Big Data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.
Big Data Analytics are the natural result of major global trends: mobile computing, cloud computing and social network.
The combination of volume and variety of data made it really complex and cumbersome with the current data management and analytics technology and practices. As a solution traditional data management and analytics software and hardware technologies, open-source technology, and commodity hardware are merging to create new alternatives to address Big Data Analytics, Big Data Analytics has given organizations the ability to retain more data than before without making decisions about which half to keep or how much history to keep.
Enabling Big Data Analytic applications presents the organization with three important possibilities:
- Improving Operational Efficiency
- Increase Revenue
- Achieve Competitive Differentiation
|Improve Operational Efficiencies||Increase Revenue||Achieve Competitive Differentiation|
|Reduce risks and costs||Sell to micro-trends||Offer new services|
|Save time||Enable self service||Seize market share, Lower complexity|
|Improve customer experience||Incubate new ventures|
Big Data is now changing the way the companies address three related needs:
- How much do I need to spend?
- How do I allocate that spend across all the marketing communication touch points?
- How do I optimize my advertising effectiveness against my brand equity and ROI in real-time?
There are many Big Data technologies that have been making impact on the new technology stacks for handling Big Data, but Apache Hadoop is one technology that has been the core of Big Data talk. Hadoop is an open-source platform for storage and processing diverse data types that enables data-driven enterprises to rapidly derive the complete value from all their data.
The two critical components of Hadoop are:
- The Hadoop Distributed File System (HDFS): This is the storage system for a Hadoop cluster. When data lands in the cluster, HDFS breaks the cluster and allocates the peieces among different server participating in the cluster.
- MapReduce: MapReduce is the agent that distributes the work and collects the results.
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