This is a comparison presentation between two popular big data frameworks, Hadoop and Spark. Here you will get detailed information about their pros and cons, alongside getting familiar with different factors to consider during ‘Spark vs Hadoop’ battle.

1. Which Big Data framework to choose?
Spark vs Hadoop:

2. Choosing between Spark and
Hadoop big data frameworks
can be a tough task for an
Entrepreneur or developer.
But not when you consider
these factors for comparison.

3. Architecture
Hadoop, unlike Spark, has two prime
elements YARN and HDFS to manage
different data storage and resource
allocation processes. This makes
Hadoop’s architecture capable of
delivering better solutions that that of Spark.
Hadoop Spark
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4. Ease of Use
In comparison to Apache Hadoop,
Spark offers more user-friendly APIs
and interactive mode facilities to
mobile app development companies.
This eases the process of learning
and using Spark.
Hadoop Spark
10
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5. Data Processing
While Hadoop offer only batch
processing, Spark avails the
opportunity to perform different
types of data processing, including
interactive, graph, iterative, and
batch processing.
Hadoop Spark
10

6. Hadoop Spark
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Fault Tolerance
In case of Spark, one has to begin
from scratch if a process fails in
between. But, in the case of Hadoop,
one can continue from the point of crash.
Something that proves that Hadoop
offers higher levels of fault tolerance.

7. Hadoop Spark
1 1
Compatibility
When it comes to compatibility, both
Hadoop and Spark big data frameworks
provides higher grades of compatibility.
They act as standalone applications, as
well as, work together by supporting
each others' data sources and file formats.

8. Hadoop Spark
10
Performance
Spark, when compared to Hadoop,
runs 10 times faster on disk and 100
times in-memory. Also, it is able to
manage 100TB of data in 3 times
faster than Apache Hadoop.

9. Hadoop Spark
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Security
Unlike Spark, Hadoop encourages Kerberos
authentication, third-party authentication,
conventional file permissions, and access
control lists. Something that makes it easier
for Hadoop big data framework to deliver
higher security.

10. Hadoop Spark
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Cost-Effectiveness
When compared to Hadoop, Apache
Spark requires more memory on disk
and has less developers in the market.
This makes mobile application development
using Big data framework expensive.

11. Hadoop Spark
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Future Possibilities
Though both Hadoop and Spark are
used by big companies, the former is
expected to grow with a CAGR of 65.6%
in between 2018 and 2025, when
compared to Spark that will flourish
with a CAGR of 33.9% only.

12. For a detailed information about this topic,
please refer to this blog:
Spark vs Hadoop: Which Big Data
Framework Will Elevate Your Business?https://appinventiv.com/blog/spark-vs-hadoop-big-data-frameworks/

13. Contact Us
Appinventiv Technologies
sales@appinventiv.com
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