Database technologies in the world of big data

ABSTRACT

Now we have a number of database technologies called usually NoSQL, like key-value, column-oriented, and document stores as well as search engines and graph databases. Whereas SQL software vendors offer advanced products with the capability to handle highly complex queries and transactions, NoSQL databases share rather characteristics concerning scaling and performance, as e.g. auto-sharding, distributed query support, and integrated caching. Their drawbacks can be a lack of schema or data consistency, difficulty in testing and maintaining, and absence of a higher query language. Complex data modelling and the SQL language as the only access tool to data are missing here. On the other hand, last studies show that both SQL and NoSQL databases have value for both for transactional and analytical Big Data. Top databases providers offer rearchitected database technologies combining row data stores with columnar in-memory compression enabling processing large data sets and analytical querying, often over massive, continuous data streams. The technological progress led to development of massively parallel processing analytic databases. The paper presents some details of current database technologies, their pros and cons in different application environments, and emerging trends in this area.

References

1. Abramova, V, Bernardino, J., Furtado, P. Which NoSQL Database? A Performance Overview. Open Journal of Databases (OJDB), Vol. 1, No. 2, 2014, pp. 17--24.
2. Brewer, E. A.: Towards robust distributed systems. Invited Talk on PODC 2000, Portland, Oregon, 16-19 July, 2000.
3. Brewer, E. A.: CAP twelve years later: how the 'rules' have changed. Computer, Vol. 45, No. 2, 2012, pp. 22--29.
4. Bu, Y., Howe, Y., Balazinska, M, Ernstm M. D. The HaLoop approach to large-scale iterative data analysis. The VLDB Journal, Vol. 21, No. 2, 2012, pp. 169--190.
5. Chen, C. L. Ph., Zhang, CH.-Y.: Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences 275 (2014), pp. 314--347.
6. Corbett, J. C., Dean, J. C., Epstein, M. et al. Spanner: Google's Globally-Distributed Database. In: Proc. of 10th USENIX Symposium on Operation Systems Design and Implementation (OSDI 2012), Hollywood, 2012, pp. 261--264.
7. Dean, D., Ghemawat, S. MapReduce: Simplified Data Processing on Large Clusters. Communications the ACM, 51(1), 2008, pp.107--113.
8. EnterpriseDB Corporation. Using the NoSQL Capabilities in Postgres. White Paper, 2014.
9. Gates, A., Natkovich, O., Chopra, S., Kamath, P., Narayanamurthy, et al. Building a high level dataflow system on top of MapReduce: The pig experience. PVLDB, 2(2), 2009, pp. 1414--1425.
10. Grolinger, K., Higashino, W. A., Tiwari, A., and Capretz, M. A. M.: Data management in cloud environments: NoSQL and NewSQL data stores. Journal of Cloud Computing: Advances, Systems and Applications, 2:22, 2013, pp. 1--24.
11. Google®: BigQuery Analytics. John Wiley & Sons, Inc., 2014.
12. Hellerstein, J., Stonebraker, M. Anatomy of a Database System. Chapter 1 in Reading in Database Systems, 4th Edition, MIT Press Book, 2005, pp. 42--54.
13. Lokegaonkar, S., Joshi, A.: Concurrency Control Schemes in NeWSQL Systems. Int. Journal of Computer Engineering and Technology (IJCET), Volume 5, Issue 8, August (2014), pp. 97--104.
14. Malewicz, G., Austern, M. H., Bik, A. J. C., Dehnert, J. C., Horn, I., Leiser, N., and Czajkowski, G.: Pregel: a system for large-scale graph processing. In: Proc. of SIGMOD '10 Proc. of the 2010 ACM SIGMOD Int. Conf. on Management of data, 2010, pp. 135--146.
15. Melnik, S., Gubarev, A., Long, J. J., Romer, G., Shivakumar, S., Tolton, M. and Vassilakis. T.: Dremel: Interactive analysis of web-scale datasets. In: Proc. of the 36th Int'l Conf on Very Large Data Bases, 2010, pp. 330--339.
16. Mohamed, M., A., Altrafi, O. G., Ismail, M. O. Relational vs. NoSQL Databases: A Survey. Int. Journal of Computer and Information Technology, Vol. 03, No. 03, 2014, pp. 598--601.
17. O'Neil, P., Cheng, E., Gawlick, D., O'Neil, E.: The log-structured merge-tree (LSM-tree). In: Acta Inf. 33 (1996), No. 4, pp. 351--385.
18. Pokorny, J. NoSQL Databases: a step to databases scalability in Web environment. International Journal of Web Information Systems, 9 (1), 2013, pp. 69--82,
19. Pokorný, J. New Database Architectures: Steps Towards Big Data Processing. In: Proc. of IADIS European Conference on Data Mining (ECDM'13), António Palma dos Reis and Ajith P. Abraham Eds., IADIS Press, 2013, pp. 3--10.
20. Radenski, A. Big Data, High-Performance Computing, and MapReduce. In: Proc. CompSysTech'14, June 27-28, 2014, Ruse, Bulgaria, 2014, pp. 13--24.
21. Rosenthal, D.: Next gen NoSQL: The demise of eventual consistency?, 2013 https://gigaom.com/2013/11/02/next-gen-nosql-the-demise-of-eventual-consistency/
22. Valiant, L. G.: A bridging model for parallel computation, Communications of the ACM, Volume 33, Issue 8, Aug. 1990, pp. 103--111.
23. Shute, J., Vingralek, R., Samwel, B., Handy, B., Whipkey, Ch., et al. F1 A Distributed SQL Database That Scales. PVLDB 6(11), 2013, pp. 1068--1079.

• Computer systems organization

  • Architectures

• Information systems

  • Data management systems

    • Database management system engines