View a markdown version of this page

TELCOSUS02-BP01 Implement efficient data lifecycle management for telco networks - Telco Lens
Implementation guidanceResources

TELCOSUS02-BP01 Implement efficient data lifecycle management for telco networks

telco networks generate diverse data types with varying retention requirements. Call detail records (CDRs) requiring long-term retention for regulatory adherence, network performance metrics needed for real-time analysis, and IoT sensor data with high velocity but short relevance periods. Design your data architecture to automatically optimize storage based on access patterns while maintaining adherence with telecommunications regulations.

Desired outcome: Reduce storage costs and energy consumption through intelligent data tiering, compression, and lifecycle management while maintaining adherence with telecommunications regulatory requirements for data retention and accessibility.

Benefits of establishing this practice:

  • Achieve reduction in storage costs through intelligent tiering and compression.

  • Automated retention policies meeting telecommunications data regulations.

  • Faster data processing through optimized storage patterns.

  • Lower energy consumption from reduced storage infrastructure.

  • Automated data management reducing manual intervention.

Level of risk exposed if this best practice is not established: Low

Implementation guidance

Start by categorizing your telco data based on regulatory requirements, access frequency, and business value. For CDRs and lawful intercept data, implement immutable storage with defined retention periods. For network telemetry and performance data, use time-series optimized storage with aggressive compression. For customer analytics and billing data, implement tiered storage based on access patterns.

Use Amazon S3 Intelligent-Tiering to automatically move data between frequent, infrequent, and archive access tiers without performance impact or operational overhead. Configure lifecycle policies specific to telco data types (for example, moving CDRs to Glacier Deep Archive after 90 days (about three months) while keeping metadata in Amazon S3 Standard for quick retrieval).

For real-time data processing, implement stream processing architectures using Amazon Kinesis Data Streams for ingesting network telemetry, with Managed Service for Apache Flink for real-time anomaly detection and Firehose for delivering processed data to Amazon S3 in compressed Parquet format.

Implementation steps

  1. Create S3 buckets with specific naming conventions for different telco data types, for example: cdr-data, network-telemetry, customer-analytics, or iot-sensors.

  2. Enable S3 Intelligent-Tiering on S3 buckets and configure archive policies: CDRs to Deep Archive after 90 days (about three months) and network logs to Glacier Instant after 30 days (about a month).

  3. Implement AWS Glue ETL jobs to compress and convert raw telco data to columnar formats (like Parquet or ORC).

  4. Deploy Amazon Kinesis Data Streams with shard auto scaling for ingesting real-time network data, sized based on peak traffic.

  5. Configure Managed Service for Apache Flink applications with SQL queries to detect network anomalies and aggregate metrics in real-time, reducing downstream processing needs.

  6. Set up AWS Lambda functions triggered by S3 events to validate data adherence, apply compression, and update metadata in Amazon DynamoDB.

  7. Implement Amazon Athena with partition projection for querying historical telco data directly from S3 without maintaining separate data warehouses.

  8. Create CloudWatch dashboards to monitor storage metrics, data transfer costs, and lifecycle transition effectiveness.

Resources

Key AWS services: