Enhancing Data Integrity and Automation: A Scalable Solution for Sponsorship Analytics

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Introduction

In the ever-evolving world of sports sponsorship analytics, maintaining data integrity and automation is crucial for effective decision-making. Our client, a leading sponsorship intelligence platform, identified the need for a robust, scalable, and automated data quality framework to enhance data accuracy and streamline operations. To address this, our team developed a comprehensive data quality and automation system, ensuring reliable insights and efficient data handling across platforms.

Client Overview

Our client provides real-time data and analytics to the sports and entertainment industries. To maintain a competitive edge, they required a scalable solution to automate data quality checks, alerting, logging, and AI-powered integrations. The goal was to ensure seamless data ingestion, integrity, and processing for enhanced analytics and decision-making.

Technical Challenges

The existing data processes faced several challenges, including:

Manual Data Quality Checks: Lack of an automated system for detecting data anomalies and inconsistencies in Redshift.

Scalability Issues: Increasing data volume required a more flexible and extensible system.

Alerting and Monitoring Gaps: Lack of automated alerts and logging mechanisms led to inefficiencies in identifying system issues.

Infrastructure Optimization: Enhancing data ingestion capacity and execution automation.

AI-Driven Enhancements: Integrating AI-powered connectivity to optimize analytics.

Complex Data Ingestion and Processing Needs: Required a modular and scalable architecture to handle diverse data sources.

Technical Solution

Our solution focused on building an end-to-end data quality framework, automating alerting mechanisms, and optimizing data infrastructure, including:

1. Data Quality Framework Implementation

Developed a flexible SQL-based framework to define and execute data quality rules in Amazon Redshift.

Automated anomaly detection using predefined thresholds for Year-over-Year asset comparisons across properties and teams.

Designed scalable workflows to allow users to add and modify data quality rules efficiently.

2. Redshift Alerting, Monitoring, and Logging

Implemented CloudWatch logging to track system performance and detect anomalies.

Set up utilization alerts for memory, disk space, and CPU in Redshift and EC2.

Configured Slack and email notifications for real-time alerting on system health and long-running queries.

3. Airbyte Worker Capacity Enhancement

Increased the number of Airbyte workers in the deployment to improve data ingestion and processing efficiency.

4. Materialized Views Checksum & Uptime Assurance

Implemented DBT-based validation to ensure materialized views remain available post-Redshift maintenance.

Automated recreation of materialized views upon schema changes, ensuring Tableau uptime and data consistency.

5. AI-API Integration and GPT Connectivity

Migrated AI-API database from MySQL to Redshift for enhanced performance and scalability.

Collaborated with AI teams to integrate GPT-based insights and analytics into the client’s ecosystem.

6. Automating DBT Execution via CI/CD

Established a CI/CD pipeline triggering DBT macro and model execution upon Git main branch updates.

Ensured consistent and automated deployment of data transformations.

7. Airbyte Cluster Setup on EKS for Dev and Prod

Automated Airbyte cluster deployment on Amazon EKS for scalability and improved data pipeline management.

Standardized the setup process for dev and prod environments, ensuring seamless infrastructure deployment.

8. EKS Migration & GitOps Implementation

Migrated from traditional infrastructure to Amazon EKS for enhanced scalability and container orchestration.

Deployed ArgoCD to establish GitOps workflows for Helm-based application deployments.

Integrated CI/CD pipelines with EKS to streamline deployment, rollback, and version control processes.

9. Multi-Model Ingestion Layer (MMIL) Pipeline

Architecture Overview

Implemented a modular pipeline architecture to handle diverse data sources and process them efficiently.

Integrated Apache NiFi for seamless data ingestion, transformation, and processing.

Designed the architecture to support multiple output destinations, including VectorDB and Redshift.

Architecture Components

Agent/Processor: Java-based Docker service responsible for integrating with data sources and performing initial data preparation.

Ingestion Service: Handles the intake of data and initiates the data processing workflow.

Data Processing Service: Standardizes, cleans, and transforms incoming data using parsers and regular expressions.

Enrichment Layer: Provides real-time or batch-based metadata enrichment to enhance data context.

Chunking Layer: Divides processed data into manageable chunks for optimized storage and retrieval.

Embedding Layer: Transforms data into vector embeddings for semantic search capabilities.

Output Destinations

VectorDB: Stores vector embeddings for search and retrieval.

Redshift: Stores structured data for reporting and analytics.

Spike Implementation: Use Cases

Sitemap Pipeline

Dedicated service handling sitemap ingestion and processing.

Java Agent processes and prepares sitemap data for ingestion.

Parsed and transformed data stored in ChromaDB.

Contacts Pipeline

Local pipeline service for ingesting contact-related data.

Java Agent processes contact data locally.

Parsed and structured data stored in ChromaDB for semantic search.

Results

The implementation of these solutions resulted in significant improvements:

Efficiency: Automated data quality checks reduced manual efforts and improved accuracy.

Scalability: EKS migration, MMIL, and Airbyte enhancements improved data ingestion and processing.

Reliability: Real-time alerts and automated recovery mechanisms ensured system uptime and integrity.

Automation: CI/CD pipelines streamlined deployment, reducing operational overhead.

AI-Driven Insights: GPT connectivity enhanced the platform’s analytical capabilities.

Conclusion

Our collaboration with the client led to a cutting-edge data quality framework and infrastructure automation. The solutions implemented provided a scalable, secure, and highly automated system, ensuring data integrity, operational efficiency, and enhanced analytical capabilities. The MMIL pipeline integration further strengthened data ingestion, transformation, and processing, ensuring a future-proof solution for sponsorship analytics.