International Journal on Science and Technology

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Designing Reliable Data Platforms: Addressing Critical Challenges in Data Backup and Recovery

Author(s) Varun Garg
Country United States
Abstract Data reliability is great concern for modern digital infrastructure, as companies are becoming increasingly dependent on dispersed data platforms to manage their ever-growing databases. These systems support information access through data storage, processing, and analysis, which enables rapid decision-making and company growth. However, significant concerns arise due to the potential for data loss from hardware, software, or network system failures. These risks can be reduced via sound backup and recovery plans.

This work investigates backup and recovery mechanisms for the primary challenges in building consistent data platforms. It offers realistic ways for creating robust systems and looks at solutions for scalability, latency, and data integrity issues. Examining tools including Databricks, Kafka, and cloud-native solutions reveals how well they may provide automated and scalable recovery systems. Indicated future directions include novel concepts such real-time recovery and artificial intelligence-driven anomaly identification. This framework is offered in a study for businesses trying to reconcile technological improvements with pragmatic uses in order to lower the risks related with data loss and, hence, improve the dependability of the data platform.
Keywords Data Reliability, Distributed Data Platforms, Data Backup, Data Recovery, Fault Tolerance, Scalability, Latency Optimization, Event Replay, Schema Evolution, Cloud-Native Tools, Databricks, Apache Kafka, AWS Backup, Workflow Orchestration, Real-Time Processing, Machine Learning, Artificial Intelligence, Anomaly Detection, Data Integrity, Redundancy, Checkpointing, Recovery Automation, Hybrid Cloud Architectures, Predictive Maintenance, Operational Continuity, Resilient Systems, Big Data
Field Computer > Automation / Robotics
Published In Volume 14, Issue 3, July-September 2023
Published On 2023-09-05
DOI https://doi.org/10.5281/zenodo.14513980
Short DOI https://doi.org/g8wcd9
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