Basically Spark is a framework - in the same way that Hadoop is - which provides a number of inter-connected platforms, systems and standards for Big Data projects. Spark has proven very popular and is used by many large companies for huge, multi-petabyte data storage and analysis. Spark is great at handling programming models involving iterations, interactivity that includes streaming and much more. That is a big reason Apache Spark is considered a prime replacement for MapReduce.

Apache Spark is an open source parallel processing framework for running large-scale data analytics applications across clustered computers. It can handle both batch and real-time analytics and data processing workloads.

Spark uses Micro-batching for real-time streaming. Apache Spark is open source, general-purpose distributed computing engine used for processing and analyzing a large amount of data. Just like Hadoop MapReduce, it also works with the system to distribute data across the cluster and process the data in parallel.

Future is all about big data and spark provides a rich set of tools to handle real-time the large size of data. Its lighting fast speed, fault tolerance, and efficient in-memory processing make Spark a future technology.