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Developers
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Hire the Top 1% of Hadoop Developers

Emilia M.

Front-end Developer
Pacific Timezone

Experience

8 years

AVAILABILITY

Full-time

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Emilia M.

Ana R.

Back-end Developer
Central Timezone

Experience

10 years

AVAILABILITY

Full-time

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Ana R.

Amanda H.

Front-end Developer
Mountain Timezone

Experience

5 years

AVAILABILITY

Full-time

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Amanda H.

Alonso R.

Fullstack Developer
Mountain Timezone

Experience

8 years

AVAILABILITY

Full-time

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Alonso R.

Mónica T.

Front-end Developer
Eastern Timezone + 1

Experience

6 years

AVAILABILITY

Full-time

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Mónica T.

Manuel M.

Data Developer
Mountain Timezone

Experience

8 years

AVAILABILITY

Full-time

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Manuel M.

Carlos R.

Fullstack Developer
Central Timezone

Experience

6 years

AVAILABILITY

Full-time

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Carlos R.

Maria T.

Back-end Developer
Eastern Timezone

Experience

5 years

AVAILABILITY

Full-time

Hire
Maria T.

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Access Revelo's talent pool of Hadoop Developers with technical expertise across Libraries, APIs, Platforms, Frameworks, and Databases

Libraries

Frameworks

Facebook API | Instagram API | YouTube API | Spotify API | Apple Music API | Google API | Jira REST API | GitHub API | SoundCloud API

APIs

Amazon Web Services (AWS) | Google Cloud Platform (GCP) | Linux | Docker | Heroku | Firebase | Digital Ocean | Oracle | Kubernetes | Dapr | Azure | AWS Lambda | Redux

Platforms

Databases

MongoDB | PostgreSQL | MySQL | Redis | SQLite | MariaDB | Microsoft SQL Server

Tips for Hiring Hadoop Developers

Hadoop developers are specialists in the Apache Hadoop framework, an open-source software solution specifically designed to manage and analyze vast amounts of big data. This powerful platform is constructed to handle a tidal wave of information, efficiently processing and storing it. 

However, when looking to hire these unique professionals, the real challenge surfaces — pinpointing a developer who not only possesses the technical mastery over this tough-to-tame beast but also has the creative thinking and problem-solving prowess needed to transform raw data into meaningful insights.

What Is Hadoop Development?

Hadoop is a robust open-source framework capable of managing and processing vast amounts of data. Fields that require big data analytics — such as healthcare, retail, finance, and advertising — use Hadoop extensively. For instance, a startup retail company could employ Hadoop to analyze customer behavior data to optimize product recommendations, while a healthcare organization might use it to process large datasets for medical research.

Hadoop development goes beyond just handling oceans of data; it makes that data work for you. The platform consists of various modules and components, including Hadoop Distributed File System (HDFS) for data storage and MapReduce for parallel data processing. It's these components that help in breaking down the data silos, making it possible to conduct comprehensive analysis on diversified datasets.

But what sets Hadoop development apart is its scalability and resilience. Hadoop’s architecture allows businesses to add more nodes as their data grows, ensuring cost-effective scalability. When a node failure occurs, the system continues processing by automatically rerouting tasks to other nodes. It's this combination of extensive analysis capability with scalable and robust architecture that makes Hadoop an invaluable asset for any business swimming in a sea of big data.

Why Hadoop?

The ability to rapidly process and analyze large data volumes can give businesses a competitive, data-driven edge. Hadoop excels at handling big data by distributing processing across multiple nodes, enabling faster insights and decision-making.

You can leverage Hadoop's power to understand customer trends, optimize operations, or even develop new products based on data-derived insights. Simply collecting data is not enough, so Hadoop helps you make that data work for your business.

Benefits of Hadoop

Hadoop offers numerous benefits that make it a top choice for businesses aiming to harness the power of big data. Here are some key advantages.

