Apache Spark, Hadoop MapReduce, Top 7 Differences, Electro4U

Apache Spark Vs. Hadoop MapReduce – Top 7 Differences

In the world of big data, Apache Spark and Hadoop MapReduce are two of the most popular technologies that have been widely used for distributed computing. Due to their similarities, many people often have difficulties differentiating between these two technologies. Here, we will discuss the top 7 differences between Apache Spark and Hadoop MapReduce.

1. Processing Speed

The primary difference between Apache Spark and Hadoop MapReduce is in their processing speeds. Apache Spark is a lot faster than Hadoop MapReduce. In fact, it is 100 times faster due to its advanced in-memory computing capabilities.

2. Processing Engine

Apache Spark uses a distributed processing engine called ‘Spark Core’, while Hadoop MapReduce uses Hadoop YARN as its distributed processing engine.

3. Programming Languages

Another difference between Apache Spark and Hadoop MapReduce is their programming languages. Apache Spark supports multiple programming languages, such as Java, Scala, Python, and R, while Hadoop uses only Java.

4. Scheduling of Jobs

Scheduling of jobs in Apache Spark and Hadoop MapReduce is very different. Apache Spark has an advanced scheduling system that allows for the scheduling of jobs based on conditions, while Hadoop MapReduce jobs are scheduled on an instruction-by-instruction basis.

5. Data Storage

Apache Spark and Hadoop MapReduce both use HDFS (Hadoop Distributed File System) as a data storage system. However, Hadoop MapReduce relies on HDFS for its processing, while Apache Spark can use any data storage system, such as Cassandra or S3.

6. Fault Tolerance

Apache Spark provides better fault tolerance than Hadoop MapReduce due to its ability to replicate data across different nodes. Hadoop MapReduce, on the other hand, does not provide this feature and therefore is more susceptible to failures.

7. Cost and Scalability

When it comes to cost and scalability, Apache Spark is far more cost effective than Hadoop MapReduce since it is much easier to scale out with Apache Spark. It also requires fewer resources to process the same amount of data.

These are some of the top differences between Apache Spark and Hadoop MapReduce that you should consider when making your decision on which technology to use.