Case Study

Syndication Data Engineering (SDE) Transformation: Transforming Data Engineering with AI-Driven Cloud Migration

A seamless transition enhancing real-time data processing, scalability, and cost efficiency.
Futuristic AI-powered cloud transformation representing data migration, automation, and seamless cloud connectivity for enhanced scalability and efficiency in data engineering.
%

Reliability for ETL Jobs

x

Reduction in Query Execution Time

$ K/month

Operational Cost Savings

Background

A major US cable operator relied on a Hadoop-based data platform for syndication and quality programs but faced high operational costs, fragmented workflows, and latency issues in real-time data processing. Managing scattered ETL jobs, multiple tools, and delayed reporting impacted efficiency. Tata Elxsi migrated the Syndication Data Engineering (SDE) platform to AWS serverless computing, integrating for seamless ETL execution. A unified dashboard on AWS QuickSight enabled real-time analytics, while machine learning-driven data validation enhanced accuracy. This modernized infrastructure streamlined cloud operations, improved scalability, and reduced operational costs.

Challenge

The media & communications industry faces growing complexity in data processing, cloud migration, and real-time analytics. A major US cable operator encountered key challenges:

  • Scattered Data Processing – ETL jobs were fragmented across multiple platforms, causing inefficiencies.
  • High Operational Costs – Maintaining on-prem Hadoop clusters led to rising cloud expenses and scalability issues.
  • Data Latency Issues – Lack of real-time data availability impacted syndication billing and analytics.
  • Complex Cloud Transition – Migrating ETL jobs required seamless integration and cost governance strategies.

Solution

Tata Elxsi enabled a seamless migration for transformation of streamlined ETL workflows, data automation, and real-time analytics, ensuring cost efficiency and scalability.

Key Solutions:

  • Cloud-Based ETL Migration – Moved Hadoop workloads to AWS Databricks & Spark, enhancing data reliability and processing speed.
  • AI-Driven Process Automation – Implemented automated ETL pipelines for data cleaning, enrichment, and validation, improving syndication accuracy.
  • Unified Data Engineering Portal – Consolidated operations and performance metrics from Tableau, Splunk, and OIV into a centralized dashboard.
  • Real-Time Data Insights – Leveraged machine learning-driven analytics and predictive intelligence.
optimizing real-time data processing, ensuring seamless syndication and analytics
AI-powered cloud orchestration optimizing data workflows, automation, and security in Syndication Data Engineering.
AI-powered cloud transformation driving efficiency, security, and seamless syndication data operations.

Impact

Tata Elxsi’s Syndication Data Engineering (SDE) Transformation is revolutionizing cloud-based data processing with AI-driven automation, serverless cloud migration, and predictive analytics. By optimizing ETL workflows and real-time data validation, the transformation delivers cost efficiency, scalability, and accuracy for syndication operations.

Key Achievements:

  • 99.44% reliability in ETL jobs, ensuring uninterrupted data processing.
  • 2x faster query execution time, improving real-time analytics.
  • $5K/month operational cost savings by eliminating on-prem Hadoop maintenance.
  • Unified dashboard on AWS QuickSight, streamlining syndication metrics & reporting.
  • AI-driven automation & zero-trust security, enhancing compliance and risk mitigation.

Services rendered

Tata Elxsi

  • Unified Data Portal – Integrated Tableau, Splunk, and OIV into a centralized dashboard.
  • AI-Driven Automation – Optimized ETL workflows, data validation, and enrichment.
  • Predictive Analytics – Leveraged for intelligent insights.
  • Secure & Scalable – Ensured zero-trust security, compliance, and cost-efficient cloud operations.

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