Types of Big Data
Big data can be classified into different types based on various factors. some of the most common types of big data include:
Structured data:
structured data refers to data organized in a specific format, making it easy to analyze and process using traditional database tools. Structured data typically consists of tables, rows and columns and can be easily searched, queried and manipulated using SQL (Structured Query Language) or other database management systems.
Examples of structured data include financial data, inventory data, customer data, and transaction data. Structured data is often used in business intelligence and analytics applications, where it can be analyzed to uncover patterns, trends and insights that can inform business decisions.
Structured data has several advantages over unstructured and semi-structured data. This includes:
Easy to analyse:
Structured data can be easily analyzed using traditional database tools, which makes it easier to uncover insights and trends.
Efficient Storage:
Structured data can be efficiently stored and indexed, which makes it faster and easier to retrieve.
Easy to integrate:
Structured Data can be easily integrated with other systems, making it easier to share and exchange data.
High Data quality:
Structured data is often of high quality since it is typically entered into a database using strict rules and standards.
Easy to automate:
Structured data can be easily automated, making it possible to perform complex calculations and operations automatically.
some of the common tools and technologies using for managing structured data include relational database management systems (RDBMS), data warehousing, data mining, and business intelligence(BI) tools.
Unstructured Data:
Unstructured data refers to data that does not have a specific format or organization, making it difficult to analyze using traditional database tools. Examples of unstructured data include text documents, images, videos, social media posts, and audio recordings.
Unstructured data is typically generated in large volumes and at a high velocity and it can come from a variety of sources . This makes it difficult to store, manage and analyze using traditional data management systems.
Despite its challenges, unstructured data can provide valuable insights into customer behaviour, market trends, and other business-critical information . Analyzing unstructured data can be particularly valuable in areas such as sentiment analysis,image and video analysis and natural language processing(NLP).
There are several tools and technologies available for managing and analyzing unstructured data. These include:
Content management systems:
content management systems(CMS) provide a way to organize and manage unstructured data, such as text documents and images.
CMS
Text analytics:
Text analytics tools can be used to analyze unstructured text data, such as social media posts and customer reviews. These tools can identify trends, sentiment and other insights from large volumes of text data.
Text analytics
Comments
Post a Comment