What are the cases where Apache Spark surpasses Hadoop?

[DataFlair Team]

How is Apache Spark better than Hadoop?
What are the benefits of Apache Spark over Apache Hadoop?

[DataFlair Team]

We can compare the Hadoop and Spark on below area:

• Storage
• Computation
• Computation Speed
• Resource

Compared to Hadoop, main advantage of Spark is its computation speed.Spark has:

• Lightning-fast cluster computing.
• Apache Spark is a fast and general engine for large-scale data processing.

This advantage is because of RDD which is the basic abstraction of spark.
Apart from this, Spark architecture and Spark execution engine are the two reason that Apache Spark is faster compared to Hadoop.

Hadoop is ideal for batch processing while Spark can do batch processing as well as iterative, interactive, streaming processing.

For difference between Apache Hadoop MapReduce and Apache Spark refer ApacheSpark vs. Apache Hadoop MapReduce

[DataFlair Team]

The below points are the benefits of Apache Spark over Apache Hadoop

1.Speed

Apache Spark is lightning fast cluster computing tool.
Map reduce reads and writes from disk and that slows down the processing speed.

2.Difficulty

It is easy to program in Spark as it contains many high-level operators with RDD – Resilient Distributed Dataset.
In MapReduce, developers need to hand code each and every operation which makes it very complicated.

3.Easy to Manage

Spark performs Batch, Interactive, Machine Learning and Streaming in the same cluster.
but MapReduce only provides provision for batch processing.

4.Latency

Spark provides low latency computing
Map Reduce is a high latency computing framework

5.Interactive mode

Spark can process data interactively
MapReduce doesn’t have interactive mode

6.Streaming

Spark can process real time data through Spark streaming.
With MapReduce, you can only process data in batch mode.

7.Ease of use

Spark is easier to use, its abstraction (RDD) enables the user to process data using high-level operators. It provides rich APIs in Java, Scala, Python, and R.
Map Reduce is complex; we need to handle low-level APIs to process the data, which requires lots of hand coding.

For comparison between Apache Hadoop MapReduce and Apache Spark refer ApacheSpark vs. Apache Hadoop MapReduce

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