The Development of Hadoop Database and Why It is Important

Written By Dale Hammington

As the World Wide Web started growing exponentially in the last decade of the 20th century and the first decade of the 21st century, search engines and the search ending indexes were formed to locate the most relevant info on the internet on text-based content. During the initial years, search results were returned by actual humans. However, as the web grew exponentially from a few dozens of pages to millions of web pages, automation became the need. So, web crawlers were formed, and many projects for search engines were established. 

History of Hadoop

One such project for web search as an open-source search engine was named as Nutch, which was the brainchild of Mike Cafarella and Doug Cutting. They wanted to speed up the web research result delivery by more effectively distributing the data and calculations by splitting across many computers, in order to help accomplish multiple tasks simultaneously. Another project was also in progress during this time, which was named Google. It was also based on a similar concept as by storing data in a distributed environment and processing it in an automated way to load the web search results faster.

In the year 2006, Cutting joined the web leader Yahoo and also took the Nutch project with him. The project was divided, in which the web crawler part remained as Nutch itself, and the distributed computing processing was called Hadoop (which was named after the name of the toy elephant of Cutting’s son). Yahoo released Hadoop in 2008 as an open-source solution. Now, Hadoop’s ecosystem and framework are managed by the Apache Software Foundation (ASF), which is a non-profit global community of expert developers and volunteering contributors.

Why is Hadoop important?

In terms of enterprise data management:

  • Hadoop has a strong ability to store, manage, and process a huge volume of data of various types quickly. As with big data, data volume and varieties are increasing exponentially as in case of social media, machine learning, and Internet of Things (IoT), etc. where Hadoop becomes a primary consideration
  • Hadoop has more computing power. As we had seen above, the advanced distributed computing model of Hadoop helps to process big data faster. The number of computing nodes out there in the system, the more computing power and speed users enjoy.
  • Better fault tolerance. In the Hadoop ecosystem, applications and data processing are protected against any potential hardware failures. In case if a node fails, then the work is automatically routed to other nodes in the network, and the processing never fails. An in case of RemoteDBA.com database management service, multiple copies of the same data are stored at different nodes.
  • Hadoop possesses better flexibility. Compared to the conventional traditional RDBMS, the users need not have to preprocess the data for storing. It is possible to store as much data as users want, of any format, and also decide how to use the data later. Unstructured data in the form of videos or images can also be stored and used this way.
  • Budget-friendly. The major point to note about enterprise Hadoop is that it is a low-cost solution. Being in the open-source framework, it can be used freely for any commercial or personal usages.
  • Hadoop is scalable. Users can easily grow or shrink your system anytime as needed to handle more data by simply adding more nodes. A little bit of administration may be required for scaling up, but it is far easier and instant compared to the need for scaling in general database management.

Hadoop challenges

1. Limitations of MapReduce 

MapReduce programming has many benefits, but it is not an ideal solution to all problems. Even though good for the simple info requests which can be divided into more independent units. However, not so efficient for interactive and iterative tasks for analytics.

2. Talent gap

In Hadoop, there is a largely acknowledged talent gap. Sometimes, it may be difficult to get entry-level developers who have enough skills with MapReduce. This is a major reason why the providers prefer relational (SQL) technologies prior to Hadoop as it is easy to find SQL specialists to work.

3. Data security

There is still a lot of discussion going around the security issues of fragmented data. However, this aspect as a very progressive development with many new tools surfacing. It is found that the Kerberos authentication protocol makes Hadoop environments much more secure lately.

4. Data management and governance tools

Even though strong, Hadoop doesn’t have fully featured and easy to use tools for end-to-end data management, cleansing, metadata, and governance. Data quality and standardization tools are also lacking.

Hadoop’s most popular uses today

Going far beyond its initial objective of searching webs pages and returning results, Hadoop has grown far higher as many organizations are no looking to adopt Hadoop as their big data platform. Some popular uses of Hadoop are:

  • Low-cost storage

Minimal cost of hardware now makes Hadoop everyone’s solution for storing and combining data of various sources as for transactional, social media, machine, sensor, click streams, scientific, etc. to name a few. Low-cost storage solutions also help you keep the information which is less critical by many be of use in the long run.

  • Discovery and analysis with the sandbox approach

As Hadoop can deal with huge data volumes in various forms and shapes, analytical algorithms can also be run over it. Big data analytics will help organizations now to attain more operational efficiency and uncover better opportunities. Sandbox approach with Hadoop offers even the smallest organizations an opportunity to innovate at low cost.

  • Data lakes

Data lakes help store data in the original format. The concept is to offer a raw view of the data to the analysts. This helps them to ask questions without any constraints. However, data lakes are not replacements for the data warehouses.

Hadoop can also be the backbone for the future technology of IoT as the core of IoT is data streaming in real-time. Hadoop can be the data store for billions of simultaneous transactions. This capability of massive storage and real-time processing will make Hadoop the sandbox for the definition of patterns for prescriptive instructions. The users can continuously improve such instructions based on current information as Hadoop gets updated constantly with new data. In the realm, Hadoop will change the business decision making and operational practices of enterprises.

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