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:
- job.py — orchestrates the data loading workflow
- logger.py — handles all logging, metrics capture, and anomaly detection
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:
- Lack of structured logging: Troubleshooting failures and performance bottlenecks can be slow and error-prone.
- No built-in anomaly detection: Data quality issues or unexpected data shifts remain undetected until downstream effects emerge.
- Scattered metrics: Success, error, and quality metrics are rarely captured in a centralized, queryable form.
This framework addresses these gaps by:
- Providing a custom logging framework to capture detailed metrics about every job run, including row counts, sums, errors, and timestamps.
- Embedding anomaly detection logic to compare current run metrics against historical data, flagging deviations beyond configurable thresholds.
- Standardizing data loading workflows to enforce consistency and reuse across multiple ETL jobs.
- Enabling proactive monitoring and rapid troubleshooting by centralizing logs and anomaly records in Hive tables.
Key Benefits of a Logging-Centric ETL Framework
- Enhanced Data Trust: Early anomaly detection prevents corrupted or incomplete data from propagating.
- Centralized Observability: A dedicated logging component aggregates metrics and errors for easy monitoring and auditing.
- Scalable Architecture: Decoupling data loading and logging enables multiple teams to adopt a consistent ETL approach without reinventing logging.
- Faster Troubleshooting: Detailed logs combined with anomaly flags help data engineers quickly pinpoint root causes.
Prerequisites
Ensure your environment is ready with:
- Cloudera Data Platform (CDP) supporting Hive and Spark
- Python 3.6+ installed
- Access to the Hive Metastore URI
- Spark session configured with Hive support
- Permissions to read/write Hive warehouse directories
- Familiarity with Python, Spark and ETL workflow development
Framework Components
1. job.py — The Data Loading Orchestrator
- Extracts data from Hive tables, APIs, or file systems.
- Applies necessary transformations to prepare data for downstream consumption.
- Loads the cleaned and transformed data into target Hive tables.
- Calculates key metrics dynamically (row counts, sum of numeric columns, etc.).
- Interfaces with logger.py to record job status, metrics, and anomalies.
2. logger.py — The Custom Logging and Anomaly Detection Engine
- Initializes a Spark session optimized for logging and metric computations.
- Logs success metrics such as row counts, column sums, and timestamps to dedicated Hive logging tables.
- Captures and logs error details for failed runs, aiding in diagnostics.
- Performs anomaly detection by comparing current metrics to historical baselines using configurable rules (e.g., percent changes beyond thresholds).
- Maintains traceability by associating all logs with job metadata, process names, and timestamps.
Workflow Execution:
- Initialization: Configure Hive metastore, warehouse paths, and instantiate the Logger.
- Spark Session Setup: Create a Spark session with Hive support and dynamic partitioning enabled.
- Data Extraction & Transformation: Run queries or API calls to collect and transform data.
- Data Loading: Insert the processed data into target Hive tables.
- Metrics Calculation & Logging: Compute metrics and invoke logger to log these.
- Anomaly Detection: Analyze metrics vs. historical data to flag anomalies, then log these flags.
- Error Handling: Catch exceptions, log error details, and raise alerts if necessary.
- Cleanup: Close Spark sessions cleanly to free resources.
Anomaly Detection Process
Anomaly detection is a cornerstone of this logging framework, enabling proactive data quality management:
- Current Run Metrics: Extracted after each job completes.
- Historical Data: Pulled from the logging tables, providing baselines for comparison.
- Comparison Logic: Numeric metrics are evaluated for significant deviations (e.g., a 50% drop or spike). Categorical or string-based data is checked for unexpected changes.
- Flagging & Logging: Any metric outside expected bounds is flagged as an anomaly, recorded for alerting and downstream dashboards.
- Continuous Updates: Logs are updated with every run, maintaining an evolving picture of data pipeline health.
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.