What is Big Data

Big Data

Big data more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.

It  refers to data that is so large, fast or complex that it’s difficult or impossible to process using traditional methods. The act of accessing and storing large amounts of information for analytics has been around for a long time.

Today, big data has become capital. Think of some of the world’s biggest tech companies. A large part of the value they offer comes from their data, which they’re constantly analyzing to produce more efficiency and develop new products.

These days, data is constantly generated anytime we open an app, search Google or simply travel place to place with our mobile devices. The result? Massive collections of valuable information that companies and organizations need to manage, store, visualize and analyze.

Traditional data tools aren't equipped to handle this kind of complexity and volume, which has led to a slew of specialized big data software and architecture solutions designed to manage the load.

Big data platforms are specially designed to handle unfathomable volumes of data that come into the system at high velocities and wide varieties. These big data platforms usually consist of varying servers, databases and business intelligence tools that allow data scientists to manipulate data to find trends and patterns.

How Does Big Data Work

Big data requires specialized NoSQL databases that can store the data in a way that doesn't require strict adherence to a particular model. This provides the flexibility needed to cohesively analyze seemingly disparate sources of information to gain a holistic view of what is happening, how to act and when to act.

Analytical systems are more sophisticated than their operational counterparts, capable of handling complex data analysis and providing businesses with decision-making insights. These systems will often be integrated into existing processes and infrastructure to maximize the collection and use of data.

In most cases this process is completely automated – we have such advanced tools that run millions of simulations to give us the best possible outcome. But to achieve that with the help of analytics tools, machine learning or even artificial intelligence, you need to know how Big Data works and set up everything correctly.

you can use the valuable insights that this data provides for making marketing decisions about your product and brand. Brands that are utilizing Big Data have the ability to make faster and more informed business decisions. Using all the information you have for your customers, you can make your product more customer-centric and create the content that your customer wants or personalize their journeys. Making decisions when you have all the information you need is easier, right.

There Are Three Types Of Big Data

1. Semi-Structured Data
2. Structured Data
3. Unstructured Data

1. Semi-Structured Data:

This can be inherent data collected, such as time, location, device ID stamp or email address, or it can be a semantic tag attached to the data later.

This data has structure but is not the same as the data model’s structure and lacks the rigid/fixed schema with types of data structured unstructured semi-structured. The fact that such does not reside in the rational database due to its organization properties makes its analysis easier

2. Structured Data:

Structured data conforms to a tabular format with relationship between the different rows and columns.

Structured data follows schemas: essentially road maps to specific data points. These schemas outline where each datum is and what it means.

Common examples of structured data are Excel files or SQL databases. Each of these have structured rows and columns that can be sorted.

3.Unstructured Data:

Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. To gain anything resembling useful information, the dataset needs to be interpretable. But the effort can be much more rewarding than processing unstructured data’s simpler counterpart This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in structured databases.