What’s the Difference Between Hadoop and Spark?

Both Apache Hadoop and Apache Spark are big-data frameworks, but they do not serve the same purposes. Actually there are multiple big data frameworks on the market, Apache and Hadoop and Apache Spark are most known among them. They are commonly used to train the starters. But in real life, choosing the right big data framework is a challenge. Businesses consider each of the framework for their particular needs.

The main difference between Apache Hadoop MapReduce and Apache Spark lies is in the processing. MapReduce is a part of the Hadoop framework for processing large data sets with a parallel and distributed algorithm on a cluster. MapReduce algorithm contains two tasks – Map and Reduce. Map converts a set of data into another set of data breaking down into key/value pairs. Reduce combines the data tuples into smaller sets. In MapReduce, the data is distributed over the cluster and processed. Spark does the job it in-memory but Apache Hadoop MapReduce read and write from and to a disk. So, the speed of processing by Apache Spark can be 100 times faster. Secondly, the volume of data processed by Hadoop MapReduce is far larger than Apache Spark. The well known arguments that Apache Spark is good for real-time processing whereas Apache Hadoop is preferred for batch processing. Apache Spark is compatibile with Apache Hadoop. Real-time processing is a need in healthcare, some of the government agencies, telecommunication, finance/banking, stock market. In these cases, using Spark is suitable.

Other important difference is in Apache Spark’s capability of graph processing. Spark is suitable for iterative computations typical in graph processing. Apache Spark has the graph computation API named GraphX.
Spark’s MLlib is the built-in machine learning library with out-of-the-box algorithms which also run as in-memory.

Apache Spark is fast cluster computing tool when compared to Hadoop. Apache Spark is easy to program as developers can save some time around hand coding. In case of MapReduce, developers practically hand code each work.
Installing Spark on a cluster usually enough to handle most of the requirements.

MapReduce is dependent on different engines, such as Storm, Giraph, Impala, etc and Spark is fault-tolerant. However, Apache Hadoop MapReduce is secure for having Kerberos and support of Access Control List.

As we can see – Hadoop MapReduce has some special use-cases and deploying Hadoop in complex situation demands more working skills.