Scalability: The AWS infrastructure ensures that POS Malaysia can scale its operations to handle spikes in parcel data and delivery requests.
3. Efficiency: Streamlined data integration and analytics workflows minimize operational overhead and enhance efficiency. 

About Minfy

Minfy specializes in multi-cloud support, advanced AWS infrastructure monitoring, Database (DB) support, and Application Modernization. Recognized for industry excellence and innovation, Minfy tailored comprehensive solutions for Uno Minda, showcasing expertise in multi-cloud environments and AWS services. Minfy actively participated in cost optimization exercises, demonstrating a commitment to efficiency and fiscal responsibility. We conduct APN programs, such as WAR (Well-Architected framework), different competencies (DevOps, Migration, Data Lake and few more), as a part of Minfy Services as Partners.

Customer Challenges

U GRO faced a multitude of technical challenges, encountering delays in applying critical updates due to irregular security measures. This exposed the system to vulnerabilities, necessitating meticulous patch management. The low-security hub score highlighted the need for a detailed security enhancement strategy, requiring in-depth analysis. In addition, the absence of a real-time monitoring dashboard complicated management, and cost oversight became a major challenge. Moreover, organizational inefficiencies, including a lack of account stability, posed challenges, making cost reductions and financial optimizations complex. Therefore, a strategic and technical approach was imperative to fortify security, streamline costs, and establish an organized operational framework. U GRO reached out to Minfy to enlist us as their MSP to solve these issues.

Partner Solution

When Minfy engaged in routine support operations with U GRO, a comprehensive analysis revealed an abundance of underutilized resources, including dormant volumes, idle IP addresses, and lingering floating snapshots. In response, a proactive strategy was initiated, incorporating automated scripting and alert mechanisms for routine sanity checks. This systematic process served the dual purpose of ongoing vigilance over idle resources and concurrent endeavors to optimize costs for U GRO. The automation of these checks ensured a preemptive approach, systematically identifying and addressing unused resources, fortifying the system's efficiency, and mitigating unnecessary costs.

It was also noted that the S3 storage size progressively grew, resulting in runaway costs. To address this a lifecycle policy was recommended to manage costs and restrict storage capacity, Minfy suggested moving the data from standard to archival storage. In terms of data transfer, the month of September 2023, recorded a total outbound data volume of 63 TB. To address this, a review of log store data and lifecycle policies in CloudWatch was advised for optimal system management.

To address the lack of real-time monitoring, Minfy deployed Swayam™ (Minfy's in-house tool). It automated reports and played a pivotal role in enhancing resource management and cost optimization. By leveraging automation, integrity in the infrastructure was continually ensured, contributing to U GRO's fiscal prudence by identifying avenues for resource optimization and cost reduction proactively and systematically through the CXO dashboard and regular cost optimization.

Results

The implemented solution for U GRO yielded transformative outcomes, enhancing patch management to fortify system security and significantly improving the low-security hub score. Proactively identifying and optimizing underutilized resources streamlined financial processes. The introduction of real-time monitoring tools like Swayam and the CXO dashboard bolstered cost oversight for fiscal prudence. Addressing organizational challenges improved operational efficiency and reduced risks, successfully fortifying system security, optimizing costs, and establishing a structured operational framework. This integrated approach aligned U GRO with industry best practices for sustained success. Cost savings initiatives achieved significant benefits, with USD 8,904 in savings attributed to RDS and an additional USD 134 in savings related to EC2, reflecting a cumulative monthly savings of USD 9,038 through successful expense optimization. Following AWS best practices in the U GRO environment resulted in a subsequent Security Hub score improvement from 42% to 70%, addressing initial security glitches and ensuring a robust and secure environment.

About Minfy

Minfy specializes in multi-cloud support, advanced AWS infrastructure monitoring, Database (DB) support, and Application Modernization. Recognized for industry excellence and innovation, Minfy tailored comprehensive solutions for Uno Minda, showcasing expertise in multi-cloud environments and AWS services. Minfy actively participated in cost optimization exercises, demonstrating a commitment to efficiency and fiscal responsibility. We conduct APN programs, such as WAR (Well-Architected framework), different competencies (DevOps, Migration, Data Lake and few more), as a part of Minfy Services as Partners.

