Project Name
How Ksolves Streamlined Data Decoding and Parsing By Using NiFi Custom Processors
A prominent telecom organization managing vast volumes of real-time operational data needed to modernize its legacy data processing architecture. Their existing system relied on standalone scripts for decoding complex CDR (Call Detail Record) files and parsing .xlsb (binary Excel) files—methods that were inefficient, hard to maintain, and not scalable. With growing data demands and performance expectations, the client sought a robust, NiFi-based solution to enable scalable, real-time processing and seamless integration with modern analytics workflows.
While working on the project, our team encountered several key challenges, including:-
- Lack of Native NiFi Support: Apache NiFi does not offer built-in processors for decoding proprietary CDR formats or reading binary .xlsb Excel files.
- Legacy Script Integration Issues: The legacy solution relied on standalone scripts that were difficult to integrate into NiFi, performed poorly under high data volumes, and lacked robust logging and monitoring capabilities.
- Scalability limitations: The scripted solution lacked the architectural flexibility and robustness needed to handle increasing data volumes or support real-time, streaming data pipelines, resulting in limited scalability and operational inefficiency.
- Performance constraints: The existing scripts were inefficient and struggled to process large datasets within acceptable timeframes, leading to delays and inconsistent throughput.
To overcome the limitations of the legacy system, a two-tiered solution was designed that includes a Custom NiFi processor (Java + Maven) and a Python-based processor for parsing .xlsb files, integrated into NiFi.
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Implementation:
- Custom CDR Decoder Processor: The custom CDR decoder was developed using Java and Maven. It implements logic to decode proprietary or binary CDR formats and is built as a NiFi processor by extending the AbstractProcessor class. The processor supports configurable input/output formats and logging, and it efficiently handles large files using parallel processing and backpressure support.
- Python-Based XLSB Parser: The Python-based custom processor was developed using NiFi’s scripting capability. It utilizes libraries like struc and custom logic for binary decoding files and pyxlsb with pandas for reading and extracting data from .xlsb files. The logic is encapsulated in a modular script that accepts NiFi FlowFiles, processes and structures the data, and outputs it in CSV format for downstream NiFi Processors.
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Deployment & Workflow Integration:
- Deployment & Routing: Both custom processors were packaged and deployed into the NiFi environment with configurable parameters such as file paths, format types, and processing options. The dataflow was intelligently designed to automatically detect the incoming file type (CDR or .xlsb), route data to the appropriate decoding processor, and process it accordingly.
- Observability & Monitoring: Integrated with NiFi’s Provenance, counters, and monitoring tools to ensure full visibility, traceability, and real-time flow insights.
Our solution brought measurable improvements by addressing core inefficiencies in the legacy system.
- Increased Efficiency: Optimized decoding logic combined with parallel processing significantly accelerated the handling of large CDR and .xlsb files, reducing overall processing time and improving system responsiveness.
- Improved Scalability: The solution is capable of processing millions of records and multiple files in parallel, ensuring consistent performance even under high data loads.
- Enhanced Observability: Full NiFi integration enables real-time monitoring, robust error handling, and complete data traceability through Provenance tracking.
- Maintainability: The modular Java and Python design enables easy updates, simplifies troubleshooting, and allows quick integration of new data formats or business rules.
- Reduced Operational Costs: Automation replaced manual script execution and minimized recovery efforts, significantly lowering operational overhead and improving overall efficiency.
By implementing custom Apache NiFi processors using Java and Python, Ksolves transformed the client’s outdated, script-based system into a robust, scalable, and automated data processing pipeline. The modernized solution empowered the telecom firm to handle complex binary formats at scale, enhance operational visibility, and drastically cut processing time and manual effort. With improved maintainability and seamless integration, the client now benefits from a high-performance, future-ready data infrastructure built for continuous growth and reliability.
Streamline Your Data Workflows With Ksolves Custom NiFi Solutions!
