1. Big Data Analytics: A
Comparative
Evaluation of Apache
Hadoop and Apache
Spark
In today's data-driven world, businesses must make sense of vast and
diverse data sets to gain valuable insights. Apache Hadoop and Apache
Spark are two powerful big data processing platforms that businesses can
use to tame their data, but which one is right for you? In this presentation,
we'll provide a comparative analysis of Hadoop and Spark to help you
make an informed decision.
by Sukhpreet Singh
2. Introduction to Big Data Analytics
Big Data Analytics refers to analyzing large, complex datasets to extract valuable insights, which can
help businesses make informed decisions. Factors like data size, complexity, and velocity are the key
challenges in big data analytics.
Technology
Big Data Analytics relies on a
wide range of technologies like
Hadoop, Spark, NoSQL
databases, Data Warehousing,
and Machine Learning to
handle massive quantities of
data and uncover insights.
Machine Learning
Algorithms
Machine Learning algorithms
play a critical role in Big Data
Analytics, enabling data
scientists to uncover patterns,
relationships, and other
insights in large datasets that
are difficult for humans to
detect manually.
Cloud Computing
Cloud computing provides an
efficient and cost-effective way
to perform Big Data Analytics.
Instead of investing in costly
hardware infrastructure and
software systems, businesses
can leverage cloud computing
services to set up analytics
platforms within minutes.
3. The Importance of Big Data Analytics
in Business
Data-Driven Decisions
Analytics provides business leaders with
valuable insights, empowering them to make
data-driven decisions that drive growth and
improve efficiency.
Competitive Advantage
Companies that use analytics gain a
competitive edge by unlocking hidden
patterns and trends, enabling them to make
smarter choices, reduce costs and boost
profitability.
4. Overview of Apache Hadoop
Features and Capabilities
Hadoop is an open-source framework leveraging
a network of computers and distributed data
storage to process big data in parallel. It is highly
fault-tolerant, scalable and adaptable, making it
an excellent choice for large-scale data
processing.
Advantages and Disadvantages
Hadoop’s large community means that it offers
many tools. However, it's complex to set up and
maintain, and requires more dedicated resources
than other options. It’s best for deeper analysis of
huge, very diverse data sets.
5. Overview of Apache Hadoop
Apache Hadoop is an open-source software framework used for storing and processing large datasets.
Hadoop consists of two main components - Hadoop Distributed File System (HDFS) and MapReduce. It
enables distributed processing of large datasets across clusters of commodity computers.
1
Hadoop Distributed File System (HDFS)
A distributed file system that provides high-
throughput access to application data. HDFS
is designed to handle large files and
streaming data. It works on the principle of
data locality, which means that computation
is performed on the same node where data is
stored.
2
MapReduce
A programming model used for processing
large datasets. MapReduce breaks down a
task into smaller sub-tasks and performs
them in parallel on different nodes of a
cluster. It provides automatic fault-tolerance
and scalability.
3
Hadoop Ecosystem
Hadoop has a vast ecosystem of related
tools, including Hive, Pig, HBase, Sqoop,
Flume, Hue, and more. They provide user-
friendly interfaces and enable various data
processing capabilities, like data
warehousing, data querying, and real-time
processing.
6. Overview of Apache Spark
Apache Spark is an open-source software framework used for large-scale data processing. It is an in-
memory data processing engine that enables fast processing of data and real-time analytics. Spark is
designed to work with various data sources, including Hadoop Distributed File System (HDFS), HBase,
Cassandra, and Amazon S3.
1 Resilient Distributed
Datasets (RDD)
An RDD is a fundamental data
structure in Spark, used for in-memory
data processing. RDDs are
partitioned, immutable, and fault-
tolerant. RDDs enable distributed
execution of parallel operations on
large datasets.
2
DataFrames and Datasets
DataFrames are distributed collections
of data organized into named
columns, similar to tables in a
relational database. Datasets maintain
strong typing information of their
contents.
3 Spark Ecosystem
Spark has a vast ecosystem of related
tools, including Spark SQL, Spark
Streaming, MLlib, GraphX, and more.
They provide high-level abstractions
and enable various data processing
capabilities such as SQL queries,
machine learning training, graph
processing.
