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1. What do you mean by the term or concept of Big Data?
Big Data means a set or collection of large datasets that keeps on growing exponentially. It isn’t easy to manage Big Data with traditional data management tools. Examples of Big Data include the amount of data generated by Facebook or the Stock Exchange Board of India daily. There are three types of Big Data:
- Structured Big Data
- Unstructured Big Data
- Semi-structured Big Data
2. What are the characteristics of Big Data?
The characteristics of Big Data are as follows:
- Volume
- Variety
- Velocity
- Variability
Where,
Volume means the size of the data, as this feature is of utmost importance while handling Big Data solutions. The volume of Big Data is usually high and complex.
Variety refers to the various sources from which data is collected. It refers to the types, structured, unstructured, semi-structured, and heterogeneity of Big Data.
Velocity means how fast or slow the data is getting generated. Big Data velocity deals with the speed at which the data is generated from business processes, operations, application logs, etc.
Variability, as the name suggests, means how differently the data behaves in different situations or scenarios in a given period.
3. What are the various steps involved in deploying a Big Data solution?
Deploying a Big Data solution includes the following steps:
- Data Ingestion: As a first step, the data is drawn out or extracted from various sources to feed it to the system.
- Data Storage: Once data ingestion is completed, the data is stored in either HDFS or NoSQL database.
- Data Processing: In the final step, the data is processed through frameworks and tools such as Spark, MapReduce, Pig, etc.
