Hadoop and Spark are 2 of the most prominant platforms for big data storage and analysis. Here are some essentials of Hadoop vs Apache Spark.

1. Hadoop Vs Apache Spark

2. Hadoop Introduction
● Hadoop helps in storing large data sets. It also helps in running processes related to distributed
analytics. Hadoop is a framework that is open source and can be freely used. Large data sets
can be quickly and easily stored using Hadoop. Hadoop is an efficient framework – it does not
require large amounts of data transfer.
● Hadoop makes sure that one job is processed at a time. Data warehousing is one of the core
functions of Hadoop. The framework ensures that big data applications continue to run in case of
a failures of individual servers.
● Hadoop is a framework that is highly prefered for batch processing. The Hadoop framework is
written in Java . Developers also use Hive on Top of Hadoop for adding SQL compatibility.
● Hadoop can be used without any programming, because there are numerous integration services
available out there.

3. Hadoop Advantages

4. Scalability
● One of the key advantages of developing with hadoop is scalability. Since large data sets can
be easily stored and distributed, it is highly scalable.
● A large number of nodes are made possible by Hadoop, ensuring large amounts of data storage
and distribution. In comparison to traditional RDMS, Hadoop is highly scalable.

5. Cost Effective
● The big data requirements of today are humongous and these requirements can be fulfilled in a
cost effective manner using Hadoop. The cost of data processing is much higher when it
comes to traditional database management systems.
● The simplified processing of complex data ensures that Hadoop is a cost effective framework.

6. Flexible Solution
● Operating on different types of data and having access to different types of data is possible with
Hadoop and this makes it a very flexible solution. This helps in generating value from all sorts of
data that is gathered.
● One could use a variety of data sources like social media and email etc. to gather as much
useful data as possible.

7. Speed
● Since there is a distributed system of files in Hadoop. The processing servers and storage
servers are the same, making the process extremely fast.
● The processing of data is highly efficient using the Hadoop framework.

8. Reliable
● The higher level of tolerance to faults, is found only in Hadoop. Data replication in different
nodes ensures that a clear backup is available.
● This minimizes the chances of data failure. Hadoop is quite a reliable framework and helps in
avoiding both single and multiple failures.

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10. Spark Introduction● Spark, is a tool that works on processing the data that has been distributed, using the Hadoop
framework. The Spark platform has be designed run on top of Hadoop. It works as an
alternative the batch model. It can used for hastening interactive queries and processing real
time data. Spark does not have its own file management system, but integrated with one.
● Spark is quite faster than hadoop when it comes to processing of data. Spark is different from
Hadoop because it ensures complete data analytics of real time as well as stored data. Spark
does not have the distributed storage system which is an essential for big data projects. Spark
is also known for its advanced data processing and machine learning.

11. Spark Advantages

12. Faster
● Spark places the data into Resilient Distributed Datasets. This data gets stored in the memory
making it easily accessible.
● Since the data is easily accessed from the memory, the MapReduce jobs can be undertaken
very quickly.

13. Real Time Processing
● There is a continuous growth of real time data. Processing large quantities of a real time data can
be a big challenge.
● This can help in processing of logs for live streaming sites and also help in fraud detection and
electronic trading data.

14. Using Big Data Effectively
● Big data needs to be used effectively to reach the right set of people with the right messaging. Big
data makes use of very specific audiences to bring out the best conversion rate for a retail business.
Many retail marketers fail to bring out the right results for the business because of lack of
understanding of how to make the data usable and how to analyse it.
● Technology has to be fully prepared and used for big data usage and integration.

15. Processing of Graphs
● Graph processing helps in capturing the relationship between data and entities.
● The process helps in analysing social as well as advertising data. Machine learning helps in
carrying out advanced analytics and getting consumer understanding.

16. Power
● Most companies need 2 systems – one for storing and streaming data and the other for
analyzing the data.
● Spark helps in simplified application development, maintenance and deployment.

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