7. Strengths and Limitations of Apache Spark
1 Strengths
Apache Spark is faster and more efficient
than Apache Hadoop. Spark can perform
processing in-memory, whereas Hadoop
requires data to be written and read from
disk. Spark also supports real-time data
processing and data streaming.
2 Limitations
Apache Spark requires skilled resources
to maintain and operate. Spark may also
have higher upfront infrastructure costs
than Hadoop as it requires more memory
resources.
Cluster Computing
Spark is designed to work with
various data sources,
including Hadoop Distributed
File System (HDFS), HBase,
Cassandra, and Amazon S3.
Real-time Processing
Spark Streaming enables real-
time processing of data, which
is essential for applications like
fraud detection, predictive
modeling, and real-time
recommendations.
Data Processing
Abstractions
Spark SQL provides a robust
set of abstractions for
processing structured and
semi-structured data. It
includes support for SQL
queries, DataFrames, and
Datasets.
8. Comparative Evaluation of Apache
Hadoop and Apache Spark
Apache Hadoop
• Reliable and mature platform for storing
and processing large datasets
• Scalable and fault-tolerant due to the
distributed architecture
• Not suitable for low-latency processing
and real-time analytics
• Extensive ecosystem of related tools
Apache Spark
• Faster and more efficient than Hadoop
due to in-memory processing
• Supports real-time data processing and
streaming
• Higher upfront infrastructure costs than
Hadoop
• Require skilled resources to maintain
and operate
Selecting a Big Data Analytics tool depends on various factors like data size, complexity, and
processing requirements. Apache Hadoop and Apache Spark are two of the most popular Big Data
Analytics tools available, each with its own strengths and weaknesses. Choosing the right tool for the
job is an essential decision that businesses must make based on their specific requirements and use
cases.
9. Comparison between Hadoop and Spark
1
Speed
Spark is generally faster than
Hadoop, especially for iterative
processing and real-time stream
processing.
2
Scalability
Both platforms are highly scalable,
but Spark tends to be more
efficient due to its in-memory
processing capabilities.
3
Usability
Hadoop can be more complex to
set up and use, while Spark has a
simpler and more user-friendly
API.
4
Applications
Both platforms can be used for a
wide range of Big Data processing
applications, but Spark is better
suited for certain types of
processing, such as machine
learning and real-time stream
processing.
10. Use Cases
Apache Hadoop
• Large data sets
• Data processing and analysis
• Data storage for distributed computing
platforms
Apache Spark
• Real-time processing
• Machine learning and AI applications
• Stream processing of high volume data feeds
11. Conclusion
1 Cost
Both platforms are open-source
and free to use, but Hadoop
requires more hardware and
administrational support. Spark
works out-of-the-box, meaning it’s
easier to operate for small
datasets.
2 Compatibility
A key advantage of Apache Spark
is that it can work independently
or sit on top of Hadoop, making it
a great choice for businesses that
already use Hadoop and want to
build on what's already in place.
Alternatively, Spark can be used
without Hadoop
3 Impact
Selecting Hadoop or Spark depends on your business's specific needs. While
both platforms have their advantages and disadvantages, the best way to make
the right choice is to consider use case scenarios, budgetary restrictions, and
project goals.
12. Conclusion and Recommendations
Both Apache Hadoop and Apache Spark are powerful tools used for big data analytics. Hadoop provides
a reliable and scalable platform for processing and storing large datasets, whereas Spark offers faster
and more efficient in-memory processing capabilities and supports real-time streaming.
Business Growth
Big data analytics provides
businesses with valuable
insights for better decision-
making, improving customer
experience, and driving
growth.
Machine Learning
Machine Learning is one of the
most significant applications of
Big Data Analytics, with vast
potential for enabling predictive
modeling, personalized
recommendations, and other
use cases.
Integration with
Business Processes
To maximize the impact of Big
Data Analytics, businesses
must integrate analytics
capabilities into their existing
business processes,
determining how data insights
can be used to drive strategic
decisions.
13. Final Conclusion
Which is better?
There is no clear answer to this question, as it
largely depends on your specific use case and
requirements.
Final Thoughts
Both Apache Hadoop and Apache Spark are
powerful Big Data processing platforms that can
help organizations gain valuable insights from
their data.
