(PITR) [⇒ Lamdera Data Storage]
Management of data backup and restore in case of emergency is a crucial process in every organization. This paper discusses an effective database recovery technique called Point In Time Recovery (PITR) in PostgreSQL database management system.
Despite emerging big data technology, relational database management system (RDBMS) is still performing the key role for storing and processing of data in most of the organizations. Almost all kinds of financial organizations like banks and mobile financial service (MFS) organizations use RDBMS as their database tool for storing their users information and all kinds of transactional information related to that organization. Nowadays those type of organizations focus on customer acquisition strategy and thus data is growing rapidly. In spite of proper system management system crash is not very uncommon while processing large volumes of data. It results loss of data and a huge financial loss for the organization. To tackle such tragedy for the business a proper data recovery system is required for every organization. Generally organizations use backup using pg_dump command and restore using pg_restore but this traditional recovery system cannot restore the data which is created or altered after the backup taken. Also this process is time inefficient because this process reconstruct the database to the state of the last dump file.
Thus our research paper implements a potent process of data recovery technique in postgreSQL that can recover all data which is created or altered after the backup taken. Again this process is time efficient because it works restoring using Write-Ahead-Log (WAL) file from the base backup.
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HOSSAIN, Md. Anower, HASAN, Md. Imrul, ISLAM, Md Rashedul and AHMED, Nadeem, 2021. A Novel Recovery Process in Timelagged Server using Point in Time Recovery (PITR). In: 2021 24th International Conference on Computer and Information Technology (ICCIT). December 2021. p. 1–5. doi 
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BEBEE, Brad, CHOI, Daniel, GUPTA, Ankit, GUTMANS, Andi, KHANDELWAL, Ankesh, KIRAN, Yigit, MALLIDI, Sainath, MCGAUGHY, Bruce, PERSONICK, Mike, RAJAN, Karthik, RONDELLI, Simone, RYAZANOV, Alexander, SCHMIDT, Michael, SENGUPTA, Kunal, THOMPSON, Bryan, VAIDYA, Divij and WANG, Shawn, [no date]. Amazon Neptune: Graph Data Management in the Cloud. . We present Amazon Neptune, a fast, reliable, and fully managed graph database service. Supporting both the Apache Tinkerpop Gremlin stack as well as the RDF 1.1 / SPARQL 1.1 W3C standards, Amazon Neptune efficiently stores and navigates highly connected data, allowing developers to create interactive graph applications that can query billions of relationships with millisecond latency.
As a purpose-built cloud service, Neptune is seamlessly embedded into the Amazon Web Service (AWS) ecosystem and comes with a broad set of enterprise features including SDKs for deployment and configuration, high availability and scale-up using replication, automatic backup and restore functionality, point in time recovery, monitoring, encryption-at-rest, security using VPCs and integrated access management, as well as audit logs. In our presentation, we will sketch customer use cases, put them into context with Neptune architecture and features, and discuss options for engagement with the Semantic Web community.