What Is Big Data?

Introduction:

Big Data describes large, difficult-to-manage volumes of data. This data exists in the form of structured and unstructured data. Data has an intrinsic value. This data contains great variety, exists in huge volumes, variabilities, and more velocity. Three Vs is another name for it. So, This data grows over time. This massive volume of data can help us address many business problems one can not tackle before. Generally, Big Data analytics is the process of examining a huge amount of data. Because of its complexity, traditional data management ways cannot store and process it.

A few examples of Big Data are:

• NYSE
• Social Media
• Personalized Marketing
• Aviation Systems
• Email
• Medical Records
• Customer Database
• Product Development
• Customer Experience
• Predictive Maintenance

The 3V’s of Big Data:

• Velocity: It means how fast is the data delivered. Some data comes in real-time while some get delivered in batches. All platforms have different ways of delivering data.
• Variety: We can store data in many different varieties. Once the database like excel, CSV, etc contains the data, a non-traditional form can show it as per need. Generally, These non-traditional forms include videos, images, text, pdf, etc.
• Volume: The Internet can create 2.5 quintillion bytes of data per day. Because of the continuous usage of big data, its volume is increasing. We expect data to double in the next 2 years.

Value and veracity are the other two V’s of big data. Big companies use big data to offer better services and develop new products.

Types of Big Data:

• Structured Data: Data that exist in structured, accessed, and manipulated form. These data exist in a fixed format. It is organized with dimensions defined by fixed parameters.
• Unstructured Data:  Unstructured data exists in an unknown form or structure. Such data poses many challenges deriving value out of it.
• Semi-structured Data: This data contains both forms of data. We can observe semi-structured data in a structured form but it is not well defined. Markup languages like XML allow text data to be defined by its own content.

Improtance of Big Data:

The importance of big data is not how it revolves around. But how one uses it.

Combining this big data with high-performance analytics, we can achieve many business tasks. It is as follow:

• Determining the root cause of failure and defects in real-time.
• Calculation of risk portfolio.
• Spotting faults and anomalies faster and with more accuracy than the human eye.
• Fraud behavior detection.
• Sharpening deep learning models’ capacity to classify the changing variable.

How it Works:

This work involves three key actions:

• Integrate: Big Data brings together data from many different sources and applications. Traditional data integration is not up to the task. During integration, one needs to bring data, process it, and organize it in the correct format. And provide that organized data to the business analysts.
• Manage: We can store data in the cloud, on-premises, or both. Moreover, We can store data in any form and bring your desired processed data on-demand basis.
• Analyze: It is a process of uncovering trends, patterns, and correlations between data.

Identifying big data sources is also an important and tedious task. Streaming data comes from IoT and other devices like smart cars, digital watches, and more. We can analyze this data and decide whether to store it or not.

Social Media data stems from interactions on Facebook, YouTube, Instagram, LinkedIn, and more.  Generally, It stores a vast amount of big data in the form of audio, text, images, videos, and more. It is useful in marketing, sales, and support functions. However, Such data is usually semi-structured or unstructured. Thus, it becomes difficult to analyze and consume.

Other sources of big data are data lakes, cloud data sources, suppliers, and customers.

Modern computers provide high speed, power, and flexibility to manage this data. Along with this, managing and integrating this data is also of primary importance. Building data pipelines, data quality, security, transparency are also challenges. So, Big Data feeds today’s advanced technologies like AI and Machine Learning with data.

Well-managed, trusted data leads to trusted analytics and trusted decisions. Generally, Data-driven organizations usually outperform their competitors. Such companies are more operational and more profitable.

Advantages of Big Data Processing:

• Business uses this outside data while making decisions.
• Improved customer services.
• Better operational efficiency.

Data technologies and data warehouses help organizations to offload accessed data.

New discoveries are possible because of the most extensive data sets.

Big Data Technologies:

This technology consists of 4 fields:

• Data Storage
• Mining of Data
• Data Analytics
• Data Visualization

Types of Big Data Technologies:

This software analyzes, processes, stores, and extracts data from huge volumes of data.

These technologies include:

• Operational Big Data Technologies: This is all about day-to-day operations. Online ticket booking, shopping, social media, and employee management system are examples.
• Analytical Big Data Technologies: It is more complex than operational data. Generally, Decisions making of actual performance and crucial business take place in this sector. Moreover, Stock marketing, space mission, weather forecast, medicine are a few of its examples.

Conclusion:

Big Data has tremendous potential. Big Data is increasing in demand and new technologies come into the picture. So, These technologies assure harmonious work with perfect supervision and security. Big Data is not only about the huge volume of data. But, It is a method of providing services and opportunities to use new data.

Big Data solution includes all data realms which include structured and unstructured data. Moreover, Resource management is critical for maintaining the entire data flow. So, It includes integration, database summarization, and modeling. Generally, Business uses advanced analytics to gain new insights from raw data. However, Advanced analytics includes data mining, statistics, machine learning, predictive analytics, and text analytics.

Hadoop is an open-source distributed data processing and storing framework for big data. Hadoop is built with extensive networks of data clusters and clusters.

Also Read:

Whаt Is The Internet Of Things (IоT)?

Importance Of IoT Devices In Daily Life

Cloud App Security: Preventions Developers Must Know

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What Is Data Security? Why it is necessary?