The term Big Data has created a lot of hype already in the business world. Hadoop and Spark are both Big Data frameworks – they provide some of the most popular tools used to carry out common Big Data-related tasks. In this blog, we will cover what is the difference between Spark and Hadoop MapReduce.
Spark – It is an open source big data framework. It provides faster and more general purpose data processing engine. Spark is basically designed for fast computation. It also covers a wide range of workloads for example batch, interactive, iterative and streaming.
Hadoop MapReduce – It is also an open source framework for writing applications. It also processes structured and unstructured data that are stored in HDFS. Hadoop MapReduce is designed in a way to process a large volume of data on a cluster of commodity hardware. MapReduce can process data in batch mode.
Hadoop: Apache Hadoop provides batch processing. Hadoop developers a great deal in creating new algorithms and component stack to improve access to large scale batch processing.
MapReduce is Hadoop’s native batch processing engine. Several components or layers (like YARN, HDFS etc) in modern versions of Hadoop allow easy processing of batch data. Since MapReduce is about permanent storage, it stores data on disk, which means it can handle large datasets. MapReduce is scalable and has proved its efficacy to deal with tens of thousands of nodes. However, Hadoop’s data processing is slow as MapReduce operates in various sequential steps.
Spark: Apache Spark is a good fit for both batch processing and stream processing, meaning it’s a hybrid processing framework. Spark speeds up batch processing via in-memory computation and processing optimization. It’s a nice alternative for streaming workloads, interactive queries, and machine-based learning. Spark can also work with Hadoop and its modules. The real-time data processing capability makes Spark a top choice for big data analytics.
Resilient Distributed Dataset (RDD) allows Spark to transparently store data on the memory, and send to disk only what’s important or needed. As a result, a lot of time that is spent on the disc read and write is saved.
Spark – It can process real-time data i.e. data coming from the real-time event streams at the rate of millions of events per second, e.g. Twitter data for instance or Facebook sharing/posting. Spark’s strength is the ability to process live streams efficiently.
Hadoop MapReduce –MapReduce fails when it comes to real-time data processing as it was designed to perform batch processing on voluminous amounts of data.