Big Data Hadoop Certification Training Course
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The above video is the recorded session of the webinar on the topic “Introduction to Hadoop Administration”, which was conducted on 14th August
Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization of the huge amount of data.
Here is a presentation on the topic ‘ Introduction to Hadoop Administration’:
In the earlier data analytics architecture, there were certain limitations like not being able to explore original high fidelity raw data, computable data not being scalable and premature data death. The above diagram clearly explains where the failures occur during data analytics process.
With Hadoop, several of the above mentioned setbacks are overcome.
Read more here.
Hadoop 2.X has the added feature – YARN. YARN takes out all the granular details managed by the administrator and thereby eases the job of a Hadoop Administrator.
HDFS and MapReduce are the core component of Hadoop 1.X. While the HDFS is responsible for distribution for files across the nodes, tracks NameNode locations and is natively redundant, the MapReduce is responsible for splitting the tasks across the processor, assembiling the distributed data and managing the JobTracker.
Hadoop 2.X is the advanced version of Hadoop 1.X. Here, the core components are predominantly the same, along with added features like High Availability, resource management and job scheduling/monitoring, shared resource for cluster and maintain API compatibility with previous established releases of Hadoop.
There are two main components in HDFS. They are:
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