Hadoop Data Warehouse: What and Why?

 

One of the most sought-after innovations in the IT industry in today’s time is Big-data and it has taken the entire world by a storm. A major contributor to this exceptional rise is the Hadoop data warehouse and its related big data technologies. Hadoop has a number of advantages that provide the power of parallel processing to the programmers. The hype and expectations around Hadoop have been witnessing a steep rise and the excitement is coming out in the form of IoT. 

 

What is Hadoop?

Hadoop has an architecture that is very similar to traditional data warehouses. While the basic structure might seem similar, there are some very obvious differences between the two. Traditional data warehouses define a parallel architecture, whereas Hadoop’s architecture includes processors that are loosely coupled across a cluster of Hadoop platforms. Each of these clusters has the ability to work on different data sources. Data catalog, data manipulation engine, and storage engine are some of the components that work independently with Hadoop serving as the main collection point. 

 

What is the purpose of Hadoop?

This modern data architecture has gained popularity among big data firms as it possesses the ability to process huge amounts of unstructured and semi-structured data. Let’s take a look at some of the cases where Hadoop can be used.

 

 

  • Large scale Enterprises — Hadoop is used in the large scale enterprise projects that require server clusters having limited programming skills and specialized data management skills. The implementation cost for the same is also pretty high.
  • Large Datasets — Hadoop is also used to ensure high scalability and cut down on the time and money spent on managing datasets available in large volumes. 
  • Separate Data Sources — Big Data applications often accumulate data from various different data sources and use Hadoop clusters. This makes it an important software for application development. 

 

Problems with traditional warehousing

There are a number of back draws of using traditional data warehouses. These traditional data warehouses can’t control complex hierarchal data types and unstructured data types. The cost factor is another disadvantage that these warehouses face. They are also incapable of holding data that lacks a definite schema because these warehouses follow schema on-write mechanism. 

Users of the data warehouse require spending a lot of time modelling the data which not a feasible option in considering the business model.

 

If you are planning to shift your business data onto Hadoop or cloud services, Impetus Technologies is the best option. They are extremely professional and deal with all big data and ETL related services.

 

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