Introduction to our Client Our client is the world's leading electronic measurement company, transforming today's measurement experience through innovations in wireless, modular, and software solutions. They deliver solutions for wireless communications, aerospace and defense and semiconductor markets with world-class platforms, software and consistent measurement science. Challenge Our client was keen on building an architecture for a collaboration platform. They were looking for a solution that would avoid maintenance and management of any server workload. AspireNXT Solution Aspire NXT helped them to create a serverless compute environment using AWS Lambda, which is completely managed by AWS and does not require any servers to run applications. The AWS Lambda function is used for backend application to verify tokens and if validated, the access is granted to the API. Additionally, AWS Lambda function helped in the conversion of excel file to JSON data and store in its respective AWS S3 bucket. Benefits Since AWS Lambda is fully managed service, they need not think about the underlying server’s management. By availing AWS Lambda service, they save at least 80% of the actual cost required for computing services. Get In Touch

Introduction to our Client Our customer runs a wide range of services, including the daily Asia vision TV news exchange, several co-productions, program exchanges, legal and management consultancies, international frequency planning and coordination. It works closely on common concern such as reserving frequencies for broadcasters, harmonization of operating and technical broadcasting standards and systems and finalizing the Broadcasting Treaty. Challenge Our customer was keen on building an architecture for a collaboration platform. They were looking for a solution that would avoid maintenance and management of any server workload. AspireNXT Solution Aspire NXT helped them to create a serverless compute environment using AWS Lambda, which is completely managed by AWS and does not require any servers. AspireNXT used AWS Elastic Transcoder Service to convert the media file from the original format to any desirable format like AAC, MP3, MPEG, MP4, WAV, MKV, FLV, etc. by using AWS Lambda Service. Both the original and converted media files are stored in their respective AWS S3 bucket, which is an AWS Storage service. Benefits Since AWS Lambda is fully managed service, our client does not need to think about the underlying server’s management. DynamoDB helps in getting insight reports on various media. By availing AWS Lambda service, our client saved at least 80% of the actual cost required for computing services. Get In Touch

Introduction to our Client The Manufacturing industry provides solutions and trusted insight accelerate innovations in electronic design, test, manufacturing, and optimization. Challenge The Datawarehouse was Oracle DWH Reverse Engineering on the Data Trace was getting challenging day by day as there was no appropriate Data Model created at the design phase for instance, if there were issues addressed by users then tracing a Single Column back to source and mapping it to target took close to a week. AspireNXT Solution 1. Oracle DWH was studied, and 2 Major Sales order tables were picked for Migration to Redshift 2. Data Model was built as part of this Study Phase 3. Columns 1-1 Mapping was performed 4. Data from Oracle in CSV format was pulled to AWS S3 data lake using AWS GLUE 5. Data Was catalogued using AWS GLUE 6. External Hive tables were created using GLUE 7. These External Tables were then Exposed to Athena for Ad-Hoc querying. 8. Data model was deployed on AWS Redshift which was designed in Step 2 9. Data from S3 was ingested to Redshift VIA AWS GLUE 10. Modern DWH was made available for Analytics and for downstream systems. Services Used 1. Glue 2. Athena 3. AWS RDS Postgress 4. AWS DMS 5. IAM 6. S3 Benefits Move Legacy Datawarehouse to Modern Datawarehouse in AWS with appropriate Data Models and Mappings Industry’s initial direction was TIBCO Spotfire for BI, however we did some comparison studies on functionalities, ease of use, support and Serverless with AWS Quick Sight and helped the team to Build their first Prod ready Dashboard on AWS Quick Sight Management very well appreciated the AWS Cloud Journey. The End to end Project was completed in ~6 weeks Get In Touch

Introduction to our Client They are more than just a regular mobile payment app on the App Store and Play Store. It is proudly Malaysia’s homegrown lifestyle e-wallet app that is both secure and rewarding. Their mission is to provide everyone with a worry-free cashless mobile payment experience all-day, every day. Challenge Our client was keen on Building Data lake on AWS S3 which will help them consolidate all their Mongo Sources at one central location, which will in turn help them do Advanced Analytics on their Customer Data. Note this project involved ingesting PII Data to AWS S3 AspireNXT Solution 1. Study was conducted on their 150 + Mongo Documents 2. Ingestion Server was set up on AWS 3. Glue Local End point was dockerised and was deployed on Ingestion server 4. Wrote a Custom SSL Python JDBC Connector on Glue Local DEV Endpoint to connect to Mongo 5. AWS DMS was configured to pull data from Mongo Servers 6. Data from Mongo in Deeply Nested JSON forms was pulled to AWS S3 data lake 7. Data was parsed to CSV using AWS GLUE out of box Spark code was used + Custom code on GLUE was developed with parsing logic 8. Data Was catalogued using AWS GLUE 9. External Hive tables were created using GLUE 10. These External Tables were then Exposed to Athena for Ad-Hoc Analysis 11. Data model will be designed, and the Data will be Ingested to Postgres RDS 12. ELT’d data will then be connected to Sagemaker to perform Descriptive and Prescriptive Analytics -- In Pipeline 13. Lake formation is used to control Athena Col level access as we are ingesting PII data 14. Custom One side Hashing Algorithm is built on GLUE using Python Services Used 1. Glue 2. Athena 3. AWS RDS Postgress 4. AWS DMS 5. IAM 6. S3 Benefits AspireNXT helped our client take advantage of Redshift, that makes it simple and cost effective to run high performance queries on petabytes of structured data so that they could build powerful reports and dashboards using their existing business intelligence tools. Bringing together structured data from your data warehouse and semi-structured data such as application logs from your S3 data lake to get real-time operational insights on your applications and systems. As the company continues to grow, it takes advantage of Amazon Redshift and Amazon EMR to run complex queries on large and growing data sets with improved performance. Get In Touch