In today’s technology-driven world we create a lot of data every minute. But the fact is that 90% of today’s available data has been generated in the last couple of years. However the terminology “Big Data” has been around for quite a time, thanks to ‘O’Reilly Media’ who launched it back in 2005. Before diving into the ocean of big data, let’s first get introduced to what is data, what is big data, and how big data is processed i.e. Big Data Ingestion.
What is data
Data is the information that has been translated in the form of electrical signals. These signals are ideal for on-boarding or processing and ultimately stored in some database. As today’s computers are capable to process binary digital systems, these electric signals represent the same binary systems.
What is Big data
Big Data is referred to as a huge collection of multiple types (text, graphics or media, etc.)of data and which is increasing per minute. As the size is very large it has a different level of complexity. And this level of complexity is so challenging that traditional data management systems (DMS) fail to execute or store it properly. The big data ingestion process comes to the rescue in such scenarios. Before discussing how exactly big data ingestion happens let’s discuss some examples of big data, its types, and its characteristics.
Examples of Big Data
Think about all the details of the Coronavirus pandemic that is happening worldwide. Imagine all the records of people who have died, who are suffering and who have recovered so far, records of the variants of the viruses discovered so far. That would be an example of big data.
Another example will be the amount of data that Youtube generates every day. Statistics say 500 hours of video are uploaded to YouTube every minute worldwide (Tubefilter, 2019). That’s 30,000 hours of video uploaded every hour. And 720,000 hours of video uploaded every day to YouTube.
The above examples can give us a brief picture of what big data is.
Types of Big data
There are three types of big data and they are as follows:
1. Structured data
Any data that can be put away, accessed, and handled within the shape of a settled format is referred to as ‘structured’ data. Over the period, ability in computer science has accomplished more prominent victory in creating procedures for working with such kind of data (where the arrange is well known in development) additionally determining esteem out of it.
2. Unstructured Data
Any data with an obscure shape or structure is classified as unstructured data. Presently day organizations have tons of data accessible with them but shockingly, they don’t know how to determine esteem out of it since this data is in its crude shape or unstructured format.
3. Semi-structured data
The terminology ‘Semi-structured data’ represents the combination of both structured and unstructured data. We can observe the semi-structured data as a form of structured data however it is not recorded in the Relational Database management systems.
Characteristics of Big data
There are four characteristics of Big Data. Let’s discuss them in a jiffy.
The title Big Data itself is related to a gigantic measure. Moreover, whether a piece of specific information can be considered as Big Data or not, is dependent upon the volume of information.
Variety alludes to heterogeneous sources and the nature of data, both organized and unstructured. This Variety of unstructured data postures certain issues for capacity, mining, and analyzing information.
This alludes to the irregularity which can be appeared by the data at times, hence hampering the method of being able to handle and oversee the information viably.
Velocity refers to the speed of generating a huge amount of data. It deals with the pace that is needed to meet the demands of complex business scenarios.
How Big data is processed
Big data ingestion assembles information and brings it into a data processing system where it can be put away, analyzed, and accessed. Ordinarily, this data is unstructured, comes from numerous sources, and exists in assorted formats. Depending on the source and goal of the data, data can be ingested in real-time, batches, or both (called lambda design). To create the data useable to the goal framework, the data will require a few sorts of conversion or change. Compelling data ingestion starts with the data ingestion layer. This layer forms incoming data, prioritizes sources, approves each record, and courses information to the right goal. It closes with the data visualization layer which presents the information to the client.