What is the Future of Hadoop? Top Trends to Watch

As far as Hadoop future is considered, data reigns supreme in 2023 and is expected to reach unimaginable heights in the coming years. Today the data analytics market has almost touched more than $103 billion and it is expected that by 2025, 463 exabytes of data will be created across the globe.

With the wider use of RPA, AI, and ML technologies, there is a focus on data freshness with real-time analysis that will lead to increased competitiveness and better-informed decisions. In such a scenario, there is a worldwide renowned concept of Hadoop, which is thriving in its current state and tends to have a significant role in the coming years.

What is Hadoop?

Apache Hadoop is a framework that manages the storage and processing of Big Data applications. It is an open-source distributed processing operation that works on clusters of technological servers. Organizations that prefer on-premise deployments cannot do without Hadoop. 

Apache Hadoop helps in processing unstructured data and large sets of data that range from Gigabytes to Petabytes and allows clusters of computers (instead of one) to analyze these massive datasets in parallel. Big Data classes will leverage big data skills to build insightful solutions and help you gain knowledge of big data quickly.

Some of the best features of Hadoop are:

• It is cost-effective and robust
• The Hadoop framework is normally written in Java, and C (only basic Native Code)
• It is a solution to big data issues like fault tolerance and scalability
• It has some of the best components under one roof. It included MapReduce (MapR), Storm, Hadoop Distributed File System (HDFS), TEZ, Yet Another Resource Manager (Yarn), and Mahaut.
• It has successfully replaced the more expensive RDBMS system and has become a critical part of Hadoop future technology.

Why do We Need Hadoop? 

Irrespective of the size or scale of any industry, sector, or business, every organization is relying on Big Data analytics to achieve exponential growth. Hadoop is needed to analyze Big Data and is used for data operations involving large-scale data. Hadoop helps in:

• Analyzing and Engaging data with large datasets
• Data processing and transformation of raw data into standardized vectors. It makes data extraction, cleanup, and acquisition much simpler.
• The Hadoop ecosystem helps, you to store Data in the Raw format using its linear scalable storage.

The Current State of Hadoop

The Hadoop market saw exceptional growth from $74.6 billion to $104.95 billion from 2022 to 2023 at a CAGR of 40.7%.

Currently, Hadoop marketing is flourishing because it has led to increased revenues in various industries. This is because of their ability to analyze larger and unstructured sets of data on clustered systems. Some of the thriving Hadoop markets today are Europe (East and West), America (North and South), Africa, the Middle East, and Asia-Pacific

Industries like IT and ITES, Media, Healthcare, BFSI, Government, Transportation, Resources, etc. are employing Hadoop in hybrid, on-premise, and cloud models

The Scope of Hadoop Future

It has been estimated that the Hadoop scope in the future and the Big Data Analytics market will grow at a CAGR of 23.9% and touch 84140 million in 2028.

It is expected to have Hadoop technology future markets like data science and data analytics to generate 11.5 million employment opportunities globally, by 2026. Some of the most thriving companies that are in high demand for Hadoop professions are OCBC Bank, Cisco, IBM, Google, Dell, Siemens, and Twitter.

Considering the high demand of Hadoop professionals across the globe, you can boost your career in Hadoop and related domains, anytime now!

Emerging Trends in Big Data

Some of the emerging trends in Big Data in 2023 are:

Making Big Data Fast and Approachable with Hadoop

Using efficient multi-source data processing, building serverless pipelines using AWS CDK and Lambda, and working on an AWS CDK project for building a real-time IoT infrastructure view project, makes big data fast and approachable using Hadoop.

Increase in the Use of Machine Learning

More than Artificial Intelligence, businesses are trending to rely more on Machine learning. Its real potential has been identified and it is all about how computers develop the ability to learn from existing data and then make realistic predictions. It is about applying algorithms, processing large amounts of data, recognizing patterns, and predicting the next step of action.

Better Security Related to Big Data

With the increase in data breaches, organizations are heavily relying on programs that enhance the security of their systems and data. There is sensitive consumer information at hand, and any data leakage can put industries in jeopardy.

Advanced Tools in Big Data

Advanced and cognitive tools that are propagated by Artificial Intelligence and Machine learning are the need of the hour. These have been a Big Data trend for quite a few years, and remain one, for the coming years.

Predictive Analysis

This is one of the most popular technological assets that industries worldwide are using. It is used in a wide range of tasks, helps in identifying consumer trends, choices, and demands, and also identifies fraud. It gives a clear picture of how consumers react or may react to a specific offer.

Hadoop's Role in the Future of Big Data 

Undoubtedly, Big Data is considering Cloud-based applications, but still, for storage and processing, several organizations and industries are using Hadoop applications. The future scope of Hadoop technology has a significant role in the future of Big Data as it is scalable, feasible, cost-effective, and resilient to failure.  

Potential Developments and Improvements to Hadoop 

The developments and improvements in Hadoop are:

1. The latest version of Hadoop supports Java version 8.0
2. The HDFS version uses erasure coding and provides fault tolerance
3. Another new feature is the YARN Timeline service which helps in the retrieval and storage of a person’s current and historical information.
4. It has introduced Task-DataNode Balancer that manages several disks and gets filled evenly.
5. Hadoop’s latest version supports more than two NameNodes

Predictions for Hadoop Future 

There is a huge demand for Hadoop Big Data Analytics Solutions, which is why several companies and industries are shifting their focus toward this developing technological sector. From 2023 to 2028, Hadoop promises immense Hadoop career scope and growth. It is recommended to go for Big Data and Hadoop training to upgrade your career in the field of big data. Some big data future predictions are:

• Hadoop market size to grow up to USD 84140 million in 2028.
• Expected CAGR growth in Hadoop is expected to be 23.9% over the period 2022-2028.
• More job opportunities and better salaries in the Hadoop job market.

