Network performance metrics summarized by cell tower
Posted: Tue May 20, 2025 9:42 am
Purpose: Data from the Raw Zone is cleaned, standardized, de-duplicated, and enriched. This makes it more usable for downstream analytics.
Transformations:
Pseudonymization/Anonymization: Crucial for privacy. Sensitive identifiers like raw phone numbers should be pseudonymized or hashed at this stage, while maintaining analytical utility. This is a key privacy control.
Tools:
Apache Spark (Batch & Streaming): Powerful for large-scale data processing and transformations. Can handle both batch ETL and real-time stream processing.
Apache Flink: Excellent for stateful stream processing, particularly netherlands phone number list useful for continuous, complex transformations on recent mobile data.
Databricks Delta Lake / Apache Iceberg / Apache Hudi: These "data lakehouse" technologies provide ACID transactions, schema enforcement, and other data warehousing features directly on top of object storage, improving reliability and performance for the Silver Layer.
Considerations for Bangladesh: Spark and Flink are open-source and highly flexible, allowing for custom logic tailored to specific Bangladeshi mobile data types or regulatory requirements.
Curated/Consumption Zone (Gold Layer):
Purpose: Highly refined, aggregated, and optimized data ready for specific business use cases (BI dashboards, machine learning models, reporting). This layer is typically structured and often denormalized for faster querying.
Transformations: Aggregations (e.g., daily call volumes per district, weekly app usage patterns), feature engineering for ML models, final joins.
Format: Optimized columnar formats like Parquet or ORC for analytical performance.
Transformations:
Pseudonymization/Anonymization: Crucial for privacy. Sensitive identifiers like raw phone numbers should be pseudonymized or hashed at this stage, while maintaining analytical utility. This is a key privacy control.
Tools:
Apache Spark (Batch & Streaming): Powerful for large-scale data processing and transformations. Can handle both batch ETL and real-time stream processing.
Apache Flink: Excellent for stateful stream processing, particularly netherlands phone number list useful for continuous, complex transformations on recent mobile data.
Databricks Delta Lake / Apache Iceberg / Apache Hudi: These "data lakehouse" technologies provide ACID transactions, schema enforcement, and other data warehousing features directly on top of object storage, improving reliability and performance for the Silver Layer.
Considerations for Bangladesh: Spark and Flink are open-source and highly flexible, allowing for custom logic tailored to specific Bangladeshi mobile data types or regulatory requirements.
Curated/Consumption Zone (Gold Layer):
Purpose: Highly refined, aggregated, and optimized data ready for specific business use cases (BI dashboards, machine learning models, reporting). This layer is typically structured and often denormalized for faster querying.
Transformations: Aggregations (e.g., daily call volumes per district, weekly app usage patterns), feature engineering for ML models, final joins.
Format: Optimized columnar formats like Parquet or ORC for analytical performance.