Apache Hadoop continues its big data dominance
The potential to extract new and actionable insights from massive volumes of disparate data has driven many organizations to pursue big data applications. However, some have found that their legacy infrastructure is ill equipped to provide a foundation that will facilitate collecting, housing and analyzing datasets at an optimal level. Even when businesses invest in big data-specific solutions, they may find that the clusters they have employed are still insufficient for their grand analytics needs. That is why so many enterprises have opted to utilize Apache Hadoop and its array of robust Hadoop clusters. The ability to house and process large amounts of both structured and unstructured data has made Apache Hadoop the ideal foundation for any big data project.
The unparalleled operability of Hadoop clusters
TechTarget's Brien Posey recently examined the numerous benefits afforded by Hadoop clusters, including:
- Data agnosticism – Many big data platforms can process structured data such as text files and statistics with ease. However, they are unable to extract usable information from unstructured data like video and audio files. This can result in data analytics projects that return flawed and inaccurate insight because they have not had access to all the available data. Businesses that depend on faulty analytics to guide their decision making may wind up going down the wrong operational path. Hadoop clusters, on the other hand, can break down unstructured data files into more manageable pieces, allowing analytics programs to effectively analyze such sources.
- Cost effectiveness – Big data has cultivated a reputation over the years for being too expensive for many companies. Since the emergence of Apache Hadoop, however, businesses have had access to an open-source platform that requires little initial investment to begin building analytics programs.
- Resiliency – Data loss presents a major challenge for numerous aspects of enterprise operations, and analytics projects are not immune. Losing valuable data could stop a program in its tracks or compromise its findings and produce ineffective insight. Apache Hadoop eludes these issues by copying data to other cluster nodes when it is sent for analysis. If a node within a Hadoop cluster were to fail for any reason, copies of the lost data would remain.
- Scalability – Businesses may quickly find that their big data projects are taking on more information than they had originally intended. Apache Hadoop can provide the scalability to surpass these ceilings and continue the analytics initiative unabated.
The inherent prowess of Apache Hadoop recently received another boost when MicroStrategy announced it was partnering with Hortonworks to certify the enterprise software company's Hortonworks Data Platform with its business intelligence software. According to ZDNet, this will allow MicroStrategy users to integrate their BI data with Hortonwork's powerful Hadoop ecosystem. Hortonworks officials reportedly stated that they planned to formulate additional partnerships in the future, which would provide even greater functionality for Hadoop adopters in the future.
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