tax-onomy of big data storage technologies. Technologies are commonly designed to provide a storage solution along with high scalability to growing data volumes with heterogeneous data structures. These databases are deployed over distributed systems to achieve high availability, improved data access, performance, and fault tolerance. However, the extent of provisioning these services is different for each database, which makes these databases distinguishable from each other. This extent helps identify these databases as of CP or AP type. In Table 5, contemporary key-value, column-oriented, document-oriented, and graph da-tabases are presented and analyzed on the basis of their adopted procedures for consistency, data parti-tioning, replication, and indexing. Moreover, their correlation with Brewer’s CAP categorization is kept in the analysis to suggest their type.
Key-value databases apply data partitioning on separate records regardless of each having the same attributes. A unique key is assigned to each record, and the value contains data of a record. Although they are mostly suitable for unstructured data, structured data can be presented with these databases if record- based data retrieval and analysis needs to be per-formed on it. As far as licensing is concerned, most of the column-oriented and key-value databases are open source. BigTable is the only column-oriented database that has proprietary license. Likewise, Dy-namoDB is the only key-value database that is available commercially. Column-oriented databases such as HBase, Hypertable, Cassandra, and BigTable are mostly suitable for structured data with enough support to unstructured data. These databases apply vertical partitioning on the data and store each column as a separate chunk of data, and performing queries on attributes as well as attribute-based analysis of data is easier with this data model.
As far as document-oriented databases are con-cerned, these databases also have the key-value data structure. However, the value identifies a document instead of a record. Documents are usually XML files with some schema. Compared to the key-value data model, document databases have less support to scalability and unstructured data. Furthermore, data-bases having document structure are, on average,
prone to availability and consistency. For instance, MongoDB, Terrastore, and RethinkDB are consistent databases, whereas SimpleDB, CouchDB, OrientDB, Rocket U2, and Qizx are highly available. Graph databases are well-structured databases, where ana-lyzing data as well as their relationships is significant.
Although graph databases do not have good support to scalability and clustering, these databases offer complex data structures. According to our analysis, all graph databases in this survey are CP-type systems.
With the proliferation of big data, industry and academia are more interested in data management than computational management. The technology has much evolved in provisioning vast storage and pro-cessing resources. However, in big data management, efficient techniques for data acquisition, prepro-cessing, proprepro-cessing, and storage are desirable. The ongoing development is focusing on bringing effi-cient solutions that support big data management. The Hadoop framework (Lam et al., 2010) has become the de facto standard for big data processing with the MapReduce programming model, which offers batch processing of extensive volume of files residing over commodity hardware, whereas for real-time big data processing, the simple, scalable streaming system (S4) (Neumeyer et al., 2010) is a widely adopted tool. The Apache Software Foundation has a list of contribu-tions to big data solucontribu-tions such as Mahout, Lucene, Hive, Pig, and Spark.
Besides processing, storage optimization is also important. Methods for data clustering, replication, and indexing for efficient storage utilization and data retrieval are of main concern. Storage-optimizing hierarchical agglomerative clustering (Buza et al., 2014), the K-means algorithm (Zhao et al., 2009), and the artificial bee colony (ABC) algorithm (Karaboga and Ozturk, 2011) are the clustering approaches used in recent research. The storage technologies presented in this paper have built-in support to replication, which in turn ensures data availability, fault tolerance, and less data accessing delay. For efficient replication, the ABC algorithm (Taheri et al., 2013), D2RS (Sun et al., 2012), and JXTA-overlay P2P platform (Spaho et al., 2013) are the famous techniques used. HAIL (Dittrich et al., 2012) provides an indexing solution for Hadoop, which improves the data search and re-trieval process.
While summarizing the storage technologies presented in this paper, it can be stated that all the storage structures are partition-resilient, meaning that network partitioning and disconnections in distrib-uted systems is rare and there are many options to handle and recover from a partitioning situation. Thus, the choice is only between consistency and availabil-ity. As illustrated in Section 4, the support to both of them is beyond the possibility of a distributed system.
Thus, the discussed distributed storage systems are placed in either category. Storage systems that pro-vide more support to consistency are CP-type systems.
Apart from the efficiency of available big data storage technologies, the challenges of storing future big data still need to be addressed and considered. Finding and adopting the tradeoff between consistency and availability so that more throughput gains can be achieved from distributed storage systems leads to a number of challenges in this research. The following are some of these challenges:
1. Frequent data update and schema change: The update rate is mostly very high and the volume of data is growing very rapidly. In case of unstructured data, change in schema is also very common. However, available storage technologies are scalable, but the need to be efficient in data updates and schema is still under consideration. Some technologies like HDFS do not offer data update but append operation in only support.
2. Partitioning method: Data models suggest two methods of big data partitioning to make it contained on distributed storage nodes accordingly. Horizontal or vertical partitioning is applied on data based on access patterns. Data may be required to be analyzed by the features or records. Thus, the choice of column-oriented or key-value NoSQL databases is available. However, the prediction of access pattern might be wrong or change during execution. This poses a critical research challenge on existing data models specified for big data storage solutions.
3. Replication factor: Data are replicated over multiple sites to achieve fault tolerance and high availability to its users. Although this concept makes the storage very efficient to improve access perfor-mance, it compromises data consistency and does not suit in frequent data changing and up-to-date access requirement conditions. This leads to poorer access performance if frequent consistency locks are
expe-rienced. Furthermore, the number of replicas for data is a multiple of storage space consumption. Some-times it looks wise to access data from a remote site rather to use local storage space. Therefore, precon-figured or customized replication by applications or users is a challenge.
4. User expertise: Data are becoming more complex nowadays. At the same time, the user space is broadened by enterprises, so that users from dif-ferent domains can execute queries on data according to their problems. It reveals the requirement of simple-to-deploy and easy-to-use storage but with higher performance than the relational database solu-tion. To achieve improved performance, sometimes these databases integrate DBMS platforms, which undoubtedly meets the expectations, but the imple-mentation and configuration process becomes com-plex for non-expert users.
6 Conclusions
A categorization of contemporary storage tech-nologies for big data has been attempted in this paper.
The main objective of the paper is to investigate and analyze state-of-the-art big data storage technologies and to present their categorization based on Brewer’s CAP theorem for distributed systems, so that re-searchers and big data analysts can explore enhanced storage solutions in specific domains, where availa-bility, consistency, and fault tolerance are considera-ble requirements. Moreover, presentation of limita-tions of existing storage technologies for adequate scalability is a major concern of this work. This paper focuses mainly on categorization under data models and classifies modern storage technologies for big data as key-value, column-oriented, document- oriented, and graph systems. These technologies are further studied and analyzed by a critical review using Brewer’s CAP theorem. A discussion is carried out to survey and highlight the efficiency of each storage system for the scalability, consistency, and availabil-ity requirements of big data. Future key challenges with regard to storing big data are also emphasized in the discussion. In conclusion, it can be stated that systems are inclined towards provisioning con-sistency and availability as required by applications or users. This leading phenomenon is used to suggest
categorization of available big data storage systems so that the selection with either preference becomes obvious.
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