International Journal For Multidisciplinary Research

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Overcoming Data Loss Challenges: Best Practices for Backfill and Reprocessing in Distributed Data Systems

Author(s) Varun Garg
Country United States
Abstract Data loss is one of the key issues in distributed systems that could cause operational interruptions, wrong analysis, and financial losses. Modern data-driven operations are built on distributed systems, which should be robust given the expanding component interdependence and increasing complexity of data
pipelines. Backfill and reprocessing techniques, studied in this work as strategies of minimizing data loss, are what maintain data accuracy and operational continuity. Together with the significant challenges provided by distributed architectures-scalability, latency, and fault tolerance-the paper offers pragmatic best practices for implementation. Tools such as Databricks, Kafka, and S3-which let trustworthy, scalable, automated data recovery processes-enable foundational pieces for these systems. With the aid of real-life scenarios and new technology, this paper tries to provide a complete framework for companies willing to enhance their distributed systems against data loss risks, thus making them resilient and robust in front of increasing data demands.
Keywords Data Loss, Backfill, Reprocessing, Distributed Systems, Fault Tolerance, Data Integrity, Data Recovery, Event Replay, Schema Evolution, Scalability, Real-Time Processing, Data Replication, Workflow Orchestration, Anomaly Detection, Cloud Computing, Edge Computing, Redundancy, Data Validation, Checkpointing, Idempotency, Automation, Machine Learning, Data Pipeline.
Field Engineering
Published In Volume 6, Issue 5, September-October 2024
Published On 2024-09-10
Cite This Overcoming Data Loss Challenges: Best Practices for Backfill and Reprocessing in Distributed Data Systems - Varun Garg - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.21547
DOI https://doi.org/10.36948/ijfmr.2024.v06i05.21547
Short DOI https://doi.org/g82hrp
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