hadoop

BEYOND THE ORDINARY

Hire Hadoop
Engineer

Welcome to Bluebash AI, where data-driven solutions meet innovation. As pioneers in the data engineering landscape, we fuse experience with cutting-edge technology to empower businesses like yours. Dive into our specialties below:

Let’s Build Your Business Application!

We are a team of top custom software developers, having knowledge-rich experience in developing E-commerce Software and Healthcare software. With years of existence and skills, we have provided IT services to our clients that completely satisfy their requirements.

Reinvent Your Data Storage & Processing with Apache Hadoop

Apache Hadoop, an open-source software framework, has revolutionized the way we handle vast datasets. Born out of a project by Doug Cutting and
Mike Cafarella in 2006, its objective was to support the distribution for the Nutch search engine. Hadoop’s unparalleled
ability to store and process enormous data amounts has since made it a staple in the big data universe.

hadoop

Why Apache Hadoop?

Hadoop revolutionised data processing and storage by enabling distributed handling of massive datasets across computer clusters, ensuring remarkable scalability. Its key elements, HDFS for storage and YARN for resource management, guarantee strong fault-tolerance and dependability.

history of hadoop

History of Apache Hadoop:

It all began at UC Berkeley’s AMPLab. Matei Zaharia, noticing the limitations of the Hadoop MapReduce computing model, conceived Spark. His vision? To accelerate a myriad of computing tasks – from batch applications to machine learning – achieving unparalleled velocities.

The EVOLUTION OF APACHE HADOOP

evolution of spark
apache_hadoop_2006
2006

The Genesis

  • Backstory:

    Hadoop emerged from the Lucene project as a solution to support distributed data processing for the Dutch search engine. Doug Cutting and Mike Cafarella were inspired by a paper published by Google on their in-house processing system, MapReduce.

  • Research Paper:

    Jeffrey Dean and Sanjay Ghemawat. "MapReduce: Simplified Data Processing on Large Clusters."

apache_hadoop_2008
2008

Entering the Mainstream

  • Backstory:

    Yahoo! recognised Hadoop's potential and deployed it in one of its large-scale production applications. This deployment symbolised Hadoop's readiness for big league applications.

  • Research Paper:

    Konstantin Shvachko, Hairong Kuang, Sanjay Radia, Robert Chansler. "The Hadoop Distributed File System."

apache_hadoop_2011
2011

A Landmark Achievement - Hadoop 1.0.0

  • Backstory:

    This version signified stability and was the culmination of years of rigorous testing and development. The most notable feature was the Hadoop Distributed File System (HDFS).

  • Research Paper:

    "Hadoop: A Framework for Running Applications on Large Clusters Built of Commodity Hardware" by Apache Foundation.

apache_hadoop_2013
2013

A Leap Forward - Hadoop 2.0.0

  • Backstory:

    Hadoop 2 introduced YARN (Yet Another Resource Negotiator), segregating the responsibilities of resource management and job scheduling/monitoring. This change made Hadoop more versatile, enabling it to support workloads beyond just MapReduce.

  • Research Paper:

    Vavilapalli VK, et al. "Apache Hadoop YARN: Yet Another Resource Negotiator."

apache_hadoop_2016
2016

The Next Generation - Hadoop 3.0.0 alpha

  • Backstory:

    This iteration brought in support for more than two NameNodes, enhancing the system's resilience. It also emphasized containerisation and GPU support, gearing Hadoop for the era of deep learning and AI.

  • Research Paper:

    Wangda Tan, et al. "Bringing deep learning to big data and data science applications using Apache Hadoop."

apache_hadoop_2020
2020

Refinement and Expansion

  • Backstory:

    Hadoop’s architecture and core have seen enhancements, focusing on scalability, performance optimisation, and support for cloud infrastructure. The community-driven aspect ensures that Hadoop stays at the forefront of big data solutions.

  • Research Paper:

    Qilin Shi, et al. "Benchmarking and improving cloud storage performance with Apache Hadoop."

Why Bluebash AI for Hadoop?

