Hadoop and Spark are the two most popular big data technologies used for solving significant big data challenges. In this video, you will learn which of them is faster based on performance. You will know how expensive they are and which among them is fault-tolerant. You will get an idea about how Hadoop and Spark process data, and how easy they are for usage. You will look at the different languages they support and what's their scalability. Finally, you will understand their security features, which of them has the edge over machine learning. Now, let's get started with learning Hadoop vs. Spark. We will differentiate based on the below categories 1. Performance 2. Cost 3. Fault Tolerance 4. Data Processing 5. Ease of Use 6. Language Support 7. Scalability 8. Security 9. Machine Learning 10. Scheduler What is this Big Data Hadoop training course about? The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. What are the course objectives? This course will enable you to: 1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training

1. What’s in it for you?
Performance
Cost
Fault tolerance
Data Processing
Ease of Use
1
2
3
4
5
Scalability
Security
Machine Learning
Language Support6
7
8
9
We will compare Hadoop, and Spark based on the following categories:
VS

2. Performance
Cost
Fault tolerance
Data Processing
Ease of Use
Scalability
Security
Machine Learning
Language Support
Comparison based on below criteria
Scheduler

3. Hadoop is generally slow as it performs
operations on the disk and cannot deliver
near real-time analytics from the data
No real-time
analytics
Performance

4. What’s in it for you?
Performance
Cost
Fault tolerance
Data Processing
Ease of Use
1
2
3
4
5
Scalability
Security
Machine Learning
Language Support6
7
8
9
We will compare Hadoop, and Spark based on the following categories:
VS
Click here to watch the video

5. Hadoop is generally slow as it performs
operations on the disk and cannot deliver
near real-time analytics from the data
Spark runs 100 times faster in-memory,
and 10 times faster on disk. If Spark
runs on YARN with other resources
demanding services, there could be major
degradation
No real-time
analytics
Faster in-memory
processing
Performance

6. Hadoop is less expensive as it is an open-
source software. It requires more memory
on disk which is relatively an inexpensive
commodity
Cost

7. Hadoop is less expensive as it is an open-
source software. It requires more memory
on disk which is relatively an inexpensive
commodity
Spark is open-source but requires a lot of
RAM to run in-memory. This increases the
cluster size and its cost
Cost

8. Hadoop is highly fault-tolerant because it
was designed to replicate data across
many nodes. Each file is split into blocks
and replicated numerous times across many
machines
Fault Tolerance

9. Hadoop is highly fault-tolerant because it
was designed to replicate data across
many nodes. Each file is split into blocks
and replicated numerous times across many
machines
Spark uses Resilient Distributed Datasets
(RDDs), which are fault-tolerant collections
of elements that can be operated on in
parallel
Fault Tolerance

10. Hadoop processes data in batches.
MapReduce operates in sequential steps
by reading data from the cluster, performing
its operations on the data, writing the results
back to the cluster
Output data
Data Processing
Batches of
input data

11. Hadoop processes data in batches.
MapReduce operates in sequential steps
by reading data from the cluster, performing
its operations on the data, writing the results
back to the cluster
Sparks performs batch, real-time, and
graph processing of data. It reads data
from the cluster, performs its operation on
the data, and then writes it back to the
cluster
Output data
Data Processing
Batches of
input data
Batch
Real-time
Graph

12. Hadoop’s MapReduce has no interactive
mode and is complex. It needs to handle
low-level APIs to process the data, which
requires lots of coding
Ease of Use

13. Hadoop’s MapReduce has no interactive
mode and is complex. It needs to handle
low-level APIs to process the data, which
requires lots of coding
Spark supports user-friendly APIs for
different languages. It has an interactive
mode and provides intermediate feedback
for queries and actions
Ease of Use

14. Hadoop framework is developed in Java
programming language. While, MapReduce
applications can be written in Python, R
and C++
MapReduce supports
programming
languages
Language Support

15. Hadoop framework is developed in Java
programming language. While, MapReduce
applications can be written in Python, R
and C++
Apache Spark is developed in Scala
language and supports other programming
languages like Python, R, and Java
MapReduce supports
programming
languages
Spark supports other
programming
languages
Language Support

16. Hadoop is highly scalable as we can add n
number of nodes in the cluster. Yahoo
reportedly used a 42,000 node Hadoop
cluster
Scalability

17. Hadoop is highly scalable as we can add n
number of nodes in the cluster. Yahoo
reportedly used a 42,000 node Hadoop
cluster
The largest known Spark cluster has 8,000
nodes. But as big data grows, it’s expected
that cluster sizes will increase to maintain
throughput expectations.
Scalability

18. Hadoop supports Kerberos and LDAP for
authentication. It also supports access
control lists (ACLs) and a traditional file
permissions model
Security

19. Hadoop supports Kerberos and LDAP for
authentication. It also supports access
control lists (ACLs) and a traditional file
permissions model
Spark’s security is a bit sparse as it
supports authentication via passwords. If
you run Spark on HDFS, it can use HDFS
ACLs and file-level permissions.
Additionally, Spark can run on YARN, giving
it the capability of using Kerberos
authentication.
Security

20. Hadoop uses Mahout for processing data
and building models. Also, Samsara, a
Scala-backed DSL language can be used
for in-memory algebraic operations and
allows users to write their own algorithms
Machine Learning

21. Hadoop uses Mahout for processing data
and building models. Also, Samsara, a
Scala-backed DSL language can be used
for in-memory algebraic operations and
allows users to write their own algorithms
Spark has a built-in machine learning
library that can be used for classification,
and regression. It can also build machine-
learning pipelines with hyperparameter
tuning
MLlib
Machine Learning

22. Hadoop MapReduce is dependent on an
external scheduler
Scheduler

23. Hadoop MapReduce is dependent on an
external scheduler
Apache Spark has its own scheduler
Scheduler