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Hadoop Technologies

Hadoop is an open source distributed processing framework that manages data processing and storage for big data applications in scalable clusters of computer servers. It's at the centre of an ecosystem of big data technologies that are primarily used to support advanced analytics initiatives, including predictive analytics, data mining and machine learning. Hadoop technologies systems can handle various forms of structured and unstructured data, giving users more flexibility for collecting, processing, analysing and managing data than relational databases and data warehouses provide.

Hadoop technologies ability to process and store different types of data makes it a particularly good fit for big data environments. Artistixe is well-known IT solution in India, typically involve not only large amounts of data, but also a mix of structured transaction data and semistructured and unstructured information, such as internet clickstream records, web server and mobile application logs, social media posts, customer emails and sensor data from the internet of things (IoT).

Open Source

Hadoop, a powerful big-data tool, is preferred by many since it is open source. This feature also makes it highly scalable and feasible.


Processing

HDFS, a robust storage layer, provides Hadoop that competitive edge when it comes to a more reliable distributed data processing system.


Cost-friendly

Owing to its open -source applicability, Hadoop is a cost-friendly option when it comes to adding more nodes or making any other significant changes.


Security

Equipped with tools like Hbase security, MapReduce, and HDFS, Hadoop provides that additional security blanket for your enterprise data.


Hadoop's Importance to Users

Despite the emergence of alternative options, especially in the cloud, Hadoop technologies are still an important and valuable technology for big data users for the following reasons:

  • It can store and process vast amounts of structured, semistructured and unstructured data, quickly.
  • It protects application and data processing against hardware failures
  • It doesn't require that data be preprocessed before being stored.

 

  • It's scalable, so companies can easily add more nodes to enable their systems to handle more data.
  • It can support real-time analytics to help drive better operational decision-making, as well as batch workloads for historical analysis.

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