Big Data ventures have captivated business officials with the guarantee of higher business returns and more prominent client understanding. Numerous profile victories have likewise taken the features, hastening enormous big data investment. The individuals who adjusted their business and IT functionality to exploit this information downpour have driven enormous returns through income development, expanded fact based choices and creative client engagement crusades. Be that as it may, far less discussed is the huge number of big data projects that have hit a low level inside organizations that have not possessed the capacity to convey the guaranteed pot of gold. Business and innovation reasons join to achieve these problematic outcomes.

The achievement of any huge big data venture essentially relies upon an undertaking’s capacity to catch, store furthermore and oversee its information. The better a  company can give quick, reliable and secure information to business chief’s the higher the odds of accomplishment in misusing huge information, getting arranged profit for ventures and defending further speculations. Below mentioned are best five most regular missteps undertakings make when moving toward big data activities and how to stay away from them.

The Five Vital Big Data Integration Mistakes

•  Not picking an enterprise grade Hadoop establishment and information incorporation technology
•  Retaining obsolete data warehousing models as opposed to concentrating on present day Big Data engineering designs
•  Not prioritizing productive data integration standards
•  Underestimating the significance of administration and governance, and lastly
•  Disregarding the data processing power energy of Hadoop/NoSQL when dealing with complex workloads.

Comprehension and settling these five standards will jump start your Big Data Integration venture.

The Oracle Advantage

Oracle Data Integration arrangements give feature evidence, modern portfolio of applications and tech s that enables clients to succeed in their enormous Big Data projects.