The Internet is limitless and infinite, don’t you agree? Starting from a few dozen websites, there are millions of websites within a span of 2-3 decades. How does anyone search information in this unlimited library? There were several solutions found back in the last decade of the 20th century which led to the discovery of today’s so-called search engines.
The concept of the search engine was simple. Many experts had the idea to store information in several systems and whenever a search is initiated, the results were computed at remarkable speeds and these results were provided to the end user. This is how Hadoop started its journey in 1999.
Hadoop is an open source software framework used for both storing information and running applications related to the commodity with enormous processing power and massive data storage capabilities. The major advantage of Hadoop is the ability to handle limitless multitasking. This was the essential advantage needed by search engines to process and return results faster.
We know how Hadoop began its journey along with its major ability. However, that alone doesn’t suffice for its extensive usage in this current technological age. There are several remarkable features which are mandated by any such open source software framework.
Let’s check out those features
• Irrespective of size and type of data, it is stored – The constant increase in data volumes due to the excessive use of social and IoT (Internet of Things) produces an unimaginable amount of data which has to be stored somewhere and it is stored here to be precise.
• Processing and Computation ability – Depending on the size of the incoming data, the more data – the more computational power concept is introduced here.
• Redirect from faulty nodes – A node is basically a system, if a hardware failure occurs, jobs are automatically redirected to a different node which provides successful distributed computing without the loss of data or resources.
• Flexible – Unstructured data storage to be used for computation and processing later is a huge advantage since the incoming data cannot be categorized immediately.
• Cost effective – Commodity hardware is used to store huge data bytes so it is basically free of cost.
• Scalable – In need of more storage or computational power, add a node or two to take care of it.
With these features, Hadoop has had tremendous growth over the last 2 decades. However, there are a few challenges that need to be overcome while using Hadoop.
• MapReduce programming
MapReduce is better suited for simple requests and problems which is divided into independent units. However, it doesn’t provide an efficient iterative or interactive analysis.
• Not suitable for a beginner
Entry level programmers don’t possess the Java skills needed to handle MapReduce effectively. This is one the main reasons for Hadoop being replaced by SQL.
Fragmented data is vulnerable to the third party. however, new technology like the Kerberos authentication protocol is providing better security for Hadoop environment.
• Data management
Hadoop lacks the tools for full-fledged data cleansing, governing and metadata. It also lacks any data quality and standardization tools.
These challenges are slowly but surely being overcome by Hadoop, it is just a matter of time since authenticators like Kerberos is developed for data protection, tools, and other necessary technology will be invented over time to make it an easy data storage and access center.
The name explains itself, big data meaning a large amount of data which cannot be computed using the traditional ways. Big data has been evolving into a more structured and organized way with many people working on it. There are several online big data courses available for you to pursue your career in this field.
Big Data can be classified into several branches based on the data produced by different devices and applications. Let’s have a look at it:
• Black Box Data – Data related to helicopters, jets, and airplanes fall into this sector.
• Social Media Data – Any social media related data including the views and shares posted by numerous people all over the globe.
• Stock Exchange Data – The buy and sell details of trading commodities and stocks are stored here.
• Power Grid Data – This holds information related to a particular node of a base station.
• Transport Data – This data includes model, distance, capacity and availability of a vehicle.
• Search Engine Data – Data gathered via search engines from different databases are stored here.
There are 3 types of incoming data here – Structured, Unstructured and Semi-Structured data which is stored immediately and computed at a later point of the time.
We talked about why Hadoop, we discuss the same here for Big Data. Hadoop and Big Data can be considered as siblings since there are online Big Data courses along with Hadoop implementation methods too.
• Social Media Information – Many social media network has become the platform to gather and analyze data. Big data does the same to learn about the responses for a particular campaign, event, promotions and more.
• Creating Business – Depending on the previous information gathered, the manufacturers produce consumed liked products. This helps in rapid manufacturing and selling thereby creating business opportunities.
• Medical Assistance – Depending on the medical history of the patient, you can expect better and quick medical aid for your ailment. This is a huge improvement for the medical field since many patients will not be carrying a medical history due to emergency situations.
There are a few challenges faced by Big Data too:
• Capturing data
However, most of these challenges are being overcome by the sibling Hadoop with ease.
SAS (Statistical Analysis System) is a programming language developed to statistical analysis whereas Hadoop is an open-source framework for storing data along with providing the platform to run applications on commodity hardware. These two are entirely different products and there is no comparison between the two. There are some good Institutes like PST Analytics are available in India to learn SAS.
Big Data and Hadoop go hand in hand to cover up each other’s weaknesses. A career can be made using any of these excellent platforms, you should be able to check for courses online for more information. That being said, I hope this information will be helpful in any possible way.