What is Hadoop?

Hadoop is one of the widely used Apache-based frameworks for big data analysis. It allows distributed processing of large data set over the computer clusters. Its scalable feature leverages the power of one to thousands of system for computing and storage purpose. A complete Hadoop framework comprised of various modules such as:

• Hadoop Yet Another Resource Negotiator (YARN

• MapReduce (Distributed processing engine)

• Hadoop Distributed File System (HDFS)

• Hadoop Common

These four modules lie in the heart of the core Hadoop framework. There are many more modules available over the internet driving the soul of Hadoop such as Pig, Apache Hive, Flume etc.

The history of Hadoop is quietly impressive as it was designed to crawl billions of available web pages to fetch data and store it in the database. And the outcome was Hadoop Distributed File System and MapReduce. If you are unaware of this incredible technology you can learn Big Data Hadoop from various relevant sources available over the internet. You can go through the blogs, tutorials, videos, infographics, online courses etc., to explore this beautiful art of fetching valuable insights from the millions of unstructured data.

Currently, it is getting used by the organizations having a large unstructured data emerging from various sources which become challenging to distinguish for further use due to its complexity. And the only solution is Hadoop which saves extra time and effort.

What is Spark?

 The Apache Spark is an open source distributed framework which quickly processes the large data sets. You must be thinking it has also got the same definition as Hadoop- but do remember one thing- Spark is hundred times faster than Hadoop MapReduce in data processing.

It was originally developed in the University of California and later donated to the Apache. With implicit data parallelism for batch processing and fault tolerance allows developers to program the whole cluster.

Spark is specialized in dealing with the machine learning algorithms, workload streaming and queries resolution. Also, the real-time data processing in spark makes most of the organizations to adopt this technology. It has its own running page which can also run over Hadoop Clusters with Yarn.

A few people believe that one fine day Spark will eliminate the use of Hadoop from the organizations with its quick accessibility and processing. Spark doesn't owe any distributed file system, it leverages the Hadoop Distributed File System.

Compatibility Factor:

Spark and Hadoop they both are compatible with each other. Talking about the Spark it has JDBC and ODBC drivers for passing the MapReduce supported documents or other sources.

Performance Test:

It doesn’t require any written proof that Spark is faster than Hadoop. The main reason behind this fast work is processing over memory. Spark can process over memory as well as the disks which in MapReduce is only limited to the disks.

Due to in-memory processing, Spark can offer real-time analytics from the collected data. This is very beneficial for the industries dealing with the data collected from ML, IoT devices, security services, social media, marketing or websites which in MapReduce is limited to batch processing collecting regular data from the sites or other sources.

Scalability:

Both Hadoop and Spark are scalable through Hadoop distributed file system. But the main issues is how much it can scale these clusters? Suppose if the requirement increased so are the resources and the cluster size making it complex to manage.

Security:

The HDFS comprised of various security levels such as:  

• the access control list (ACL),

• Permission model

• Service level authorization

• Kerberos authentication

• Active directory Kerberos

• Data encryption

• LDA

These resources control and monitor the tasks submission and provide the right permission to the right user. You can also implement third-party services to manage your work in an effective way.

Talking about the Spark, it allows shared secret password and authentication to protect your data. As it supports HDFS, it can also leverage those services such as ACL and document permissions.

Costs:

Apache has launched both the frameworks for free which can be accessed from its official website. You will only pay for the resources such as computing hardware you are using to execute these frameworks.

Both of these frameworks lie under the white box system as they require low cost and run on commodity hardware. Hadoop requires very less amount for processing as it works on a disk-based system. In order to enhance its speed, you need to buy fast disks for running Hadoop.

At the same time, Spark demands the large memory set for execution. It also supports disk processing.  But, in contrast with Hadoop, it is more costly as RAMs are more expensive than disk.

Thus, we can see both the frameworks are driving the growth of modern infrastructure providing support to smaller to large organizations. Currently, we are using these technologies from healthcare to big manufacturing industries for accomplishing critical works. It is still not clear, who will win this big data and analytics race..!!