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Data Quality Management Using Talend: A Framework for Reliable Data Integration

Author(s) Srinivasa Rao Karanam
Country United States
Abstract In the contemporary digital era, data management endeavors remain a pivotal determinant of organizational success. Many enterprise-level applications and advanced data analytics modules rely upon consistent, accurate, and robust data pipelines for critical decision-making processes. This paper attempts to address these challenges by presenting a specialized framework, leveraging the Talend ecosystem, that fuses data integration with comprehensive data quality measures. The approach covers crucial steps such as data ingestion, profiling, cleansing, standardization, and deduplication, culminating in a governance-based methodology for continuous improvement. By adopting these techniques, organizations can harness consistent data that yields superior insights and fosters improved strategic outcomes.
In an effort to underscore its value, the proposed framework draws from a thorough review of prevailing literature, extensive empirical experimentation, and conceptual mappings to Talend’s suite of tools. The method aims to integrate data quality transformations from the earliest point of data capture to the final stage of consumption, thus negating the inherent inefficiencies and inconsistencies that typically plague large-scale data integration projects. The paper also reveals significant lessons gleaned from real-world scenarios, analyzing performance trade-offs, governance complexities, and the potential to adapt the approach for emerging paradigms such as data mesh architectures. Ultimately, the synergy between Talend’s robust functionalities and well-defined data quality protocols can yield heightened trust, better compliance, and more timely decision-making in modern data-driven ecosystems.
Keywords -
Field Engineering
Published In Volume 4, Issue 10, October 2023
Published On 2023-10-03
Cite This Data Quality Management Using Talend: A Framework for Reliable Data Integration - Srinivasa Rao Karanam - IJLRP Volume 4, Issue 10, October 2023. DOI 10.5281/zenodo.15050996
DOI https://doi.org/10.5281/zenodo.15050996
Short DOI https://doi.org/g88zxc
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