Designing a Scalable Data Loading and Custom Logging Framework for ETL Jobs using Hive and PySpark
Introduction Efficient ETL (Extract, Transform, Load) pipelines are the backbone of modern data processing architectures. However, building reliable pipelines requires more than just moving data — it demands robust logging, monitoring, and anomaly detection to quickly identify and resolve issues before they impact business decisions. To meet this need, we developed a modular data loading and custom logging framework tailored for the Cloudera Data Platform (CDP). The framework’s main focus is on comprehensive logging and intelligent anomaly detection that provide deep observability into ETL processes. At the heart of this framework are two core components: In this blog, we’ll walk you through the design and execution of this framework, showing how it boosts reliability and scalability in data pipelines. Why Build a Custom Data Loading and Logging Framework? Traditional ad-hoc ETL scripts often suffer from: This framework addresses these gaps by: Key Benefits of a Logging-Centric ETL Framework Prerequisites Ensure your environment is ready with: Framework Components 1. job.py — The Data Loading Orchestrator 2. logger.py — The Custom Logging and Anomaly Detection Engine Workflow Execution: Anomaly Detection Process Anomaly detection is a cornerstone of this logging framework, enabling proactive data quality management: Conclusion By integrating custom logging and anomaly detection directly into your ETL jobs, this framework significantly enhances pipeline observability and resilience. It enables data teams to proactively monitor data quality, quickly identify issues, and scale ETL operations with confidence. We encourage data engineering teams to adopt similar logging-centric ETL frameworks to future-proof their data infrastructure and drive better, faster decision-making Ready to Streamline Your ETL Workflows? At Alephys, we work closely with data teams to design and implement modular, logging-first ETL frameworks that elevate pipeline reliability, traceability, and scale. Built to establish trust from source to sink, this framework brings structure and control to even the most complex data environments. With built-in logging and anomaly detection at the job level, teams gain deeper visibility into their data flows, making it easier to catch issues early, enforce data quality standards, and respond quickly to anomalies. The result is a more resilient and transparent ETL process that supports confident decision-making and continuous scaling. By embedding these capabilities directly into your ETL architecture, we help you unlock operational efficiency and lay the groundwork for a future-ready data platform. Authors: Jayakrishna Vutukuri, Senior Systems Architect at Alephys(Linkedin)Saketh Gadde, Data Consultant at Alephys(Linkedin) We design scalable data pipelines and automation frameworks that power efficient data-driven decision-making. Connect with us on Linkedin to discuss building reliable ETL platforms and operationalizing data quality in Spark and Hive environments.