Building High Availability and Disaster Recovery Strategies for SQL Server with Real-Time Protection for Critical Systems

References

[1] Mishra, S., & Sharma, V. (2021). High availability and disaster recovery in SQL Server: Techniques and implementation. International Journal of Computer Applications, 183(28), 18–25. https://doi.org/10.5120/ijca2021921402

[2] Reddy, M., & Narayana, S. (2020). Real-time data replication techniques for SQL Server in high availability systems. Journal of Emerging Technologies and Innovative Research, 7(10), 1225–1230.

[3] Sharma, R. (2019). Disaster recovery planning for SQL Server in financial institutions. International Journal of Advanced Research in Computer Science, 10(3), 45–52. https://doi.org/10.26483/ijarcs.v10i3.6461

[4] Jain, A., & Patil, A. (2021). Optimizing Always On Availability Groups for business continuity. In IEEE International Conference on Smart Technologies (pp. 228–232). https://doi.org/10.1109/SMARTTECH50466.2021.9385680

[5] Kumar, P. (2022). Critical system protection using SQL Server failover clustering. International Journal of Engineering Research & Technology, 11(1), 57–61.

[6] Pulivarthy, P. (2024). Harnessing serverless computing for agile cloud application development. FMDB Transactions on Sustainable Computing Systems, 2(4), 201–210.

[7] Pulivarthy, P. (2024). Research on Oracle database performance optimization in IT-based university educational management system. FMDB Transactions on Sustainable Computing Systems, 2(2), 84–95.

[8] Pulivarthy, P. (2024). Semiconductor industry innovations: Database management in the era of wafer manufacturing. FMDB Transactions on Sustainable Intelligent Networks, 1(1), 15–26.

[9] Pulivarthy, P. (2024). Optimizing large scale distributed data systems using intelligent load balancing algorithms. AVE Trends in Intelligent Computing Systems, 1(4), 219–230.

[10] Pulivarthy, P. (2022). Performance tuning: AI analyse historical performance data, identify patterns, and predict future resource needs. International Journal of Innovations in Applied Sciences and Engineering, 8, 139–155.

[11] Pulivarthi, P., & Bhatia, A. B. (2025). Designing empathetic interfaces enhancing user experience through emotion. In S. Tikadar, H. Liu, P. Bhattacharya, & S. Bhattacharya (Eds.), Humanizing Technology With Emotional Intelligence (pp. 47–64). IGI Global. https://doi.org/10.4018/979-8-3693-7011-7.ch004

[12] Puvvada, R. K. (2025). Enterprise revenue analytics and reporting in SAP S/4HANA Cloud. European Journal of Science, Innovation and Technology, 5(3), 25–40.

[13] Puvvada, R. K. (2025). Industry-specific applications of SAP S/4HANA Finance: A comprehensive review. International Journal of Information Technology and Management Information Systems, 16(2), 770–782.

[14] Puvvada, R. K. (2025). SAP S/4HANA Cloud: Driving digital transformation across industries. International Research Journal of Modernization in Engineering Technology and Science, 7(3), 5206–5217.

[15] Puvvada, R. K. (2025). The impact of SAP S/4HANA Finance on modern business processes: A comprehensive analysis. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(2), 817–825.

[16] Puvvada, R. K. (2025). SAP S/4HANA Finance on cloud: AI-powered deployment and extensibility. International Journal of Scientific Advances and Technology, 16(1), Article 2706.

[17] Banala, S., Panyaram, S., & Selvakumar, P. (2025). Artificial intelligence in software testing. In P. Chelliah, R. Venkatesh, N. Natraj, & R. Jeyaraj (Eds.), Artificial Intelligence for Cloud-Native Software Engineering (pp. 237–262).

[18] Panyaram, S. (2024). Digital twins & IoT: A new era for predictive maintenance in manufacturing. International Journal of Inventions in Electronics and Electrical Engineering, 10, 1–9.

[19] Singh, A., & Verma, R. (2020). HA and DR implementation using SQL Server in healthcare systems. Asian Journal of Computer Science and Technology, 9(2), 30–35.

[20] Gupta, M., & Chawla, A. (2018). Enterprise data recovery models in Microsoft SQL Server. International Journal of Information Technology and Computer Science, 10(6), 49–56. https://doi.org/10.5815/ijitcs.2018.06.07

[21] Rani, P., & Thomas, S. (2019). Ensuring zero downtime with SQL Server availability groups. International Journal of Research in Engineering, IT and Social Sciences, 9(5), 95–101.

[22] Kapoor, D., & Saxena, N. (2021). Evaluation of backup strategies in SQL Server 2019. International Journal of Computer Engineering and Applications, 15(4), 82–88.

[23] Deshmukh, S., & Yadav, A. (2023). Enhancing SQL Server data resilience through synchronous mirroring. In International Conference on Recent Innovations in Engineering and Technology (pp. 345–349).

[24] Panyaram, S. (2024). Enhancing performance and sustainability of electric vehicle technology with advanced energy management. FMDB Transactions on Sustainable Energy Sequence, 2(2), 110–119.

[25] Panyaram, S. (2024). Optimization strategies for efficient charging station deployment in urban and rural networks. FMDB Transactions on Sustainable Environmental Sciences, 1(2), 69–80.

[26] Panyaram, S. (2024). Integrating artificial intelligence with big data for real-time insights and decision-making in complex systems. FMDB Transactions on Sustainable Intelligent Networks, 1(2), 85–95.

