What is Big Data Analytics?

What is Big Data Analytics?

Big Data is a huge and complex volume of data. created by consumers in various activities that cannot be stored, processed, or analyzed using traditional tools There are sources of information all over the world. Some of the biggest sources of information are social media platforms and networks. Let's use Facebook as an example.

There are 3 types of Big Data:

-Structured data

-unstructured data

-Semi-structured data

Big Data Analytics is the use of insights within Big Data, such as hidden patterns. unknown relationship Market trends and customer needs Then it is analyzed and processed to be used as a reference for decision-making, development, or production of goods and services.

Why is Big Data Analytics important to today's businesses?

Big Data analytics is powering everything we do online. In every industry, for example, various platforms have millions of users every day. Each platform recommends products, songs, etc. based on viewing, listening, liking, sharing, and search history. which is a result of using Big Data Analytics

Uses and examples of data analysis

There are many methods of analyzing big data. Can be applied as appropriate for each business, such as analysis to understand customer behavior Predict future trends, improve marketing campaigns, etc., which does not have a fixed method of application.

Types of Big Data Analytics

1. Descriptive analysis To summarize historical data into a format that people can easily read. It helps in generating reports such as revenue, profit, company sales, and more.

2. Analysis diagnosis To understand what caused the problem in the first place, techniques such as deep dives and data recovery can be used.

3. Predictive analysis To consider past and present information to predict the future.

4. Prescriptive analysis to determine how to solve a specific problem Functional analytics works with both descriptive and predictive analytics.

Big Data Analytics steps

1. Evaluate and identify the reasons and goals behind the analysis.

2. Identify a variety of information sources.

3. Filter out damaged data

4. Extract data that is incompatible with the tool and convert it to a compatible format.

5. Combine data of the same type

6. Analyze data

7. Visualizing data

8. Display final data

Big Data Analytics tools

-Hadoop is used to store and analyze data.

-MongoDB is used with data sets that change frequently.

-Talend is used for data integration and management.

-Cassandra is used to create distributed databases used to manage large amounts of data.

-Spark - used for real-time processing and analysis of large amounts of data.

-STORM open source real-time computing system

-Kafka, a distributed streaming platform used for fault-tolerant data storage.