Scalability

Unlike traditional databases, Hadoop can store and distribute large data sets across hundreds of inexpensive servers. As your data grows, you can easily expand your system by adding more machines.

Cost-Effective

Hadoop provides a cost-effective storage solution for businesses' exploding data sets. It allows storing large volumes of data without the need for expensive, proprietary hardware.

Flexibility

With Hadoop, you're not confined to one type of data. You can store and process text, images, videos, or basically any kind of data. That leads to remarkable flexibility when collating and analyzing information.

Speed

When dealing with vast amounts of data, speed is paramount. Hadoop's distributed computing model quickly processes big data. The more computing nodes you use, the more processing power you have.

Resilience

Data loss is a significant concern in big data analytics, but Hadoop's fault tolerance feature protects data against hardware failure. If a node goes down, Hadoop automatically redirects jobs to other nodes, ensuring that processing continues.

What Does a Hadoop Developer Do?

A Hadoop developer's responsibilities revolve around designing, building, and maintaining systems and solutions leveraging the big data capabilities of the Hadoop ecosystem. They're responsible for transforming complex data into business insights that can drive decision-making.

Some tasks they commonly handle include the following:

  • Designing and implementing scalable Hadoop-based data processing infrastructure
  • Developing complex MapReduce programs to perform data analysis
  • Working closely with data scientists to implement machine learning algorithms
  • Ensuring high data quality and accessibility through database tuning and maintenance
  • Debugging and troubleshooting system errors or performance issues

Their overarching goal is to harness the power of big data effectively, driving actionable insights that can propel business growth.

Required Skills and Qualifications

The vast capability of Hadoop and its features means that Hadoop developers need several key qualifications and skills to succeed in their role. Note that because Hadoop development skills are in such high demand, advertised positions in data science and more general web development prefer some Hadoop experience. However, for a specialized Hadoop developer, there’s an expectation of a deeper understanding of the framework. 

A bachelor’s degree in computer science or a related field is usually required for developers, though practical experience using Hadoop is also suitable for most positions. Knowledge of components such as Hbase, Pig, Hive, Flume, and Sqoop are vital, as these tools help to gather, populate, and analyze data within Hadoop. In order to develop Hadoop applications, proficiency in back-end programming languages like Java and Python are often required.

As a Hadoop developer progresses to more senior positions, soft skills and leadership development become increasingly important. Based on the organization, Scrum or Agile certification may be necessary to lead junior developers in a project management capacity. At this stage, senior developers act as a technical knowledge base and a leader to other team members in both DevOps methodology and traditional roles.

An example of adding a list of numbers using Hadoop (version 3.3.6) and Python 3
Figure 1: An example of adding a list of numbers using Hadoop (version 3.3.6) and Python 3.

Popular Hadoop-Related Languages and Libraries

Hadoop programmers often harness a combination of languages and libraries to handle big data operations.

Languages

  • Java: Serving as the backbone of Hadoop’s core code, understanding Java is critical for writing MapReduce programs.
  • Python: Renowned for its simplicity and readability, Python is a go-to language in the data science community and is ideal for developing big data solutions on Hadoop.

Libraries

  • Apache Hive: A data warehouse infrastructure built atop Hadoop, Apache Hive simplifies querying and managing large datasets residing in distributed storage.
  • Pig: An easy-to-master scripting language, Pig empowers developers to write complex MapReduce transformations using straightforward syntax.
  • HBase: This open-source, non-relational, distributed database operates on top of Hadoop and provides real-time read/write access to large datasets.
An example of using Hadoop (v3.3.6) and Python 3 to calculate the total sum for each grouped product.
Figure 2: An example of using Hadoop (v3.3.6) and Python 3 to calculate the total sum for each grouped product.

Popular Hadoop Programming Tools

In addition to languages and libraries, a variety of tools can assist Hadoop developers in effectively managing big data tasks.