Customer Challenges

Chola was confronted with a critical need for seamless and controlled software deployments. Lacking an in-house development team, they heavily relied on external vendors, leading to a fragmented landscape with multiple vendors and disjointed deployment pipelines. This approach resulted in operational challenges, exacerbated by a continuous influx of change requests from end customers and insurance aggregators. These challenges led to significant downtimes, negatively affecting the end-user experience. To address this, Chola aimed to establish a centralized repository to streamline and govern the deployment process for improved efficiency and consistency.

Why Minfy

Chola selected Minfy as its DevOps partner due to their status as an AWS Premier Partner, extensive experience in complex DevOps projects, the required competencies to build the proposed solution, and their existing partnership with Chola as a managed services provider. This choice was underpinned by the confidence in Minfy's expertise and their ability to deliver a robust and tailored DevOps solution, aligning seamlessly with Chola's cloud-focused objectives.

Minfy’s Solution

Application Discovery:

Minfy initiated the solution by conducting a comprehensive application discovery process, which included identifying the number of applications in Chola's ecosystem, profiling the application stack, and identifying dependencies. This insight provided a clear understanding of Chola's application landscape and was pivotal for the subsequent DevOps activities.

DevOps Solution Setup Activities:

•  AWS Cognito with SAML against ADFS + MFA: Minfy implemented AWS Cognito with SAML authentication against ADFS, enhancing security with Multi-Factor Authentication (MFA).
•  AWS Code Commit Repo Setup: Minfy established AWS Code Commit repositories, offering a secure and efficient version control system for Chola's applications
• Jenkins Master and Slave Setup: Minfy configured Jenkins Master and Slave (Windows) environments as per the proposed specifications. Additionally, they integrated AWS Cognito with Connect ID and set up an S3 bucket for enhanced functionality.
• Sonarqube with AWS ADFS SAML Integration: Minfy integrated Sonarqube with AWS ADFS for seamless Single Sign-On (SSO) access and efficient code quality analysis.
• CICD Pipeline Configuration: Minfy created CodeCommit repositories for the identified applications and established Continuous Integration and Continuous Deployment (CICD) pipeline scripts, ensuring streamlined code deployment.
• Source Code Commit: Chola's development team and vendors were provided access to commit source code to AWS Code Commit, facilitating collaborative development.
• Deployment, Validation, and UAT Deployment: The solution included deploying the code to the development environment, validating its accuracy and functionality with input from the development team, and then merging and deploying it to UAT servers for comprehensive testing and quality assurance.

Challenges Faced by Minfy During Implementation:

A notable challenge encountered by Minfy during the implementation was the integration of SAML with Jenkins while implementing Active Directory (AD) authentication. A hurdle in this process was the absence of native support for AD authentication in Jenkins. Chola's requirement to onboard multiple vendors onto the AD system further added complexity, especially with their strong preference for avoiding the creation of separate logins for Jenkins. The overarching goal was to ensure that vendors who were already authenticated by the AD could effortlessly access Jenkins without the need for additional login credentials.

To address this challenge, Minfy effectively resolved the issue by enabling AD authentication using AWS Cognito. This solution streamlined the login process for vendors authenticated by the AD, eliminating the need for separate Jenkins credentials. This strategic implementation not only tackled the SAML integration issue but also provided a seamless experience for Chola's users, simplifying access to Jenkins and improving overall efficiency and security within the DevOps ecosystem.

Business Outcomes

Minfy's comprehensive solution effectively addressed Chola's pressing challenges, resulting in transformative outcomes. By streamlining software deployments and fostering collaboration among multiple vendors, Minfy reduced time to deployment and market by 40%, facilitating faster and more agile development cycles. Additionally, the integration of Sonarqube significantly enhanced Chola's security posture, improving code quality and fortifying the codebase. This holistic approach not only tackled Chola's initial challenges related to fragmented deployments but also laid the foundation for heightened efficiency, security, and competitiveness within the evolving landscape of DevOps.

Solution Offerings

Solution 1: Data Digitization

The process of converting unstructured data into structured/semi-structured data involves several steps, including data extraction from documents, data cleaning and pre-processing, data mapping, data standardization, and data normalization. After these steps are completed, the data is typically stored, analysed, and used to generate insights.

AWS offers an array of tools and services to assist healthcare stakeholders in unlocking the full potential of their data. Our solution utilizes various AWS services like Amazon Textract to process a small sample of documents, extracting relevant data and standardizing for further processing.

