Big Data Tutorial

Big Data Tutorial

Big Data helps in the ongoing collection of those data and their systematic processing. Learn the fundamentals to begin your career in big data processing through this big data tutorial.

Introduction to Big Data

“Big data” refers to the collection of massive volumes of data that are too big to handle using conventional computer techniques. The phrase also encompasses the different frameworks, tools, and methodologies.

History of Big Data

• The first individual to apply statistical data analysis was John Graunt.
• Later, in the early 1800s, the study of statistics expanded to include the collection and analysis of data. 
• The issue of copious data was first brought to global attention in 1880.

The Three V’s of Big Data

Big data Big data can assist groups and companies in carrying out many tasks on a single platform. These are big data sources:

Volume: Quantity is the key to big data. data volumes that could reach previously unthinkable heights.

Velocity: Velocity measures the rate at which data enters the system. We will receive certain data in bunches, but other data will come in spurts.

Variety: Data can be given in non-traditional formats, such as text, video, PDF, and graphics, through technology, like wearables and social media.

Types of Big Data

There are three types of big data. They are displayed as follows:

• Structured Data
• Semi-Structured Data
• Unstructured Data

Structured Data

Structured data is organized so that it can be easily accessible and used by both computers and humans. It has a specific data model, a clear structure, and a consistent order. Structured data is kept in tabular form, which is just rows and columns. 

Example: MS Excel and Database Management Systems (DBMS).

Semi-Structured Data

Although unstructured data shares some characteristics with structured data, much of this kind of data is unstructured and does not adhere to the formal structure of data models like RDBMSs. 

Example: A CSV (Comma Separated Values) file.

Unstructured Data

Unstructured data is a category of data that lacks a set structure. It is dynamic and doesn’t follow a set format. It might, however, sporadically contain information about data and timing. Example: Pictures, audio files, etc.

Functioning of Big Data

To inform data-driven choices, big data entails identifying trends, patterns, and correlations among enormous volumes of raw data.

Data Collection: Collecting both structured and unstructured data from a range of sources, including mobile apps, cloud storage, IoT sensors installed in stores, and more.

Data Organization: When data is collected and stored, it must be properly organized, especially if it is large and unstructured, for analytical queries to produce accurate results.

Data Cleaning: All data must be properly formatted, and redundant or superfluous data must be eliminated or accounted for.

Data Analytics: Converting massive amounts of data into a useful format takes time. When big data becomes available, advanced analytics techniques can turn it into meaningful insights.

The techniques used in big data analytics are as follows:

• Data mining sorts through massive datasets to find patterns and connections by identifying anomalies and creating data clusters.
• Predictive analytics examines future estimates using past data from a company to identify possible risks and possibilities.
• Deep learning mimics human learning processes by using layers of algorithms to find patterns in even the most complex abstract data. 

The Sources of Big Data

The following are the sources of big data:

Black Box Data: It includes voices from the flight crew, microphone recordings, and information on aircraft performance.

Social Media Data: It has data from websites like Facebook, Instagram, Twitter, and Google+.

Stock Exchange Data: It contains stock exchange data on customer decisions to buy and sell shares.

Power Grid Information: It contains data about specific nodes, like use details.

Transport Data: It contains a vehicle’s model, capacity, availability, and travel distance.

Search Engine Data: Large databases are where search engines obtain their information. 

Use Cases of Big Data

Here are the applications of big data:

Improved Customer Acquisition and Retention: Big data gives companies insights into the interests of their customers, how they use their products and services, and the reasons for their customers’ discontinuations.

Complete View of the Product: Businesses frequently use big data to develop dashboard applications that provide a 360-degree view of the customer.

Improved Cybersecurity and Fraud Prevention: Businesses employ big data analytics to identify patterns of fraud or abuse, identify anomalies in the behavior of their systems, and apprehend criminals.

Improvements in Forecasting and Pricing Optimization: It helps companies make the required corrections, averting expensive mistakes farther down the supply chain.

Examples of Big Data

Here, we explain how we can utilize big data in real-time.

Transportation: GPS smartphone apps, which let us get from point A to point B as quickly as possible, require a tonne of transportation data. GPS data can be collected from government organizations and satellite images.

Advertising: Advertising and marketing strategies have typically focused on certain customer segments. 

Banking and Financial: It can be implemented for fraud detection and risk management.

Government Operations: The IRS and Social Security Administration are among the government agencies that can spot false tax evasion and disability claims. 

Benefits of Big Data

The following are the advantages of big data:

• You can leverage actionable data from big data to interact with your customers directly and in real-time.
• You can redevelop the goods and services you market thanks to big data.
• You can experiment with multiple CAD (computer-aided design) image variations using big data to see how little adjustments impact your workflow or final output.
• You’ll always be one step ahead of your rivals with predictive analysis.
• Large data sets are useful for protecting data. Mapping your company’s data landscape using big data tools facilitates the investigation of internal dangers.

Components of Big Data

Several interconnected components are essential for managing and extracting value from big data. These components include:

• Data sources
• Data collection
• Data storage
• Data processing
• Data analysis
• Data visualization

Future Trends of Big Data

The growth of big data continues to influence many industries. Prospective patterns encompass:

Edge computing: It involves processing data at the network’s edge, closer to its source, to reduce latency and enhance real-time decision-making.

Hybrid and Multi-Cloud Solutions: To effectively handle and process large amounts of data, organizations will make use of both on-premises and cloud-based resources.

Integration of AI and Machine Learning: More sophisticated AI and machine learning algorithms will produce automated decision-making and predictions that are more accurate.

Data Security and Privacy: Tighter laws and cutting-edge security protocols will handle data security and privacy issues.

Conclusion

We hope that we can help you understand the fundamentals of big data through this big data tutorial. Excel in big data processing through our big data training in Chennai at SLA.