[27] Panyaram, S. (2024). Utilizing quantum computing to enhance artificial intelligence in healthcare for predictive analytics and personalized medicine. FMDB Transactions on Sustainable Computing Systems, 2(1), 22–31.

[28] Panyaram, S., & Hullurappa, M. (2025). Data-driven approaches to equitable green innovation bridging sustainability and inclusivity. In P. William & S. Kulkarni (Eds.), Advancing Social Equity Through Accessible Green Innovation (pp. 139–152).

[29] Hullurappa, M., & Panyaram, S. (2025). Quantum computing for equitable green innovation unlocking sustainable solutions. In P. William & S. Kulkarni (Eds.), Advancing Social Equity Through Accessible Green Innovation (pp. 387–402).

[30] Panyaram, S., & Kotte, K. R. (2025). Leveraging AI and data analytics for sustainable robotic process automation (RPA) in media: Driving innovation in green field business process. In S. Kulkarni, M. Valeri, & P. William (Eds.), Driving Business Success Through Eco-Friendly Strategies (pp. 249–262).

[31] Kotte, K. R., & Panyaram, S. (2025). Supply chain 4.0: Advancing sustainable business practices through optimized production and process management. In S. Kulkarni, M. Valeri, & P. William (Eds.), Driving Business Success Through Eco-Friendly Strategies (pp. 303–320).

[32] Panyaram, S. (2024). Automation and robotics: Key trends in smart warehouse ecosystems. International Numeric Journal of Machine Learning and Robots, 8(8), 1–13.

[33] Panyaram, S. (2023). Digital transformation of EV battery cell manufacturing leveraging AI for supply chain and logistics optimization. International Journal of Emerging Technology, 18(1), 78–87.

[34] Panyaram, S. (2023). Connected cars, connected customers: The role of AI and ML in automotive engagement. International Transactions in Artificial Intelligence, 7(7), 1–15.

[35] [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]

[36] ñâñÓñâñǼ±£˜Œ}kYE4E4 h7e?5�6�CJOJPJQJaJ&h�V$h�V$5�6�CJOJPJQJaJ#h˜gh^~ 5�CJOJPJQJaJ"h˜gh^~ 5�6�CJOJQJaJh^~ h˜gCJOJQJaJh^~ CJOJQJaJhÉZh�V$OJQJh�V$h�V$6�OJQJ]�h�V$h�V$OJQJh7e?h�V$OJQJh�V$CJ(OJQJaJ(h�V$hçÔCJ(OJQJaJ(h�V$h7e?CJ(OJQJaJ(h�V$h�V$CJ(OJQJaJ(z{”ØÙÚXY‰Š—˜,]!^!p)q)õõõõããÙÙÙÙ½¨ÙÙÙÙÙÙÙ$„ñÿ„üdì¤:]„ñÿ^„üa$gd�V$$ &F„ñÿ„7„Éýdì¤:]„ñÿ^„7`„Éýa$gd7e? $¤a$gd�V$„ñÿ„ dð¤]„ñÿ^„ gd^~ $¤a$gd7e?º [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]

@article{1703328,
      author = {Padma Rama Divya Achanta},
      title = {Building High Availability and Disaster Recovery Strategies for SQL Server with Real-Time Protection for Critical Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {10},
      pages = {380-387},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703328.pdf},
      abstract = {In today's data-intensive business scenario, the integrity and availability of mission-critical systems are of utmost importance. Microsoft SQL Server is a heavily used product by organizations to store and manage critical business information. Accordingly, maintaining uninterrupted data access through strong High Availability (HA) and Disaster Recovery (DR) solutions is crucial to reduce downtime, avoid data loss, and ensure business continuity. This paper discusses the architecture, implementation, and best practices of constructing effective HA and DR plans particularly for SQL Server environments with emphasis on real-time protection of mission-critical applications. The paper discusses the comparative study of native SQL Server features like Always On Availability Groups, Failover Cluster Instances (FCIs), Log Shipping, Database Mirroring, and Backup/Restore approaches. It also addresses how third-party offerings and real-time data replication technologies aid in the optimization of recovery point objectives (RPO) and recovery time objectives (RTO). It looks into cloud DR solutions and hybrid models integration to discover how they can complement on-premises infrastructures with economical, scalable alternatives. In addition, the research discusses real-time HA/DR challenges including network latency, storage replication consistency, application failover compatibility, and the administrative burden in configuring and monitoring HA/DR solutions. Automation of failover processes, round-the-clock monitoring, and performing periodic DR drills to be well-prepared is also of much importance. Using case studies and actual implementations, the paper illustrates how businesses from various industries have been able to achieve near-zero downtime and fractional data loss by implementing sophisticated HA and DR mechanisms. Business size, budget limitations, compliance demands, and system criticality are used as guidelines to make recommendations for selecting the appropriate HA/DR strategy. Finally, this paper provides value to the IT practitioner and system architect by providing a well-defined method for designing, implementing, and managing high availability and disaster recovery plans for SQL Server. Highlighting real-time data protection is to ensure that mission-critical business systems continue to function even if there is hardware failure, cyber-attack, or natural disasters.},
      keywords = {SQL Server, High Availability (HA), Disaster Recovery (DR), Real-Time Protection, Always On Availability Groups, Failover Cluster Instances, Log Shipping, Database Mirroring, RPO, RTO, Data Replication, Business Continuity, Cloud Disaster Recovery, Hybrid Architecture, System Resilience.},
      month = {April},
  }