Extraction: The foundation of utilizing unstructured healthcare data lies in the extraction of relevant information from various sources such as images, PDFs, and scanned documents. This process involves leveraging the power of Optical Character Recognition (OCR) algorithms and tools, such as the widely available services like Amazon Textract. These algorithms and tools enable us to recognize, extract, and define output from the unstructured data, making it machine-readable and ready for further analysis.

We used Amazon Textract to perform the OCR and digitize the data. An instance of a sample lab investigation report ran through Textract to get the extracted information in the CSV or Excel format.

The corresponding output file of the unstructured document:

Processing: Once the data is extracted, we are standardizing it to a uniform format for meaningful comparison and calculations. Natural Language Processing (NLP) techniques play a vital role in extracting valuable information from textual data. This feature allows a healthcare provider to look from the complete document and get an understanding of mapping of what were the diagnoses, symptoms, treatments and qualities etc.

Data Mapping, Normalization, & Standardization: The extracted data is diverse and needs to be transformed to a common scale to facilitate meaningful comparison and calculations. We transformed the data into the standard forms to ensure homogeneity of medical concepts across different datasets.

Because data ontology plays a crucial role in establishing a common ground for understanding the data, thereby enhancing the accuracy and relevance of analyses by defining the relationships between different medical concepts.

Solution 2: Advanced Analytics and Visualizations on Patient Data

We have successfully extracted, cleaned, and pre-processed the data. As a result, it is now structured and ready to be used for analytics and visualizations. We started with performing a descriptive analysis of the patient data to gain insights into their current health status. We used Amazon QuickSight to create dashboards that visualize the medical data.

For instance, we had patient lab investigation reports, and we had extracted the information along with unique identifiers such as UHID. We then performed the necessary preliminary steps to ensure that the data is usable.

Our descriptive analytics involved creating a dashboard that displays vitals trends, health parameters abnormalities, a timeline view of medical events, the distribution of normality, and the classification of parameters based on abnormality. We also included a health parameter value change indicator to compare the changes during a specific period.

These powerful visualization techniques would enable healthcare professionals to determine patterns, trends, and correlations within the data - providing valuable insights into patient health, current stage of the disease and potential medical outcomes.

Solution 3: AI Powered Sentiment Analysis of Nutraceutical Product Reviews

For performing the sentiment analysis, Minfy used their in-house solution – Sentiment.ai. Sentiment.ai is a solution that uses AI/ML to analyse the sentiment of text data to understand the emotions and opinions. ​

With the help of Sentiment.ai, we enabled Ayur.AI to better understand the customer emotions about products, make data-driven and informed decisions, and provided real-time analytics, trend detection, and sentiment tracking to capture the pulse of customer sentiment.

The analysis helped Ayur.AI to accurately understand the sentiment of customers, proactively addressing the detected issues or concerns, and finally to use transparent and data-driven approach. Overall, the solution enhanced customer satisfaction, personalized marketing strategies, optimized product assortment and improved Ayur.AI’ s reputation across retailers.

Outcomes

The following outcomes were achieved by Ayur.AI during their collaboration with Minfy:

Effective Data Utilization: Ayur.AI successfully transitioned from unstructured data formats like images and PDFs to structured data. The data digitization process enabled Ayur.AI to harness valuable insights from previously untapped data sources.

Proactive Healthcare Approach: With the ability to track patient progress over time, Ayur.AI adopted a proactive and preventive approach to healthcare. Advanced analytics provided a comprehensive view of patient health, leading to informed decision-making for doctors.

Data-Driven Decision-Making for Products: Ayur.AI gained a deeper understanding of customer emotions and opinions regarding their nutraceutical products listing. Real-time sentiment analysis empowered Ayur.AI to make data-driven decisions, updating listing of products in real-time.

Conclusion

Healthcare systems are at a critical juncture in the dynamic world of smart healthcare. While fundamental principles and systems have been in use for a long time, the introduction of new technologies opens a wide range of opportunities for growth and advancement.

The adoption of technological developments has moved from voluntary to mandatory in a fast-expanding healthcare market. It is the path that will allow us to provide efficient, patient-centric care, ultimately leading to better health outcomes for everyone.

As a result, it is critical that we work together, push the boundaries, and truly embrace the transformative force of innovation as we journey towards a future where healthcare’s potential is limitless.

