Statistics show that the Hadoop market has been continuously growing, with projections suggesting a valuation of $842.25 billion by 2030. (Allied Market Research) This phenomenal growth of Hadoop has been attributed to several factors such as the growing data volumes, the increase in unstructured data, and the need for big data analytics, among others. Facebook, Yahoo!, and Netflix are some of the many big brands using Hadoop for cost-effective and fast processing of big data. Hadoop finds its way into diverse industries including banking and financial services, healthcare, and manufacturing.
In this blog post, we will discuss the Hadoop architecture in more detail to help you better understand its underlying structure and functionalities. We’ll delve into components of the Hadoop architecture, understanding how each contributes to fast big data analytics and processing.
Table of Contents
What is Hadoop?
Hadoop is an open-source framework that allows the storage and processing of gigabytes and petabytes of data. The platform is written in Java, and it is a part of the Apache ecosystem. Hadoop clusters enable parallel analysis of massive datasets across multiple interconnected computers, instead of using a single large computer, which significantly accelerates data processing.
Components of Hadoop Architecture
The Hadoop architecture uses several core components for parallel processing of large data volumes:
HDFS: The Storage Layer in the Hadoop Architecture
The Hadoop Distributed File System allows the storage of large data volumes by dividing it into blocks. HDFS is designed for fault tolerance and ensures high availability by replicating data across multiple nodes in a Hadoop cluster, allowing for efficient data processing and analysis in parallel.
The HDFS has three components – 1. NameNode 2. Secondary NameNode and 3. Slave Node
- NameNode, which is the master server containing all metadata information such as block size, location, etc. It is the NameNode that permits the user to read or write a file in HDFS. The NameNode holds all this metadata information on the various DataNodes. There can be several of these DataNodes retrieving blocks and sending block reports to the NameNode.
- Secondary NameNode server that maintains metadata copies in the disk as a backup in case the NameNode fails. The Secondary NameNode performs the same function as the standby NameNode in a high-availability cluster.
- Slave Node that stores all the data as blocks.
YARN: The Cluster Resource Management Layer in the Hadoop Architecture
YARN (Yet Another Resource Negotiator) is the cluster resource management layer in Hadoop architecture. It has the crucial job of job scheduling and managing the cluster. YARN helps in task distribution, job prioritization, dependency management, and other aspects across the Hadoop cluster for optimum processing efficiency. It allows multi-tenancy, supports easy scalability, and optimizes cluster utilization.
YARN resides as a middle layer between HDFS and MapReduce in the Hadoop architecture. It has three core elements – 1. ResourceManager 2. ApplicationMaster and 3. NodeManagers
- YARN ResourceManager is the sole authority for resource allocation and tracking of resources in the cluster. It features two main components – the Scheduler, which schedules resources for various applications, and the Application Manager, which accepts job submissions and monitors the application clusters.
- YARN ApplicationMaster investigates the resource-management side, fulfilling the resource requirements of individual applications through interactions with the scheduler.
- YARN Node Manager tracks the jobs and monitors resource utilization in containers that house the RAM and CPU.
MapReduce: Distributed Parallel Processing Model in the Hadoop Architecture
Hadoop uses the MapReduce programming model for parallel processing of large datasets. It is a fundamental component in the Hadoop ecosystem for big data analytics.
MapReduce consists of two main phases: the Map Phase and the Reduce Phase.
In the Map Phase, input data is divided into smaller chunks and processed in parallel across multiple nodes in a distributed computing environment. The input data is typically represented as key-value pairs.
In the Reduce Phase, the results from the Map phase are aggregated by key to produce the final output.
Hadoop Common or Common Utilities in the Hadoop Architecture
This crucial component of the Hadoop architecture ensures the proper functioning of Hadoop modules by providing shared libraries and utilities. Hadoop Common contains the Java Archive (JAR) files and scripts required to start Hadoop.
Advantages of a Well-designed Hadoop Architecture
- Data Storage and Scalability: The Hadoop Distributed File System’s ability to store and process vast data volumes at speed is its biggest strength. As data grows, organizations can scale their Hadoop clusters easily by adding more nodes for increased storage capacity and processing power.
- Batch and Real-time Data Processing: Hadoop’s MapReduce module supports both batch processing and real-time stream processing when integrated with frameworks like Apache Spark). This versatility allows organizations to address various use cases of advanced analytics.
- Cost-Effectiveness: Hadoop is designed to run on commodity hardware, which is more cost-effective than investing in high-end, specialized hardware. This makes it an attractive option for organizations looking to manage large datasets without incurring substantial infrastructure costs.
- Data Locality and Data Integrity: Hadoop processes data on the same node where it is stored, minimizing data movement across the network. This approach enhances performance by reducing latency and improving overall efficiency. Hadoop minimizes data loss and ensures data integrity through duplication on multiple nodes.
- Community Support: Hadoop users enjoy a large open-source community for continuous updates, improvement, and collaboration. Hadoop also offers a rich repository of documentation and resources.
These are some of the many advantages that Hadoop architecture provides to its users. Having said that, the Hadoop architecture does present some limitations such as security management complexities, vulnerability to cyber threats due to Java, and challenges in handling small datasets, among others. This often prompts organizations to seek modern cloud-based alternatives such as Databricks, Snowflake, or the Azure suite of tools.
Strategic Implications of the Hadoop Architecture in Business
As we explore the technical details of Hadoop architecture, understanding its strategic implications is also important for businesses. Organizations can either adopt Hadoop or consider cloud-based alternatives depending on their specific organizational needs, existing infrastructure, the nature of the data being processed, and various other factors. Many organizations also adopt a hybrid approach, leveraging both on-premises Hadoop clusters and cloud-based services to strike a balance between performance, scalability, and ease of management. Therefore, a thorough understanding of this domain is necessary for success.
In the evolving landscape of big data solutions, companies often lose focus on real business objectives and high-value tasks and instead, get entangled in solving data framework complexities. In such cases, seeking guidance from experts can be instrumental in realigning their focus with core business objectives for sustained success. Speaking to experts can result in more efficient operations, improved decision-making processes, and better business outcomes.
Connect with our data experts to understand the opportunities related to Hadoop architecture and other leading big data solutions in your business to make well-informed and strategic decisions.
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