Data is the new gold, and with Hadoop, we help you mine it. Our Hadoop engineers, armed with years of experience, curate solutions
tailored to decipher your data’s stories. Here’s what makes us the torchbearers of Hadoop excellence

  • Experience:

Diverse project exposures arm our Hadoop engineers with unmatched skills.

  • Customisation :

We believe in bespoke. Our Hadoop solutions resonate with your unique challenges.

  • Full-Circle Management:

From inception to resolution, our oversight ensures your Hadoop ecosystem thrives.

low price

Certainly! Let's deep dive into the process, integrating the
specifics of Apache Hadoop:

Diagnosis

Our initial step involves a holistic understanding of your current digital framework. We dedicate time to unravel your system intricacies, locating inefficiencies, potential bottlenecks, and assessing scalability hurdles. This diagnosis ensures our solutions are tailored, addressing specific challenges and enhancing the robustness and efficiency of your data operations

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Blueprinting

 Our design phase emphasises bespoke solutions. Our experts meticulously construct a Hadoop architecture that resonates with your business requirements. By integrating the powerful Hadoop Distributed File System (HDFS) for data storage and YARN for proficient resource management, we set the stage for a transformative data journey

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Rollout

Transition is crucial. We ensure the Hadoop environment seamlessly integrates with your systems, emphasizing absolute data integrity and streamlined migration. Our rollout strategies are formulated to minimize disruptions, guarantee data continuity, and lay the groundwork for optimal initial performance.

planing
planing

Analysis

With Hadoop’s capabilities at our disposal, we employ MapReduce jobs to delve into vast datasets. Our analytical methods are designed to sift through data noise, extracting pivotal insights, highlighting trends, and unearthing actionable intelligence that can drive strategic decision-making

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Fine-Tuning

Post-deployment, our commitment remains unwavering. We engage in continuous monitoring, optimizing query speeds, ensuring balanced data distribution across nodes, and fine-tuning system parameters. This adaptability ensures your Hadoop setup remains agile, efficient, and ready for evolving data challenges.

planing planing

Guardianship

We adopt a proactive stance towards system maintenance. Our teams maintain a vigilant watch over your Hadoop clusters, preemptively spotting potential issues. This guardianship approach guarantees that any challenges are swiftly addressed, ensuring your operations remain uninterrupted and consistently high-performing.

Hadoop in Action: In-Depth Use Cases

petabyte search

Building a Petabyte-Scale Search Engine for a Tech Giant

A prominent tech conglomerate recognized a demand for a scalable search engine capable of indexing and retrieving petabytes of data with minimal latency.

realtime analytics

Crafting a Real-time Analytics Platform for a Global Online Retailer

In the fiercely competitive e-commerce landscape, a global online retailer sought to gain an edge by understanding user behavior in real-time to tailor offerings and enhance user experience.

data lake

Data Lakes for a Leading Financial Institution

A leading financial institution, dealing with diverse data sources, wanted a unified solution for comprehensive data analysis

Frequently Asked Questions

Apache Hadoop is an open-source framework for distributed storage and processing of large datasets. It provides a scalable and reliable platform for storing and analyzing big data.

At Bluebash, we carefully screen and select top-tier Hadoop developers to ensure you get skilled professionals who can meet your big data requirements with expertise and precision.

The Apache Hadoop architecture consists of two main components: Hadoop Distributed File System (HDFS) for storage and MapReduce for processing. It enables the distributed storage and processing of large datasets across a cluster of computers.

Our Apache Hadoop developers at Bluebash are highly qualified and experienced in working with big data. They possess in-depth knowledge of Apache Hadoop, ensuring they can efficiently handle complex data processing tasks.

Our developers can help you harness the power of big data by implementing robust Hadoop solutions. They optimize data processing, storage, and analysis to provide insights that drive informed decision-making for your business.

Yes, at Bluebash, we understand that every business has unique requirements. Our Apache Hadoop developers can tailor solutions to meet your specific big data needs, ensuring optimal performance and efficiency.