A recent survey states that the big data
professionals having Spark skills have enjoyed hike in their salary. If we
consider the statistics from any part of the world, the conclusion will be- to
learn Spark. The big data project known as Spark introduced by the Apache
Software Foundation has influenced the analytics world with its increasing
speed. It won’t’ be wrong to say that now Spark can be seen as a competitor of
Hadoop software.
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| Hadoop Training in Delhi |
Understanding the Apache Spark
→ Apache Spark is an excellent framework that
is helpful in executing general data analytics over the distributed system as
well as computing clusters like Hadoop. Apache Spark enables in-memory
computations at higher speed while the low latency data process on MapReduce.
It doesn’t replace Hadoop, rather operates
atop the already existing Hadoop cluster for accessing the HDFS or Hadoop
Distributed File System. Apache Spark can also process structured data in Hive
and streaming data from Twitter, Flume and HDFS. Madrid Software
Trainings provides complete practical hadoop
training in delhi.
What Makes Spark Stand Out?
It has been observed that Real time stream
processing is getting popular among all the big data functions. It means
analyze the data as it is captured and feed it back to the user. Spark can also
create difference in the field with its amazing speed. It is excellent when it
comes to operating machine learning algorithms. These are the most critical
reasons why Spark is popular and the demand of Spark developers are on rise.
Hadoop Vs Spark →
If you are aware of the latest trends in the world of big data, you must be aware that Hadoop has been there for quite some time which has made it a most widely used software system for various big data operations. The advent of Spark has created confusion among many enterprises. Having similar features, they both boast of their unique features and can produce great results if worked together. So, if you are making up your mind for Hadoop training, move ahead as it is the right time. Spark big data training will add benefit to your career if you are already involved in Hadoop oriented functions. Madrid Software Trainings is rated as the best hadoop institute in delhi by professionals.
In-depth Overview →
Whenever there is a discussion on the topic
of Hadoop, the comparison with Spark happens. Reason behind Hadoop’s popularity
is that the Hadoop Distributed File System or HDFS. At a time, when
organizations were apprehensive about their data yet they could not afford the
quantity of storage space needed, HDFS brought in an easy solution at
reasonable price. The other tools offered by Hadoop like MapReduce were enjoying
a decent job. Spark came in and influenced everyone with its speed. It copies
the data into faster RAM memory right from the distributed storage system.
Spark’s in memory operations happen 100 times faster than similar Hadoop tools.
But it does not offer any distributed file storage. So Spark and Hadoop both
should work amazingly with each other- Spark for analyzing it in a flash and
HDFS for data storage.
Future of Spark →
The main feature of Spark open source
software system that appeals users is, it is cheap and affordable. With the
type of functionality and speed offered by Spark, it is just a matter of time
when the world starts looking for Spark developers. The analytics industry is
all set to experience a global shortage of many professionals within coming
couple of years. So it is always better to pre plan your career and get
enrolled in Spark big data training.
Apache Spark vs. Hadoop MapReduce →
As we know that Apache Spark is helpful in
in-memory data processing, while Hadoop MapReduce does I/O operations on the
disc after each and every map and reduces actions. It further boosts Spark’s
processing speed which can outperform Hadoop MapReduce.
It can be said that Apache Spark could
replace Hadoop MapReduce but when it comes to Spark, it requires a lot more
memory. MapReduce ends the processes once the job is accomplished, hence it can
operated with some in-disk memory. Apache Spark works well with iterative
computations when cached data is used again and again. Hadoop MapReduce
operates better with data which doesn’t fit in the memory and while other
services need to be executed. Spark is designed for instances where data
adjusts in the memory particularly on individual clusters.
Being written in Java, Hadoop MapReduce is difficult
to program whereas Apache Spark is known for its flexibility and ease of usage
APIs in languages like Scala, Python and Java. Professionals can write
user-defined functions in Spark as well and they can even add interactive mode
to run commands.
Observing its speed, flexibility and ease
of using, Spark can be accepted more widely. Chances are there that it can
replace MapReduce. But we cannot ignore the fact that there are still some
areas where MapReduce will be in demand, especially when non-iterative
computation takes place with availability of limited memory.
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