Modernizing the Telecom Invoice Generation Process
Transforming telecom invoice generation with a scalable Spark-based mediation layer that processes 30 million records in minutes instead of hours.
Client
A US-based global telecom solutions expert specializing in omnichannel customer engagement.
Problem Statement
The client faced extreme delays in invoice generation due to massive data volumes and fragmented source systems across their mediation layer.
Industry
Quick Summary
- Built a robust ETL pipeline using Apache Spark and Amazon EMR to automate the extraction, validation, and enrichment of telecom usage data.
- Developed a high-performance mediation layer that integrates with SureTax to generate accurate, tax-included invoices for global customers.
- The client achieved a 90% improvement in process efficiency, slashing daily billing cycles from 8 hours to just 14 minutes.
Client Profile
US-based global telecom solutions expert providing customer experience and call center technology to mid-sized and large businesses. The company sells cloud-based and on-premises software, contact center applications, and omnichannel customer engagement solutions.
Challenges: Processing Millions of Records
- Legacy Bottlenecks: The existing billing process took over 8 hours daily, delaying financial reporting and customer invoicing.
- Data Quality Issues: High volumes of raw data from multiple sources required rigorous auditing and validation to ensure billing accuracy.
- Complex Tax Integration: The need to calculate precise taxation across various jurisdictions required seamless integration with SureTax.
- Scale Requirements: The system needed to handle over 30 million records from 30,000+ files daily without performance degradation.
QBurst Solution: Scalable Spark-Based Mediation
We delivered a solution that leverages the distributed computing power of Apache Spark running on Amazon EMR to create a unified information base for the client’s billing system.
- Dual-Stage Aggregation:
- Daily Aggregation: Python scripts ingest CSV data into Amazon S3, where Spark applications perform validation, derivation, and enrichment before storing data in optimized Parquet format.
- Monthly Aggregation: A secondary Spark process executes complex rating and taxation logic, saving finalized billed data into the production database.
- Data Audit Model: Implemented a custom audit layer within the ETL process to ensure data integrity and fix existing system errors during transit.
- Cloud-Native Scalability: Utilized Amazon EMR to dynamically scale compute resources, allowing the system to handle sudden spikes in call volume and data traffic.
Technical Highlights
- High-Performance Storage: Transitioned to Parquet format to enable faster data retrieval and significantly reduce storage costs.
- Automated Rating & Taxation: Integrated SureTax via APIs to automate tax-inclusive invoice generation.
- Standardized Formatting: Provided a unified data format across the mediation layer, improving compatibility with downstream financial systems.
- Fault-Tolerant Processing: Fixed long-standing issues in the legacy data flow through rigorous ETL testing and error-handling logic.
Impact
- 97% Faster Daily Billing: Reduced processing time from 8 hours to 14 minutes for 30 million records.
- Ultra-Fast Monthly Runs: Final monthly billing cycles now complete in just 2.2 minutes.
- 90% Process Efficiency: Dramatically improved the speed and accuracy of the entire invoice generation workflow.
- Improved Profit Margins: Achieved through significant cost savings in infrastructure, time, and manual effort.
- 25% Faster Data Retrieval: Standardized billing formats enabled rapid analysis and reporting for stakeholders.
Client Profile
Challenges
QBurst Solution
Technical Highlights
Impact