Apache Flume

Flume is a distributed service for efficiently collecting, aggregating, and moving large amounts of log data into Hadoop. Its creators designed Flume with extensibility and robustness in mind.

Oozie

Oozie is a server-based workflow scheduling system that allows developers to manage Hadoop jobs. It combines multiple jobs sequentially into one logical unit of work.

Sqoop

Sqoop serves as a tool for transferring bulk data between Apache Hadoop and structured data stores such as relational databases. It efficiently offloads certain tasks, such as ETL processing from the EDW systems to Hadoop.

How Does Hadoop Fit Into Modern Back-End Development?

Modern organizations are constantly looking for ways to leverage big data. Herein lies the value of Hadoop in the landscape of back-end development. 

Hadoop developers can help businesses design and implement efficient back-end solutions that handle massive data volumes. For instance, for a social media company, a Hadoop developer could build robust back-end systems that process and analyze user data to tailor personalized content. 

In another scenario, consider a logistics company. A Hadoop developer can help optimize routes by analyzing large sets of GPS coordinates and traffic information.

By integrating Hadoop into your back-end development strategy, your business can tap into its reservoirs of big data to drive insights, streamline processes, and boost overall efficiency.

How Much Does it Cost to Hire a Hadoop Developer?

The approximate annual base salary for a mid-level Hadoop developer in the United States is approximately $94,875 per year. Several factors affect this figure, including experience, location, and industry. Additionally, initial onboarding and training costs can potentially increase the cost for an employer. According to the same data, salaries can range from $76,000 to $118,000 depending on experience.Note that these salaries are a base figure and don't include bonuses, commission, or company equity options.

Because the cost of living is lower for equally skilled Latin American Hadoop developers, they usually earn about 30% to 50% less than American developers based on their experience and location. Revelo connects you to highly qualified, pre-vetted Latin American developers, providing a cost-effective workflow solution without a loss in product quality.

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Finding Hadoop Developers for Your Project

Locating top-notch Hadoop developers requires a strategic approach. Start by defining your project requirements clearly — knowing the exact skills and experience you need helps narrow down your search.

Consider tapping into tech-focused job boards or forums, where many talented developers showcase their work. Furthermore, networking events and "hackathons" can also serve as excellent avenues to connect with potential candidates.

Don't limit yourself to local talent, but consider remote software developers too. Widening your talent pool geographically leads to finding more highly skilled professionals than if you were to only search locally. 

While hiring is about finding candidates who have the right skills, don't forget to screen for cultural fit and shared values to foster effective teamwork and communication. 

Why Hire Hadoop Developers With Revelo?

Hiring a Hadoop developer can be a game-changer for your organization's big data needs. They can help you harness the power of data, drive insights, and propel your business growth. However, finding the right candidate demands time, resources, and a deep understanding of the role.

Revelo helps make this process seamless. Our extensive vetting process ensures you have access to top Hadoop developers from Latin America who possess not just technical prowess and soft skills but also have English proficiency and align with your company culture.

Looking to hire big data developers to join your development team? Contact us to connect with top talent today.

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Frequently Asked Questions

Is there a free trial period for hiring
Hadoop
 
Developers
through Revelo?

Yes, if for any reason you find the developer you hire isn't a good fit within the first 14 days - you pay nothing or we can find you a replacement at no additional cost.

How are Revelo
Hadoop
 
Developers
different?
Revelo offers full-time remote
Hadoop
 
Developers
who share or highly overlap with your work day. You get world-class
Hadoop
 
Developers
in Latin America who speak English and are vetted on soft and technical skills. All
Developers
live in the same time zones as the US or adjacent due to our talent base being exclusively in Latin America.
How do I hire
Hadoop
 
Developers

Hiring a full-time developer through Revelo is a simple 3-step process. First, you tell us your hiring needs. Second, we match you to the best developers within 3 days. Third, you interview the candidates you like and hire the one you like most.

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