transferring data to hadoop

Many companies use Hadoop to analyze customer behavior on their websites, process call center activity, and mine social media data. Based on this data, companies can make decisions in real time to understand customer needs, mitigate problems, and ultimately gain an advantage over the competition. But other organizations simply use it to reduce their data storage costs.

The Process

Data is generated from a variety of devices, and this data can be both structured and unstructured. Structured data is stored in a Relational Database Management System (RDBMS) and unstructured data is stored in a file system. Data users may employ a mainframe, or use a distributed system like Hadoop. But to utilize Hadoop, mainframe data must be moved to the server where the Hadoop system lives.

Why use Hadoop rather than a different distributed system?

The Benefits of Hadoop

  1. Scalability: Unlike traditional relational database systems (RDBMS) that can’t easily scale to process large amounts of data, it enables companies to run applications on thousands of nodes involving thousands of terabytes of data.
  2. Cost Effectiveness: An issue with traditional relational database management systems is they are extremely cost prohibitive to scale when dealing with large amounts of data. In an effort to reduce costs, many companies in the past would have had to down-sample data and classify it based on certain assumptions as to which data was the most valuable. The raw data would be deleted, as it would be too cost-prohibitive to keep. This meant that when business priorities changed, the complete raw data set was no longer available. Hadoop is designed as a scale-out architecture that can affordably store all of a company’s data for later use, therefore avoiding this issue of lost data.
  3. Flexibility: It enables businesses to easily access new data sources and tap into different types of data (both structured and unstructured) to generate value. This means businesses can use Hadoop to derive valuable business insights from data sources such as social media, email conversations or clickstream data. In addition, it can be used for a wide variety of purposes, such as log processing, recommendation systems, data warehousing, market campaign analysis and fraud detection.
  4. Processing Speed: Its unique storage method is based on a distributed file system that basically ‘maps’ data wherever it is located on a cluster. The tools for data processing are often on the same servers where the data is located, resulting in much faster data processing. If you’re dealing with large volumes of unstructured data, Hadoop is able to efficiently process terabytes of data in just minutes, and petabytes in hours.
  5. Fault Tolerance: Another key advantage is its fault tolerance. When data is sent to an individual node, that data is also replicated to other nodes in the cluster, which means that in the event of failure, there is another copy available for use.

There are several different ways your  organization can utilize all the features of Hadoop, but first you need a safe and secure way to move your data there. We have experts at this type of data transfer. We will not only deliver your data to any application, but do it in less time and at a significantly lower cost, regardless of the platform or location. Contact us to learn how we can help you!