Introduction

Apache Hadoop has quickly carved a niche in the data science scene with its industry-leading scalability and economic advantages. An open-source tool, Hadoop is equipped to process huge volumes of data with simple programming language, in a distributed and resilient environment. The tool can store and process chunks of unstructured, semi-structured, or structured data, and also assures flexible database management. Apache Hadoop does not require any sort of specific format which makes it a breeze to use as well.
The post below sheds light on the major aspects of Apache Hadoop open source tool.

Key Features of Apache Hadoop

Here is a list of major features that make Apache Hadoop one of the most powerful big data tools:

1. Open source:

The open-source framework of Hadoop offers the user the ability to change project coding anytime, as per the requirements of a business.


2. Highly scalable:

The scalability feature allows the tool to add new nodes with less to no downtime. Hadoop provides horizontal scalability that can facilitate adding multiple nodes. Also, it supports vertical scalability that helps to boost overall hardware node capacity, resulting in better computation power.


3. Fault tolerance:

Hadoop stores all kinds of data in HDFS and the data is also replicated into blocks across the Hadoop cluster. This way, if one block fails to perform, the system assigns work to a different block automatically. It helps to maintain data continuity even if there is a mishap.


4. Wider availability:

The hardware data in Hadoop is stored in multiple copies so that the data can be accessed from more than one source.


5. Feasibility:

Hadoop can run on any hardware without the need for specialised machines. In Hadoop, the number of nodes can be increased without any sort of pre-planning or downtime.


6. Easy to use:

Hadoop makes it highly convenient to utilise processing and storage capacity in the cluster servers.


7. Data reliability:

Given Hadoop’s data replication facility, data is always secured and available, even if the machine fails to perform.


8. Faster data processing:

In Hadoop, the data is stored in a distributed format and hence allows data processing across the node clusters. This makes the whole process faster across the Hadoop framework.

Modules in Apache Hadoop

1. Yarn:

A resource navigator, Yarn is mainly used to manage clusters and job scheduling.


2. MapReduce:

The framework facilitates Java programs in parallel communication by taking the input data and converting it. The converted data set can be calculated to get the key value pair.


3. HDFS:

Hadoop Distributed File System signifies that the file is segregated into blocks and the data is stored in nodes across a distributed architecture.


4. Hadoop Common:

This includes Java libraries which are used for starting Hadoop and are also used by the other Hadoop modules.

How does Apache Hadoop work?

The tool works by distributing data across a cluster, processing it in parallel using the MapReduce paradigm. Simultaneously, Hadoop manages resources through the HDFS and YARN components. This allows for the efficient storage and processing of large-scale data across a distributed environment. Also Apache Hadoop offers quick response time for the queries that are connected to business systems.

Advantages of using Apache Hadoop

1. Ability to work with huge datasets

This is certainly one of the major benefits of using Apache Hadoop. The tool is particularly developed to work with huge volumes of datasets.


2. Scalability

Hadoop, unlike traditional data management systems that struggle to work with big-volume data, is highly scalable. The tool can store as well as distribute huge data sets across a large number of servers.


3. Flexibility

This is another USP that keeps Apache Hadoop ahead of conventional database management systems. The tool stands out with easy flexibility that allows it to store data in multiple formats- including unstructured or semi-structured data.


4. Schema-independence

Hadoop features an advanced Schema-independent architecture that allows it to work with different data types and formats.


5. Low latency & high throughput

Hadoop follows distributed as well as parallel processing that ensures simultaneous processing on all data blocks together. Then, it’s the code that is sent to data in cluster in place of moving the data itself. These two advantages benefit Hadoop users with low latency and high throughput.


6. Compatibility with multiple languages

Despite its Java base, Hadoop offers support for a list of other languages such as Perl, Ruby, Python, etc.


7. Economical

Hadoop is a cost-effective investment. The cutting-edge tool doesn’t need much more than regular commodity hardware that keeps the hardware costs in check. Also, the overall cost of Hadoop data management is way less than conventional ETL systems.


8. Abstraction

Hadoop makes things more convenient for developers by providing Abstraction at different levels. The system breaks a big file into equal chunks which are then distributed across various locations within one single cluster.


9. Supports diverse file systems

Hadoop supports a long list of file systems such as Avro, XML, JSON, Parquet, and so on.

Popular competitors of Apache Hadoop

With its growing significance and importance, Hadoop has emerged to be one of the most widely used and popular tools in data science. Needless to mention other competitors of Hadoop are also of great significance:

• Apache Spark
• Apache Storm
• Ceph
• DataTorrent RTS
• Disco
• Google BigQuery
• High-Performance Computing Cluster (HPCC)
• Hydra
• Pachyderm
• Presto

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Conclusion

It can be summarized that Apache Hadoop is one of the most trusted and widely used open-source tools in data science. It’s cutting-edge fault tolerance feature, coupled with flexibility, and ease of use, make it a favourite among data scientists and developers.
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