Co-Author - Dr. Bala Pesala | Founder & CEO, Ayur.AI

Co-Author – Yedhu Krishnan B | Senior Full Stack Developer, Ayur.AI

Author – Gaurav Lohkna | Data Scientist, Minfy

Challenges

Pos Malaysia’s existing business model uses on-premises data centers for storing and processing all the data generated across different services. The company stores a large amount of data on-premises database which is used by different business units for data processing, transformation and further analysis. As the company is seeing an increase in orders, they are looking forward to leveraging cloud technology to build a data warehouse for data analytics. The company wants to build an end-to-end ML pipeline using this data warehouse to incorporate advanced ML algorithms that can predict ETA (Estimated Arrival Time). The company is also faced with challenges like scaling during data spikes and vertical scaling for heavy workloads. A secure solution to maintain and monitor different APIs for delivering integrated solutions. The company is therefore looking for a cost-effective solution that can address the above requirements and reduce its operational overheads. Being an AWS premier partner, Minfy brings in a range of deep cloud expertise in migration and modernization. Minfy believes that the client can modernize its database for scalability and security and leverage serverless architecture for its workflows. Minfy proposes various data analytics services offered by AWS for building advanced data analysis and ML pipelines enabling the company to be more agile in delivering its solution.

Client Expectation

• A robust and scalable infrastructure solution with minimal spending
• An architecture that can allow provision-on-demand spikes.
• Efficient and fast data warehouse solution for quick insights
• Low latency storage with minimum operational overheads
• Fast response and deployment times for APIs
• Security of data at rest and in transit across services

Results and Benefits

• Using API Gateway, the client was able to handle a huge number of requests. They were further able to reduce the cost using the tiered pricing model of this service. Since API Gateway is a fully managed service, the client was also able to reduce the operational efforts in managing the APIs and tracking requests.
• Since Lambda was querying results from the DynamoDB, the customers got the response from the API with very low latency even during peak traffic.
• Using AWS Glue, Redshift, Batch and Sagemaker, the client was able to implement the ML pipeline to deliver quick results. Using AWS Batch, the client was able to run Batch computing reducing the cost significantly

Customer Background

Founded by Mr. Jaidayal Dalmia in 1939, Dalmia Cements is a pioneering cement company in India. Headquartered in New Delhi, the company operates as a 100% subsidiary of Dalmia Bharat Ltd. The company has a manufacturing capacity of 35.9 million tons per annum (MTPA) across 14 plants and grinding units located in 10 states. With over 33,000 dealers and sub-dealers, the company serves more than 22 states and is a leading player in each region. The company has grown through acquisitions and greenfield expansions to scale and venture into new territories. Dalmia Cement is unique in that it has at least one plant in each of the four major eastern states of West Bengal, Bihar, Jharkhand, and Odisha.

Challenges

The Dalmia Group has their workloads hosted on-premise and in a third-party data center. An assessment identified a total of 123 servers and 11 applications currently hosted in these locations. They were looking to move their workloads to AWS due to the poor performance of the
prevous data center. The goal is to centralize their infrastructure, modernize their applications, and reduce costs, including license costs. They also want to transform their applications and make them future-ready in order to accelerate innovation.

Business Drivers

Business Driver 1: Private cloud contract extension on the cards, Critical Business Applications & ERPs hosted on the private cloud.
Business Driver 2: Limitations / Constrains on the tech front to scale/innovate their business faster in on-premise infrastructure

Solutions

Minfy leveraged the Migration Acceleration Program to create the business case for migration to AWS. We designed and implemented a
highly-available infrastructure for their SAP workloads on AWS. Additionally, Minfy migrated Dalmia’s critical databases as well as their non-SAP applications. We also defined & implemented a Cloud Operating Model & transitioned Dalmia’s cloud-ops to Minfy’s Managed Services
Support.

About Minfy

Minfy Technologies is a Cloud Native System Integrator helping enterprises, start-ups and fast-growing businesses navigate digital journeys leveraging AI & Cloud technologies. We assist our customers in accelerating Digital Transformation, Cloud Adoption and Innovation. Our transformative services include Cloud Consulting, Migration & Legacy Modernisations, Cloud-Native Application Development and DeepTech implementations while offering reliable and world class 24x7 managed services.