Future Impact of Hadoop on Industries

Here is the Hadoop future scope in major industries, and how they are applying Hadoop in their operations:

1. Educational Industry

The education sector uses big data for digital education, deploying LMS (Learning Management System) and tracking the overall progress of the student over time.

2. Healthcare Industry

Hadoop has its importance in the Healthcare sector as it works on the unstructured format of data for example patient history, and disease case histories, and helps with the right treatment based on previous case histories.

3. Media Industry

Hadoop helps in collecting consumer data for insights and finding patterns in real-time usage. The technology helps in creating content for different target audiences.

4. Banking Industry

Hadoop helps the Finance and Banking sector in dealing with compliance trials, credit risk reporting, card frauds, and financial analytics. It also works towards security fraud, and trade visibility, and transforms customer data for better insights.

Hadoop's Current Market Position

Currently, the market share of Apache Hadoop in the Big data analytics market is 19.18%. 

The market and demand of Big Data Hadoop scope in the future are growing, and so is the job market. There is a massive increase in the pay scale and demand for Hadoop experts in the worldwide market of technology. Industries and businesses are using Hadoop as the building block, on which other services and applications are based.

Hadoop Job Predictions 

According to a trustworthy source, the demand for Hadoop data scientists will see an increase of 28% by 2025. According to Dice analysis, the average annual salary of entry-level big data Hadoop developers in the USA is $112687 and ranges between $85000 to $140000 across the USA. In India, the entry-level Hadoop developer can earn around $112,687.

Some of the most important job roles in Hadoop along with the projected salary package for each are as follows:

1. Hadoop Architect

They design the technical architecture, and application design, and deploys a proposed Hadoop solution. The average salary of a Hadoop architect is $144,062 a year.

2. Hadoop Administrator

They are responsible for keeping track of Hadoop cluster connectivity and security and setting up a Hadoop recovery, cluster, backup, and maintenance. The average salary of a Hadoop Administrator ranges between $70,000 and $125,000 a year.

3. Hadoop Tester

Their job is to troubleshoot and fix bugs in Hadoop applications. The average salary of a Hadoop Tester is $128,760 a year.

4. Hadoop Developer

Their core job is to install and configure Hadoop. The average salary of a Hadoop Developer is $112,714 a year.

5. Data Scientist

They collect and interpret data and develop data-driven solutions for business challenges. The average salary of a Data Scientist is $128515 a year.

6. Data Engineer

They design solutions, manage technical communications,  and maintain the production systems of an organization. The average salary of a Data Engineer is $132,922 a year.

7. Big Data Architect

They design the full cycle of a Hadoop solution and create a technical architecture, platform selection, and requirement analysis for a company. The average salary of a Big Data Architect ranges between $146,387 to $180,225 a year.

8. Big Data Analyst

His job is to evaluate the technical performance and evaluation of system enhancements in an organization. The average salary of a Big Data Analyst ranges between $68,600 and $87,893 a year.

Challenges Faced by Hadoop 

Some of the biggest challenges faced by Hadoop are:

1. Not for Small Files

Hadoop is best suited for large and unstructured data but not for small files. It has a high-capacity design, and on storing multiple small files, its NameNode gets overloaded. The best solution for big data Hadoop future scope is to merge small files with larger files, to prevent this issue.

2. Supports Batch Processing and Not Real-Time Data Processing

It takes a huge amount of data in input, processes it, and generates an output. This is the batch processing, which doesn’t include real-time data processing and makes the process extremely time-consuming.

3. No Delta Iteration

Hadoop is not for cyclical data flow, which means there is no chain of stages, in which the output generated from one stage becomes the input of the next stage. Hence lack of iterative processing is another challenge.

4. Lengthy Line of Code

Its line of code has a 120000 number of lines, which makes it more time-consuming to function a program and makes it vulnerable to bugs.

Competitors of Hadoop 

The biggest competitor of Hadoop is cloud-based applications. Considering aspects like design, management, monitoring, deployment, and visualization tools, some of the biggest competitors of Hadoop are:

1. Apache Storm

Another open source, which defines information sources to allow batch and distributed processing of streaming data. Its topology is similar to MapReduce and keeps running until damaged.

2. Apache Spark

It is an open-source clustered computing framework that provides an interface for programming entire clusters

3. DataTorrent RTS

It is a data processing framework built on Apache Apex that helps process big data applications in a scalable, fault-tolerant, and easily developed manner.

4. Google BigQuery

It is a low-cost data warehouse for analytics that only focuses on analyzing data for better insights using familiar SQL

5. Hydra

It is a distributed data storage and processing system, which is a part of the ML pipeline and supports data partitioning, resource sharing, distributed backups, and bulk file transfer.

The Road Ahead 

The familiarity, trust, and feasible working of Hadoop make it impossible for users to love it. Businesses today choose the hybrid version of both Hadoop and Cloud to create money-saving options. Business trends and market demands keep changing, but the reliability of the future scope of big data Hadoop is increasing. The job market is flourishing, and the increasing demand for Hadoop in various industry sectors assures a steady growth of this technology in Hadoop future. Along with this, upGrad Big Data courses will help you work on real-life projects with actual datasets and build a robust skill-set working.

Advance your career with our "Popular Software Engineering Courses." Gain in-demand skills and industry insights to excel in the ever-evolving tech world!

Advance your in-demand Software Development Skills with our top programs. Discover the right course for you below.