Big Data: a continuous evolutionBig Data continues to evolve today and seems to be in the early stages of evolution. It will continue to grow and will require constant research initiatives to keep pace. This article will examine the definition of Big Data and how it is used, why the current DBMS is incapable of handling Big Data effectively, what hardware and software solutions are being tested, and what challenges researchers are facing. term used today to talk about the dramatically increasing amounts of data (primarily unstructured, but can also include structured and semi-structured data) available for exploitation [1]. Data mining attempts to derive meaningful insights from data. As the amount of data in different varieties continues to increase, it becomes more difficult to process useful information with an acceptable return time. Current software and hardware tools fail to meet the needs of Big Data. Big Data requires the ability to process complex computer data at the petabyte or exabyte level. [2].Big Data is growing from many sources, and as storage capacity has doubled in size every 14 months over the past 30 years, data retention has become increasingly cheaper [3] . Some of the data sources are internet social media, mobile sensors, astronomy, transaction logs and many more [4]. Companies today want to collect massive amounts of data that may not be useful today, but could be useful later. The popular social media site Facebook collects more than 500 terabytes per day [4]. The term Big Data is not defined only by its volume, by its capacity to retrieve knowledge within a reasonable time. For example, Netflix, a video streaming service, uses a machine learning technique ...... middle of paper ...... the future", ACM SIGKDD Explorations Newsletter, Vol. 14, No. 2 , pp. 1-5, 2012.[6] C. Ordonez, “Can we analyze big data in a DBMS,” in DOLAP '13 Proceedings of the Sixteenth International Workshop on Data Warehousing and OLAP, San Francisco. , 2013.[7] . Bear, A. Lamb, and N. Tran, “The Vertica Database: SQL RDBMS For Managing Big Data,” in MBDS '12 Proceedings of the 2012 Managing Big Data Systems Workshop, San Jose. , 2012.[8] Tran, S. Bodagala, and J. Dave, “Designing Query Optimizers for Future Big Data Problems,” Proceedings of the VLDB Endowment, vol. , 2013.[9] “From Databases to Big Data,” IEEE Internet Computing, vol. 3, pp. 4-6, May 2012.[10] Management Review, IEEE, vol. 42, no. 1, p... 8-9, 2014.