Amlgo Labs

The solution processes large-scale historical and incremental data using a distributed cloud architecture. Each property handles approximately 4 GB of historical data with ~40 MB of incremental data, processed through a 12-node AWS EMR cluster. Workflow orchestration is managed using Oozie, ensuring reliable and scheduled execution.

All business logic and data transformations are implemented directly within the Elevate system using PySpark, enabling scalable and efficient data processing. To maintain high data quality, Amazon S3 source and publish buckets are leveraged for additional validation checks, an approach already standardized within the organization and extended to this implementation.

Snowflake serves as the centralized analytics data warehouse, from which curated data is consumed by Tableau to deliver insightful dashboards and visualizations for business users.

Technology Stack

• Computation: AWS EMR (PySpark)

• Storage: Amazon S3, Snowflake

• Version Control & CI/CD: Jenkins

• Visualization: Tableau

• Orchestration: Oozie, Apache Airflow
  
  Keywords: Cloud data platform, large-scale data processing, distributed data architecture, incremental data processing, historical data ingestion, AWS EMR, PySpark, Amazon S3, Snowflake, Tableau, Jenkins, Apache Airflow, Oozie, Data validation checks, source & publish buckets, data quality framework, standardized data pipelines

Our client, a global financial services firm, requires a robust data pipeline to ingest both historical and daily data. The goal is to enable the client’s OpenPages platform to analyze this data and generate actionable insights, ultimately supporting business decisions that drive revenue growth.

The Amlgo Labs team is assisting the client by:

• Designing and building efficient data pipelines.
• Scheduling daily and monthly jobs to ensure timely data processing.
• Monitoring the pipelines and jobs on a daily basis to maintain reliability.

The solution involves:

• Apache Spark for fast and scalable data processing.
• Arturo for deploying the pipelines in UAT and PROD environments.
• Oozie for scheduling and automating the data workflows.
  
  Keywords: Data pipeline development, OpenPages analytics, enterprise data ingestion, actionable insights, financial services analytics, Apache Spark, Oozie, Arturo, ETL pipelines, workflow automation, batch processing, Data engineering, pipeline orchestration, reliability monitoring, UAT & production deployment, operational data management

A persona-based GenAI conversational chatbot built to ingest and analyse large volumes of structured and unstructured public data, such as social media reviews and customer feedback. The solution enables product decision teams to interact conversationally with the data to uncover customer needs, sentiment trends, and feature insights without conducting traditional market surveys. It includes an integrated dashboard displaying key KPIs and visualizations, primarily focused on sentiment analysis and trend monitoring.

Demo was successfully completed in AMLGO’s environment, validating technical feasibility and business value

Keywords: AI-persona chatbot, generative AI chatbot, conversational AI, AI agents, enterprise chatbot, GenAI solution, Natural language processing (NLP), large language models (LLMs), sentiment analysis, recommendation systems, voice & speech recognition, computer vision, AI image generation, big data analytics, BI dashboards.

• We provide end-to-end implementation and support for AxiomSL solutions across Development, Testing, UAT, and Production environments on both Unix and Windows platforms. Our team ensures seamless system setup, including optimised Apache configurations to support high-performance Axiom dashboards.
• We design and develop core AxiomSL objects to implement complex business logic, covering portfolios, aggregations, workflows, taxonomies, and related configurations across Dimensions, Facts, and Cubes.
• Our expertise also includes secure and reliable code migration across environments using modern migration tools, along with validation utilities to ensure accuracy and consistency of reports.
• To enhance operational efficiency, we leverage Robotic Process Automation (RPA) and AWS cloud services, EC2, Redshift, Glue, Athena, and S3 to automate workflow refreshes, update dashboard metrics, and enable scalable, data-driven reporting.
  
  Keywords: AxiomCV implementation, AxiomSL implementation, regulatory reporting, financial analytics, enterprise reporting, data-driven reporting, AxiomSL, AxiomCV, Apache, Unix, Windows, AWS, EC2, Redshift, Glue, Athena, S3, RPA, cloud automation, Dimensions, facts, cubes, portfolios, aggregations, taxonomies, business logic modeling, data pipelines

Objective of Defect Correlation & Forecasting for New Model Recurrence Prevention

The objective of defect correlation and forecasting is to leverage all available QA datasets to prevent defect recurrence in new models. This initiative focuses on:

• Establishing correlations across QA datasets to evaluate overall component and supplier performance.

• Forecasting high-risk components and potential issues to enable early prevention in new model launches.

• Analyzing warranty claims by correlating them with line defects, plant defects, and historical new model development data.

• Assessing component and supplier performance across the entire vehicle lifecycle.

• Monitoring the effectiveness of recurrence prevention measures at the model, component, and supplier levels.

• Detecting and triggering alerts for sudden changes in defect trends, warranty claims, or quality issues.

• Enabling proactive actions during new model development, including design reviews and additional or special testing.
  
  Keywords: Defect correlation, defect forecasting, recurrence prevention, QA datasets, warranty claims, new models, Component & supplier performance, high-risk components, line & plant defects, vehicle lifecycle, defect trends, quality issues, Correlation analysis, early prevention, proactive actions, design reviews, special testing, trend monitoring, alerts & effectiveness tracking