Holistic disaster recovery approach for big data NoSQL workloads

Aharon, Abadi; Ashraf, Haib; Roie, Melamed; Alaa, Nassar; Shribman, Aidan; Hisham, Yasin

doi:10.1109/bigdata.2016.7840833

Cited by 3 publications

(6 citation statements)

References 1 publication

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“…BARNS save about 66% of storage spaces by reducing two replica copies for MongoDB (master-slaves) and Cassandra (master-less). But according to [10], they have reported that the NoSQL databases have developed as respond to continuous increasing in datasets volumes. However, these NoSQL databases solutions do not support out of the box the necessary backup and recovery features needed by many big data modes.…”

Section: State Of Artmentioning

confidence: 99%

“…Even though the existence of replicas might mistakenly represent the illusion that data will not be lost but that's not the case as they don't account for out of the box capabilities for restoring, recovering and necessary backup techniques to account for different catastrophic events [5]. Recovery techniques used in most NoSQL database are considered modest mechanisms and are basically only based on logs whether local or global logs and snapshots that come in use when server crashes for instance [10].…”

Section: Opportunities and Challengesmentioning

confidence: 99%

“…The existing techniques are acceptable but they don't account for the effect of faulty operations, whereas if an executed operation lead to tainting the database the restoring of old version from the snapshots will help get back the data but will also result in losing operations that have been performed after it was taken leading the data to be in an incorrect state and not reflecting the actual correct version [10].…”

Section: Opportunities and Challengesmentioning

confidence: 99%

“…Another huge challenge addressed in NOSQL systems is recovery from disaster failure [10]. Replication and different backup capabilities can only support recovery in local storage only against crashes not human errors or malicious security issues [9] in this section, the study provides the Holistic backup/restore approach to handle the disaster failure inside the NOSQL systems.…”

Section: Recovery and Concurrency Techniquesmentioning

confidence: 99%

“…The synergy based systems first foundation is having quicker join performance to increase the disk operation based on materialized views. Figure 2: "backup versioning: the new baseline gather the baseline delta into a tt less granular time slice" source [10] Generally, Materialized views (MV) is an object of the database where the query results are held and basically are locally replicated data that is remotely local located. The addition of MV to the NoSQL capabilities is purely aimed to enhance its performance but alone it will lead us to facing the consistency issues that lead the authors to account for concurrency control in their model as multi-versioning or locking techniques to help resolve this issue.…”

Section: Recovery and Concurrency Techniquesmentioning

confidence: 99%

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Recovery and Concurrency Challenging in Big Data and NoSQL Database Systems

Abed¹

2020

IJANA

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Big data is becoming a very important concept nowadays as it can handle data in different formats and structures, velocity, and huge volume. NOSQL databases are used for handling the data with these characteristics as traditional database can't be used in managing this type of data. NoSQL database design is based on horizontal scalability with the concept of BASE which supports eventual consistence and data is considered in a soft state and basically available. Although NoSQL has a lot to offer when used in big data it is still not mature enough and faces some challenges including low join performance, concurrency control and recovery. Not only this but also it is very challenging for organizations to know which NoSQL data model to use and how does it fit with its organizational needs. This paper mainly displays the different NOSQL data models and the opportunities and challenges alongside with some techniques for handling these challenges.

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Section: State Of Artmentioning

confidence: 99%

Section: Opportunities and Challengesmentioning

confidence: 99%

Section: Opportunities and Challengesmentioning

confidence: 99%

Section: Recovery and Concurrency Techniquesmentioning

confidence: 99%

Section: Recovery and Concurrency Techniquesmentioning

confidence: 99%

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Recovery and Concurrency Challenging in Big Data and NoSQL Database Systems

Abed¹

2020

IJANA

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The Time Machine in Columnar NoSQL Databases: The Case of Apache HBase

Tsai¹,

Chang

Hsiao

et al. 2022

Future Internet

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Not Only SQL (NoSQL) is a critical technology that is scalable and provides flexible schemas, thereby complementing existing relational database technologies. Although NoSQL is flourishing, present solutions lack the features required by enterprises for critical missions. In this paper, we explore solutions to the data recovery issue in NoSQL. Data recovery for any database table entails restoring the table to a prior state or replaying (insert/update) operations over the table given a time period in the past. Recovery of NoSQL database tables enables applications such as failure recovery, analysis for historical data, debugging, and auditing. Particularly, our study focuses on columnar NoSQL databases. We propose and evaluate two solutions to address the data recovery problem in columnar NoSQL and implement our solutions based on Apache HBase, a popular NoSQL database in the Hadoop ecosystem widely adopted across industries. Our implementations are extensively benchmarked with an industrial NoSQL benchmark under real environments.

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Advancing Enterprise Data Strategies: Integration and Optimization of SAP HANA for Backup and Disaster Recovery

Saga

2023

J Arti Inte & Cloud Comp

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This research paper delves into the integration and optimization of SAP HANA, in enterprise data centers with a focus on strategies to ensure backup and disaster recovery (DR). SAP HANA, which is an in memory database brings about a transformation in how data's stored and processed. It offers real time analytics, predictive modeling and advanced analytics capabilities. The paper discusses the utilization of SAP HANA in both on premise and cloud environments while highlighting the need to strike a balance between speed, scalability and challenges such as data security and system integration. Emphasizing the importance of backup and DR protocols it underscores their role in protecting data integrity and ensuring uninterrupted business operations even during high risk situations. Through exploration of methods and technologies for backup and recovery this study aims to enhance efficiency in handling data within the realm of SAP HANA. By shedding light on implications related to deployment of SAP HANA – regarding data security measures and operational resilience – this research contributes to the ongoing discourse surrounding, in memory databases within enterprise settings.

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Holistic disaster recovery approach for big data NoSQL workloads

Cited by 3 publications

References 1 publication

Recovery and Concurrency Challenging in Big Data and NoSQL Database Systems

Recovery and Concurrency Challenging in Big Data and NoSQL Database Systems

The Time Machine in Columnar NoSQL Databases: The Case of Apache HBase

Advancing Enterprise Data Strategies: Integration and Optimization of SAP HANA for Backup and Disaster Recovery

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