Project Name
Restored SLA-Consistent Telecom ETL Processing Using Informatica
A telecommunications operator managing very high daily data volumes, including call detail records and usage data, depended on a batch ETL process to move data from source systems into an enterprise data warehouse for billing, churn analysis, and operational reporting. As data volumes grew, the existing process began straining against service-level agreements – long-running jobs were fragile, source system load during extraction was a growing risk, and any pipeline failure had no clean restart path. Applying its AI-First approach, Ksolves built an ETL framework around low-impact extraction and tuned, restartable batch processing – restoring SLA-consistent delivery and giving billing, churn, and reporting teams a dependable daily data foundation.
- Very High Daily Data Volumes: The pipeline had to process large volumes of CDRs and usage data every day; any slowdown risked delays in downstream billing, churn analysis, and operational reporting.
- SLA Risk on Long-Running ETL Jobs: Complex business transformations and multi-step workflow dependencies made it difficult to guarantee batch jobs would complete consistently within their service-level windows.
- Source System Impact During Extraction: Pulling data at this volume risked placing direct load on production source systems with no low-impact extraction method in place.
- No Structured Restart Mechanism for Batch Failures: When a long-running job failed partway through, there was no robust mechanism to resume from the point of failure - risking reprocessing and further SLA slippage.
- Complex Interdependent Business Transformations: Multiple transformation steps with dependencies between them made the pipeline harder to tune, troubleshoot, and keep performant as volumes grew.
- Downstream Reporting Depended on Predictable Delivery: Billing, churn analysis, and operational reporting teams needed data to land reliably - pipeline performance had not kept pace with data growth.
Ksolves built the ETL framework around low-impact extraction and tuned, restartable batch processing so the pipeline could keep pace with data growth without adding risk to source systems or reporting timelines. The governing principle was performance discipline at every layer - from how data left the source to how each session executed and recovered from failure.
- Low-Impact Extraction With Informatica PowerExchange: Used PowerExchange to pull data from source systems with a minimal footprint - addressing the risk of extraction load on live production telecom systems during high-volume daily CDR pulls.
- Batch ETL Built on Informatica PowerCenter: Core transformation workflows implemented in PowerCenter - a governed, auditable framework for the business logic driving billing and reporting outputs across all downstream teams.
- Partitioning, Parallelism and Pushdown Optimisation: Partitioning and parallel processing applied alongside pushdown optimisation - moving transformation work closer to the database engine to cut processing time on high-volume loads.
- Tuned Sessions, Commit Intervals and Restartability: Session settings and commit intervals reconfigured, restartability mechanisms built in - failed batch runs resume without reprocessing completed work, protecting SLA windows.
- Curated Delivery Into Oracle Exadata: Transformed, analytics-ready data loaded into Oracle Exadata - giving billing, churn analysis, and operational reporting teams a single reliable source on a predictable daily schedule.
Technology Stack
| Category | Technology |
|---|---|
| Integration | Informatica PowerExchange |
| Processing | Informatica PowerCenter |
| Database | Oracle Exadata |
| Methodology | Session & Commit Tuning + Restartability |
- SLA Adherence Restored for Batch ETL Processing: Batch jobs now consistently complete within SLA windows, providing billing and reporting teams with dependable daily delivery. Metric not quantified in source - confirm hard number before publication.
- Source System Load Reduced During Extraction: PowerExchange-based extraction removes direct load from production telecom source systems, protecting source system performance during high-volume daily CDR pulls.
- Batch Resilience Improved With Structured Restartability: Tuned restartability mechanisms let failed jobs resume without reprocessing completed work - long-running batch failures no longer cascade into SLA slippage.
- Reliable Data Delivery for Billing, Churn and Reporting: Curated data lands in Oracle Exadata on a predictable schedule - downstream billing, churn analysis, and operational reporting teams receive consistent data delivery every day.
“The billing and reporting delays we used to plan around simply aren’t a factor anymore. Our ETL jobs run predictably, and that reliability has changed how confidently we report.”
– Head of Data or IT Operations, Telecommunications.
A telecommunications operator whose ETL pipeline was straining under very high daily data volumes was transformed through Ksolves Big Data services. With no low-impact extraction method, no structured restart mechanism, and rising risk of missed SLA windows, the team had no reliable path to consistent billing and reporting delivery. A tuned, partitioned, and restartable Informatica-based ETL framework now delivers analytics-ready data into Oracle Exadata on a predictable schedule. SLA adherence is restored. Source system load is reduced. Batch failures are handled with structured restartability. Billing, churn, and reporting teams now operate on a reliable daily data cadence. The ETL foundation scales to absorb further data growth without redesigning the pipeline.
Is Your Etl Pipeline Keeping Pace with Your Data Growth or Putting Your Slas at Risk?
