The critical thing to remember about Spark and Hadoop is they are not mutually exclusive or inclusive but they work well together and makes the combination strong enough for lots of big data applications.

1. Hadoop VS Spark
The critical thing to remember about Spark and Hadoop is they are not mutually
exclusive or inclusive but they work well together and makes the combination strong
enough for lots of big data applications.
• Hadoop Defined
A software library and a framework for permitting the distributed processing of big
data sets among computer clusters using with the help of noncomplex programming
models is called Hadoop and is the project of Apache organization.
From scaling single computer systems up to thousands of systems for computing
power and storage, Hadoop does the job with ease.
For creating the Hadoop framework there are a set of modules created by Hadoop.
The Primary Hadoop Framework Modules Are:
Hadoop Common
Hadoop Distributed File System (HDFS)
Hadoop YARN
Hadoop MapReduce
There are lots of other modules apart from the above modules and they are Hive,
Ambari, Avro, Pig, Cassandra, Flume, Oozie and Sqoop which induces Hadoop's
power to reach big data applications and large data processing.
When dataset becomes very large or tough, Hadoop is used by most of the companies
as their current solutions cannot process the information by taking lots of time.
The ideal text processing engine is none other than MapReduce and it is used to the
best when compared to crawling and searching the web.

2. • Spark Defined
A rapid and a proper engine for big data processing used by most of the Apache
Spark developers is called Spark. Hadoop's big data framework is 800-lb gorilla and
Spark is 130-lb big data cheetah.
The real-time data processing capability and MapReduce's disk-bound engine are
compared to and the real-time game is won by the former. Spark is also considered a
module on Hadoop project page.
A cluster-computing framework called spark means it is contesting with lots of
MapReduce than with the whole Hadoop.
The main difference between Spark and MapReduce is that persistent storage is used
by MapReduce and Spark uses Resilient Distributed Datasets (RDDs) under the Fault
Tolerance section.
1. Performance
The performance of processing in Spark is very fast because all the processing is
done only in the memory and it can also use disk space for data that doesn't fit in the
memory. For gathering information on goingly this was installed and there was no
need for this data in or near real-time.
2. Ease of Use
It is not good only in terms of performance but is also easy to use and is user-friendly
for Scala, Python, Java, etc. Most of the users and developers use the interactive
mode of Spark for its queries and other actions. There is no interactive mode in
MapReduce but Pig and Hive make the operations quite easier.
3. Costs
Both Spark and MapReduce are the projects of Apache and they are opensource and
there is no cost for these products. These products are made to run on commodity
hardware and are called white box server systems. It is a well-known fact that Spark
systems do costs more due to high requirements of RAM for running in the memory.
Similarly, the number of systems needed is also significantly reduced.
4. Compatibility
Both Spark and MapReduce are working well with each other with respect to data
sources, file formats, business intelligence tools like ODBC and JDBC.

3. 5. Data Processing
MapReduce is a batch-processing engine. MapReduce operates in sequential steps by
reading data from the cluster, performing its operation on the data, writing the results
back to the cluster, reading updated data from the cluster, performing the next data
operation, writing those results back to the cluster and so on.
A sequential step of operation is done in MapReduce which is a batch-processing
engine and it does the operation on data and returns the result to the cluster and
performs the next data operation and writing it back, so on and so forth.
A similar operation is done by spark but everything is done in one step and in
memory. The data is read from the cluster and the operations are done on data and
written back to the cluster.
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