What is Big Data?
Internet is full of Data, and these data are available in structured and unstructured format online. The size of the Data that is generated every day is equal to 2.5 Quintillion Bytes of Data. This massive set of Data is often referred to as Big Data. It is estimated that almost 1.7 megabytes of data will be generated per second by the year 2020 by every person on earth.
A collection of data set that is very complex and large, which is very difficult to process and store using the traditional data processing application or database management tools are called Big Data. There are many challenging aspects to it, as the visualization of data, analyzing, transferring, sharing, searching, storing, curating, capturing.
The Big Data is available in three formats, and they are:
- Unstructured: These are the data that are not structured and not easy to analyze. These types of data will include unknown Schemas such as video files or audio files etc.
- Semi-Structured: These are the type of data in which some are structured, and some are not. It does not have a fixed format such as JSON, XML, etc.
- Structured: These are the best type of data in terms of structuring. The Data is wholly organized with fixed schema such as RDBMS, which makes it easier to process and analyze.
The 7 V’s of Big Data
1. Variety: Big Data has many different types of the format of data such as emails, comments, likes, sharing, videos, audios, text, etc
2. Velocity: The speed of Data at which it is generated every minute on every single day is huge. For example, Facebook users will generate 2.77 million views of the video per day and 31.25 million messages on average.
3. Volume: The Big Data has mainly got its name because of the Amount of Data created every hour. For example, a company like WalMart generated 2.5 petabytes of data from the transaction of customers.
4. Veracity: It refers to the uncertainty of the Big Data, which means how much the data can be trusted for decision making. It often refers to the accuracy of the Data collected and thus sometimes makes Big Data unreliable to make any kind of perfect decision alone.
5. Value: It refers to the meaningfulness of the Big Data, which means that just by having Big Data does not mean anything unless and until it is processed and analyzed.
6. Variability: It means that Big Data is the kind of data whose meaning is constantly changing over time, and there is no fixed meaning to it.
7. Visualization: It means the accessibility and readability of Big Data. The readability and accessibility of Big Data are very difficult due to the humongous volume and velocity of it.
What is Hadoop?
Hadoop is one of the open-source software frameworks that is used for processing and storing large clusters of commodity hardware in a distributed manner. It was developed by the MapReduce system and is licensed under the Apache v2 license, which applies the concepts of functional programming. It is one of the highest level Apache projects and is written in Java programming language.
Hadoop vs. Big Data
Hadoop can be used to store all kinds of structured, semi-structured, and unstructured data, whereas traditional database was only able to store structured data, which is the main difference between Hadoop and Traditional Database.
Difference between Big Data vs. Hadoop
1. Accessibility: One can use the Hadoop framework to process and access the data at a faster rate when it is compared to other tools, whereas it is tough to access the big data.
2. Storage: Apache Hadoop HDFS has the capability of storing big data, but on the other hand, Big Data is very difficult to be stored because it often comes in an unstructured and structured form.
3. Significance: Hadoop can process Big Data to make it more meaningful, but Big Data has no value on its own until it can be utilized to create some profit after processing the data.
4. Definition: Hadoop is a kind of framework that can handle the huge volume of Big Data and process it, whereas Big Data is just a large volume of the Data which can be in unstructured and structured data.
5. Developers: Big Data developers will just develop applications in Pig, Hive, Spark, Map Reduce, etc. whereas the Hadoop developers will be mainly responsible for the coding, which will be used to process the data.
6. Type: Big Data is a type of a problem that has no meaning or value to it unless it is processed, and Hadoop is a type of a solution that solves the complex processing of Huge Data.
7. Veracity: It means how trustworthiness the Data is. The Data that is processed by Hadoop can be used to process, analyze, and use for better decision-making. But on the other hand, Big Data cannot be relied on entirely to make any perfect decision because it has so many varieties of format and volume of data that makes it incomplete structured data to be able to process efficiently and understand. It makes Big Data not wholly reliable or trustworthiness to make a perfect decision.
8. Companies Using Hadoop and Big Data: The companies that are using Hadoop are IBM, AOL, Amazon, Facebook, Yahoo, etc. Big Data is used by Facebook, which generates 500 TB data every day and the airlines’ industry, which produces 10 TB of data every half an hour. The total data generated in the world every year is 2.5 quintillion bytes of data.
9. Nature: Big Data is vast in nature with high-variety of information, high velocity, and humongous volume of data. Big Data is not a tool but Hadoop is a tool. Big Data is treated like an asset, which can be valuable, whereas Hadoop is treated like a program to bring out the value from the asset, which is the main difference between Big Data and Hadoop.
Big Data is unsorted and raw, whereas Hadoop is designed to manage and handle complicated and sophisticated Big Data. Big Data is more like a concept for business used to denote a wide variety and volume of data sets, but Hadoop is just another technology infrastructure for analyzing, managing, and storing these vast sets of data in large quantities.
10. Representation: Big Data is like an umbrella which is representing the collection of technologies in the world, whereas Hadoop is just representing one of the many frameworks which are implementing big-data principles for processing.
11. Speed: The speed of Big Data is very, very slow and especially in comparison with Hadoop. Hadoop can process the data faster comparatively.
12. Range of Applications: Big Data has an extensive range of uses in many sectors of businesses like Banking & Finance, Information Technology, Retail Industry, Telecommunications, Transportation, and Healthcare. Hadoop is used to solve mainly three types of components, which are YARN for cluster resource management, MapReduce for parallel processing, and HDFS for data storage.
13. Challenges: For Big Data, Securing Big Data, Processing Data of Massive Volumes and Storing Data of Huge Volumes is a very big challenge, whereas Hadoop does not have those kinds of problems that are faced by Big Data.
14. Manageability: The management of Hadoop is very easy as it is just like a tool or program which can be programmed. But Big Data is not so easy one to manage or handle as it is called Big Data mainly because of the amount, quantity, volume, variety of data set. It is challenging to manage and process this kind of data and can only be done by Large Companies with large resources.
15. Applications: Big Data can be used for Weather forecasting, prevention of cyberattacks, the self-driving car of Google, Research and Science, Sensor Data, Text Analytics, Fraud Detection, Sentiment Analysis, etc. Hadoop can be used to handle complex data easily and with speed, processing data in realtime for decision making and optimization of business processes.
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