Learning Hadoop For Big Data Enthusiasts

Posted 2022-10-26 12:05:47

592

Before knowing Hadoop, Let’s understand what Big data is.

Datasets that are too huge and complicated for conventional systems to store and handle are called big data. Big Data's main issues are categorized into three Vs. Volume, velocity, and diversity are what they are.

Why was Hadoop invented?

Let's talk about the drawbacks of the conventional strategy that prompted the development of Hadoop.

Storage for Large Datasets

The typical RDBMS cannot store huge volumes of data. In the existing RDBMS, data storage is costly because hardware and software are expensive.

Handling data in different formats

The RDBMS has the ability to store and work with structured data. However, we must deal with organized, unstructured, and semi-structured data in the actual world.

Data is getting generated at high speed.

Tera to petabytes of data is being produced every day. Therefore, we require a system that can quickly handle data in real time. Traditional RDBMS fall short in their ability to deliver real-time processing at high rates.

What is Hadoop?

The answer to the aforementioned Big Data issues is Hadoop. It is the technique for distributed storage of large datasets on a cluster of affordable devices. Furthermore, it offers Big Data analytics using a distributed computing infrastructure.

It was created as an open-source project by the Apache Software Foundation, and developed by Doug Cutting, Hadoop. Yahoo donated Hadoop to the Apache Software Foundation in the year 2008. Since then, there have been two versions of Hadoop. Versions 1.0 and 2.0.6 were released in 2011 and 2013, respectively. There are several types of Hadoop, including Cloudera, IBM BigInsight, MapR, and Hortonworks.

Conditions for Learning Hadoop

Knowledge of certain fundamental Linux commands

Ubuntu is the preferred Linux operating system for Hadoop setup. Therefore, one has to be familiar with some fundamental Linux commands. These commands may be used to upload files to HDFS, get files from HDFS, and more.

Fundamental Java concepts

People who desire to study Hadoop can start using it while also learning the fundamentals of Java. Other languages can also be used to create maps and reduce functions in Hadoop. These include C, Ruby, Perl, Python, and more. The streaming API makes this feasible. It allows for both reading from and writing to standard input and output. Additionally, Hadoop offers high-level abstraction tools like Pig and Hive that don't require Java expertise.

You can master Hadoop with the best data science and data analytics course in Bangalore available online with IBM certification.

Core Hadoop Components

HDFS

Acronym for Hadoop Hadoop can store data distributedly thanks to the distributed file system. A master-slave topology governs HDFS.

Slaves are low-cost computers, whereas the master is an expensive system. A number of blocks are created from the Big Data files. On the cluster of slave nodes, Hadoop distributes the storage of these blocks. We have metadata kept on the master.

Two daemons are now active for HDFS. As follows:

NameNode: NameNode carries out the following operations:

On the master system, NameNode Daemon is active.
It is in charge of controlling, observing, and maintaining DataNodes.
It keeps track of information about the files, such as block locations, file sizes, permissions, hierarchies, etc.
Namenode records all metadata changes in edit logs, including file creation, deletion, and renaming.
The DataNodes send heartbeat and block reports to it often.

DataNode: The following are some of its many features:

Running on the slave computer is DataNode.
The real business data is kept there.
It fulfills the user's read-write request.
In response to an instruction from NameNode, DataNode sets up the blocks by generating, replicating, and deleting them.
By default, it transmits a pulse to NameNode every three seconds informing it that HDFS is healthy.

2. MapReduce

It is the Hadoop data processing layer. It uses two stages to process data.

They include:

Map Step: The data is subjected to business logic during this phase. Key-value pairs are created from the supplied data.

Reduce Phase: The Map phase's result is the input for the Reduce phase. Based on the key of the key-value pairs, aggregation is applied.

The method that Map-Reduce operates is as follows:

For the Map function's input file, the client provides the file. Into tuples, it is divided.
Key and value are defined by the map function using the input file. This key-value pair is what the map function returns as its result.
The map function's key-value pair is sorted using the MapReduce framework.
The framework combines all tuples with the same key.
These combined key-value pairs are supplied as input to the reducers.
On a key-value pair, the reducer applies aggregate functions.
The producer's output is written to HDFS.

3. YARN

There are the following parts to Yet Another Resource Locator:-

Resource Manager: A description of how it operates

On the master node, Resource Manager is put to use.
It is aware of where slaves are located (Rack Awareness).
It is aware of how much each slave's resources are.
One of the crucial services provided by the Resource Manager is the Resource Scheduler.
The resource scheduler decides how the resources are allocated to different activities.
Resource Manager also manages the service known as Application Manager.
The Application Manager negotiates the initial container for an application.
The Resource Manager monitors the heartbeats of the Node Manager.

Manager of Nodes

On slave machines, it functions.
Containers are managed by it. Containers only make up a small portion of Node Manager's resource capacity, and Node Manager keeps track of how much each resource is used.
It notifies the Resource Manager of its pulse.

Future of Hadoop

There will be significant investment in the big data sector in the upcoming years. 90% of worldwide firms will invest in Big Data technologies, according to a FORBES analysis. Thus, there will be an increase in demand for Hadoop resources. Your professional advancement will be boosted if you learn Apache Hadoop. Additionally, it often results in a wage raise.

Conclusion

After finishing this session, we can state that Apache Hadoop is the most well-known and effective big data technology. Big Data analyses massive amounts of data concurrently on a cluster of nodes while storing it in a dispersed fashion. It offers HDFS, the most dependable storage layer in the world. MapReduce, a batch processing engine, and YARN, a resource management layer. If you want to master Hadoop for data science, visit the IBM-accredited data science course in Bangalore and become an expert data scientist today!