In most organizations, data is always growing, changing, and manipulated and therefore time to analyze data significantly increases.
As a result, to process large and diverse data sets, ad-hoc analytics or graph data structures, there must be better alternatives to Hadoop / MapReduce.
Google (architect of Hadoop / MapReduce) thought so and architected a better, faster data crunching ecosystem that includes Percolator, Dremel and Pregel. Google is one of the key innovative leaders for large scale architecture.
Percolator’s architecture provides horizontal scalability and resilience. Percolator allows reducing the latency (time between page crawling and availability in the index) by a factor of 100. It allows simplifying the algorithm. The big advantage of Percolator is that the indexing time is now proportional to the size of the page to index and no more to the whole existing index size.
Dremel's architecture is similar to Pig and Hive. Yet while Hive and Pig rely on MapReduce for query execution, Dremel uses a query execution engine based on aggregator trees.
Pregel is architected for efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier. Distribution-related details are hidden behind an abstract API. The result is a framework for processing large graphs that is expressive and easy to program.