Apache’s Hadoop framework has become familiar with the big data movement and is dominant for data management platform. We used to wonder how can we store the information but now we think on whether we can afford to throw away the information. Big Data Hadoop has changed the economics of analysing and storing the information. It’s easier to store, manage and analyse the information. Hadoop requires lots of hands on coding and limited resources for supporting tools are available. People are doing amazing things with this technology. More robust SQL capabilities have been integrated with Hadoop technology to bring out an entire SQL-based ecosystem.
To scale up databases are using Hadoop. Hadoop shall grow in the big data arena; the open source technology shall evolve in bigger enterprise. Hybrid architectures are being used where there is a combination of two technologies in specialized roles. In real world deployment, hybrid deployments involve in-memory, parallel processing, key value stores, and stream computing and graphical databases. Thus specific categories of analytical workloads, deployment roles, data sources and downstream applications are used in the real world deployment. Many users have realized that no one type of big data platform is optimal for all user requirements. This big data landscape must be abstraction layer or standard query virtualization that helps in easy access of SQL and back-end platforms. Big Data solution must help the SQL developers in easy integration and tapping into full range of big data platforms. Still the big data industry lacks query virtualization approach. The developers must wrangle SQL languages for data access. Many of the languages are not associated with Big data which poses as a challenge to the developers. Thus implementing Big Data is an hot topic for the developers. To view the details of Hadoop Developer
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