Unlike MapReduce, Tez project aimed to be highly customizable. This meant that developers could use the cases without being forced to work in the platforms way. Projects such as Hive and Pig were incorporated to make sure that the response time taken is much lesser than that of MapReduce (Papp, pg. 24).
The main elements of its design philosophy was to empower the developers to have the freedon to carry out their tasks in an efficient manner. In addition to that, the philosophy wanted to ensure that there was better execution performance (Papp, pg. 25).
Its key features included:-
Expressive dataflow APIs- to achieve an expressive-dataflow-definition API, the team developed a structural kind of API whereby the developer can add all the processes thereby visualize their construction (Papp, pg. 25).
Flexible input-processor-output runtime model- By connecting different processors, inputs and outputs, the developer can be able to dynamically construct runtime executors
Data type agnostic- The type of data in movement does not matter. This means that any format of data can be used as the platform is only concerned with the movement of the data and not the data itself (Papp, pg. 27).
Simple deployment- in the Tez platform, there is really no need to deploy anything on the cluster since the platform is completely a client-side application. The relevant libraries can be uploaded to the HDFS and the Tez client submits them to the libraries (Papp, pg. 30).
Even with its vast developer based advantages, Tez is still low on the transfer of Big Data. It is faster that MapReduce in stream transfer of data. The limitations makes it disadvantaged when dealing with data that need immediate relay and execution (Papp, pg. 30). ...
... middle of paper ...
...ike Hadoop whose loop outside the system and based on driver programs. It is also more efficient that spark whose iterative programs look like different jobs which are independent of one another. Flink therefore becomes the sole winner in terms of Big Data transfer (Papp, pg. 49).
When it comes to managing Big Data efficiently, there is only one winner. The Apache Flink is designed to ensure that real time events are relayed in real time. Furthermore, when activities require the urgency of analysis, the Big Data need to be analysed in real time so that the end user gets the best of services (Papp, pg. 44).
Furthermore going by the other platforms and architecture, it is clear that Flink is one step ahead in terms of streaming and thereby setting the trend of adoption for the next generation. Flink is the future of Big Data management (Papp, pg. 43)
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