Is adopting Spark and hadoop Enterprise version helpful in that case?
In this special featured story, author will highlight the ways with which hadoop and spark enterprise adoption is gathering major considerations from users to drive business value by getting closer to real time analysis of data.
To keep up the spirit and stay competitive in the world of Big Data, it is essential to intend strategies around data analysis. For faster responses from their data, businesses need to ask questions as early as possible.
Companies cannot hold on to the process of transporting data to a central processing system and later making analysis report because it is time consuming. Today, major companies are trying to get closer to real time data analysis to drive more profits. Big data technologies like Spark and Hadoop are making their job easier.
These technologies promote economic scaling, flexible development patterns, and can be easily adapted to requirements of the business.
As per the latest reports, Hadoop has reached the hype cycle and is now offering substantial business value to half the enterprises that are using Hadoop.
As the demand for big data is rising immensely, more firms are adopting big data technologies like Spark and Hadoop. It is required for companies to react to real-time events and decision making due to volume and veracity of big data. Such adoption of technologies assists them in handling big data in most efficient way.
Today, Hadoop developers and consultants are leveraging these technologies for capturing single view of customer, core data mining, and assisting data scientists in performing predictive analytics.
As hadoop is an open source framework, companies have got one more reason to adopt it. The major reason behind the adoption will remain constant, i.e. scaling out the needs. The best thing about Hadoop is that it allows apps to store all types of data, including semi structured, unstructured, or structured one from multiple sources.
Hadoop may be interesting to use, but Spark worth your time. You cannot use Hadoop MapReduce to analyze small data sets instantly. Spark meets fast data requirements in medium companies and supports bulk batch processing.
Spark is gaining more numbers for enterprise adoption with immense excitement among big data community.
Hadoop developers should not think that both the Spark and Hadoop are unproductive tools. Companies making adoption of Hadoop and Spark technologies are gaining success in big data processing.
