Through Apache pig big data can be analyzed seamlessly as this platform delivers all features through which a sophisticated data with complex language applied in the data analysis programs, could be analyzed substantially. The Apache Pig programs are open to parallelization, and through this feature large data sets can be easily managed. It can be defined as a high-level method for the parallel encoding of MapReduce jobs to be implemented on Hadoop clusters.
Brief Description of Apache pig big data
- Pig’s infrastructure layer includes a compiler that generates series of Map-Reduce programs, for which large-scale parallel implementations are already at hand (e.g., the Hadoop) Pig’s textual language is called Pig Latin, which have the following advantages:
- Facilitated programming mode. Through the Apache big complicated procedures of assorted data, transformations are clearly programmed as data flow sequences which are easy to write, understand, and preserve. The parallel execution of simple, parallel data analysis task is flawlessly achieved.
- Optimization. The encoding allows the system to optimize their automatic execution, thus more focus is given to semantics.