Advantages and disadvantages of hadoop

Hadoop is one kind of open-source software utility collection for performing computations on a large amount of data. It provides a software framework for multiple storages in various locations and processes them using MapReduce technology. Hadoop processes the various structured and unstructured to collect, process and analyze big data. Let us check out some important advantages and disadvantages of Hadoop to know about it.

Advantages of Hadoop:

  • Hadoop is a highly scalable storage platform. Hence, it can store and distribute a huge amount of data sets across hundreds of inexpensive servers.
  • Hadoop provides a cost-effective storage solution for businesses to exploding data sets.
  • Hadoop allows businesses to easily access new data sources and tap into various types of data to generate value from that data. Hence, Hadoop derives valuable business insights from data sources such as social media, email conversations.
  • Hadoop can be used for a wide range of purposes, including log processing, data warehousing, consumer strategy analysis, and fraud detection.
  • Hadoop can handle unstructured as well as semi-structured data.
  • The main advantage of Hadoop is its fault tolerance. When data is sent to a specific node, the data is also distributed to other nodes in the network, ensuring there is another copy available for use in the event of failure.
  • Hadoop framework has built-in power and flexibility to do what not possible earlier.
  • The addition of more nodes to the Hadoop cluster provides more storage and computing power. This feature eliminates the need to buy external hardware. Hence, it is a cheaper solution.
  • The unique storage method of Hadoop is based on a distributed file system that effectively maps data wherever the cluster is located. The data analysis devices are also on the same servers where the data is located, resulting in much quicker processing of the data.
  • Hadoop helps in distributing data on different servers and must be prevented network overloading.
  • The HDFS layer in Hadoop has self-healing, replicating and fault tolerance characteristics. By using this, it automatically replicates data if the server or disk got crashed.

Disadvantages of Hadoop:

  • Hadoop is complex applications and it difficult to manage. The security of Hadoop is the main concern, which is disabled by default due to sheer complexity. If whoever managing the platform lacks to know how to enable it, your data could be a huge risk.
  • Talking about security, the own makeup of Hadoop makes it a dangerous proposition to manage. The framework is written almost in Java which has been heavily exploited by cybercriminals.
  • Hadoop does not have storage or network-level encryption.
  • Whenever, Hadoop operated by a single master it will cause difficulty in scaling.
  • Hadoop is not suitable for small and real-time data applications.
  • The Hadoop distributed file system lacks the ability to efficiently support the random reading of small files, due to its high capacity design. Thus, it is not recommended for organizations with small quantities of data.
  • Hadoop has had its fair share of stability issues like all open-source software. The organizations are strongly recommended to make sure they are running the latest stable version to avoid these issues.
  • The Apache Flume, Google’s own cloud dataflow are potential solutions and the ability to enhance data collection, processing, and integration performance and reliability. The many organizations missing out on big benefits by using Hadoop alone.
  • The programming model of Hadoop is very restrictive.
  • Hadoop is a built-in redundancy duplicates data, therefore requiring more storage resources.
  • Hadoop map-reduce programming model is basically a group handling framework. It does not boost preparing gushed information.

 

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