Apache Hadoop

What is Apache Hadoop?

Apache Hadoop is an open source software framework for running distributed applications and storing large amounts of structured, semi-structured, and unstructured data on clusters of inexpensive commodity hardware.

Hadoop is credited with democratizing big data analytics. Before Hadoop, processing and storing large amounts of data was a challenging and expensive task that required high-end, proprietary hardware and software. Hadoop’s open-source framework and its ability to run on commodity hardware made big data analytics more accessible to a wider range of organizations.

Since Hadoop was first released in 2006, cloud computing, as well as containerization and microservice architectures, have significantly changed the way applications are developed, deployed, and scaled. While Hadoop is now considered a legacy technology for big data, the framework still has specific use cases.

Techopedia Explains the Hadoop Meaning

The Hadoop software framework was created by Doug Cutting and Mike Cafarella and was inspired by how Google processed and stored large amounts of data across distributed computing environments.

The name “Hadoop” doesn’t stand for anything; Doug Cutting named the framework after his son’s toy elephant. The unique, playful name inspired an ecosystem of open-source tools for obtaining actionable insights from large, complex datasets.

While Hadoop’s role in projects today may be limited because of more advanced frameworks like Apache Spark, Hadoop still plays a role in scenarios where organizations have already invested in Hadoop infrastructure and still have specific use cases for using the Hadoop ecosystem.

Apache Hadoop vs. Apache Spark

Hadoop’s ability to scale horizontally and run data processing applications directly within the Hadoop framework made it a cost-effective solution for organizations that had large computational needs but limited budgets.

It’s important to remember, however, that Hadoop was designed for batch processing and not stream processing. It reads and writes data to disk between each processing stage. This means it works best with large datasets that can be processed in discrete chunks, rather than continuous data streams.

While this makes Hadoop ideal for large-scale, long-running operations where immediate results aren’t critical, it also means that the framework may not be the best choice for use cases that require real-time data processing and low-latency responses.

In contrast, Apache Spark prioritizes in-memory processing and keeps intermediate data in random access memory (RAM). This has made Spark a more useful tool for streaming analytics, real-time predictive analysis, and machine learning (ML) use cases.

Hadoop History

Hadoop’s ability to scale horizontally and run data processing applications directly within the Hadoop framework made it a cost-effective solution for organizations that had large computational needs but limited budgets. Here’s a brief overview of how Hadoop came to be:

How Hadoop Works

Hadoop splits files into storage blocks and distributes them across commodity nodes in a computer cluster. Each block is replicated across multiple nodes for fault tolerance. Processing tasks are divided into small units of work (maps and reduces), which are then executed in parallel across the cluster. Parallel processing allows Hadoop to process large volumes of data efficiently and cost-effectively. 

Here are some of the key features of Hadoop that differentiate it from traditional distributed file systems:

• Hadoop can scale out from a single server to thousands of machines with minimal human intervention.
• Hadoop automatically replicates data blocks across multiple nodes. If one node fails, the system can access copies of those blocks from other nodes and automatically restart failed tasks.
• Hadoop can be used with data lakes that store multiple types of data.

Hadoop Components

Here’s a breakdown of the three key Hadoop components, along with their roles in the big data ecosystem:

The following tools work alongside Hadoop’s core components to create an ecosystem that expands the framework’s capabilities:

Hadoop Challenges

While expanding the Hadoop ecosystem to support diverse workloads beyond batch processing greatly increased the framework’s capabilities, it also made deployments and ongoing management more complex.

Today, cloud-based services like Amazon EMR, Google Cloud Dataproc, and Microsoft Azure HDInsight provide managed service environments that abstract away some of Hadoop’s underlying complexity.

This evolution reflects broader trends in technology, where cloud-based solutions are increasingly providing more managed and integrated services to simplify complex infrastructure and software management tasks in hybrid cloud or multi-cloud environments where data needs to be moved between different storage systems and platforms.

Pros and Cons of Hadoop

Organizations that use Hadoop can achieve significant insights and value from files stored in data lakes as long as the organization’s information technology (IT) team has the skills required to navigate the framework’s intricacies – and the time to optimize and maintain the Hadoop ecosystem effectively.

Here’s a comparison table that outlines some of the pros and cons of using Hadoop for big data processing:

The Bottom Line

Apache Hadoop is a legacy open source framework for processing and storing large volumes of data across distributed computing environments. Hadoop is still useful for batch processing big data, but the framework’s complexity, steep learning curve, and performance limitations for real-time processing can be challenging.

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