Customer Background

Cyient is a leading Indian multinational technology company specializing in engineering, manufacturing, data analytics, and networks and operations. With over 15,000 associates in 19 countries, they provide services and solutions to a diverse customer base of over 300 clients, including 29 Fortune 500 companies. Cyient's approach combines global delivery with local relationships, innovation, and digital expertise.

Challenges

Business Challenge:

• Overprovisioning of Licenses and costs associated with it.

• Freedom from Licensing and Vendor Lock-in Challenges.

Specific Technical Challenges:

• The end-customer had access restrictions for DMS GUI usage by Cyient & Minfy on AWS console and AWS CLI was used to solve this issue

• Converting complex Oracle SQL code objects to PostgreSQL for COTS app required significant engineering effort.

• Achieving HADR, DR, Scale-up/out with Oracle posed cost & management challenges which we resolved as a part of our solution

Technical Solutions

Features of the Solution:

•  Used AWS SCT and AWS CLI with DMS for migration to AWS Aurora PostgreSQL

•  Customized Ora2PG integration for SQL code object conversion

•  Code conversion for schema and SQL/DB objects with open-source accelerators.

•  Factored RDS Multi-AZ and Read Replicas for HADR & read scaling

•  Open-source customizations for BLOB/CLOB tables and CDC using DMS.

•  Designed and executed open-source PostgreSQL APMs for performance insights

•  Provided SQL query optimization recommendations with Aurora PostgreSQL APM reports

About Minfy

Minfy Technologies is a Cloud Native System Integrator helping enterprises, start-ups and fast-growing businesses navigate digital journeys leveraging AI & Cloud technologies. We assist our customers in accelerating Digital Transformation, Cloud Adoption and Innovation. Our transformative services include Cloud Consulting, Migration & Legacy Modernisations, Cloud-Native Application Development and DeepTech implementations while offering reliable and world class 24x7 managed services.

Specific Outcomes

Through Minfy's migration initiative, Mjunction achieved enhanced application performance and scalability with dynamic autoscaling, optimized costs by transitioning to open-source PostgreSQL databases, established a secure landing zone ensuring compliance, and seamlessly migrated their critical application to AWS, ensuring uninterrupted service. This modernization drive equips Mjunction for future growth while providing immediate benefits in efficiency, cost, and user experience.

About Minfy

Minfy Technologies is a Cloud Native System Integrator helping enterprises, start-ups and fast-growing businesses navigate digital journeys leveraging AI & Cloud technologies. We assist our customers in accelerating Digital Transformation, Cloud Adoption and Innovation. Our transformative services include Cloud Consulting, Migration & Legacy Modernisations, Cloud-Native Application Development and DeepTech implementations while offering reliable and world class 24x7 managed services.

Challenges

•  IAM User best practice: POS Digital account has IAM users and wanted to improve their security posture in terms of IAM users, password policy, temporary access, secrets manager etc by implementing best practices to protect against unauthorized access across the cloud environment.
•  Automation: POS Digital account wanted to have a reliable platform with a modernized environment by leveraging AWS cloud-native services with higher fault isolation.
•  Reliability: POS Digital account’s objective is to enhance their testing process to validate the multi-deployment strategy along with the auto-scaling group to minimize the peak load performance.
•  Higher Availability for Application Recovery: POS Digital account wants to improve its application resiliency with multi-account strategy with a disaster recovery plan.

Customer Outcomes

•   Improved Security: The POS Digital account has enabled security posture across the environment in terms of MFA and IAM best practice and have enabled IMDSv2 to protect all the EC2 instances.
•   Enhanced Reliability: POS Digital account enabled auto-scaling group with a highly reliable environment. And customer has performed distributed load testing using Apache JMeter.
•   Cost Optimization: AWS Compute Optimizer has been enabled to have more control over the rightsizing of resources and addressed RI instances for better cost optimization.
•   Security Standards: End-to-end monitoring has been enabled on both the infra and app layer with application insights. Also tested some canary scripts for Synthetic monitoring on the web layer.

About Minfy

Minfy Technologies is a Cloud Native System Integrator helping enterprises, start-ups and fast-growing businesses navigate digital journeys leveraging AI & Cloud technologies. We assist our customers in accelerating Digital Transformation, Cloud Adoption and Innovation. Our transformative services include Cloud Consulting, Migration & Legacy Modernisations, Cloud-Native Application Development and DeepTech implementations while offering reliable and world class 24x7 managed services.