There are many alternatives to Hadoop, but the others are far behind. Hadoop is the undisputed leader in Big Data.
The most promising alternative to Hadoop is Spark
The claim is, it runs 100x times faster than Hadoop in scenarios like iterative algorithms and interactive data mining . Spark is also used for data processing. Spark is also the engine behind Shark, a fully Apache Hive-compatible data warehousing system that can run 100x faster than Hive. A comparison of the performance of logistic regression using Hadoop MapReduce and Spark is shown in the below figure (advertised).
Spark benefits from in memory compared to hadoop’s disk based. It can cache datasets in memory to speed up reuse.
There might not be one solution fit all kind of framework and therefore, its wise to evaluate other related distributed frameworks like Spark which could help in achieving solution to specialized kind of scenarios/problem. Compatibility with hadoop is a plus.