Big Data is a process that is generally used when traditional data mining and handling techniques are unable to give the meaning and insights of the given data. So, big data process can give you the information out of data. The relational database engines aren’t capable to handle unstructured, semi-structured, time sensitive or very large data. Big data can handle such data by massive parallelism on easily-available hardware.
Explaining Big Data:
The term big data is constantly evolving the world we live in. The more things change and time passes, the more we need to record this as data. For instance, weather forecaster collects data from around the world to determine local environments, regional environments, and the global environment. Logically, it makes sense that local and regional environments affects the global environment. But, we can make this simpler. Weather data gives an idea of big data in which we need real-time processing for massive amount of data.
Processing information from this type of data explains why big data has become so important:
- Data collected is very large, semi-structured, or unstructured. So, it requires different processing and storage than that available in traditional relational databases.
- Computational power available today provides great opportunities to process big data.
- The Internet has balanced data, and has also steadily increased more and more raw data.
In terms of Databases, raw data has no value unless it is processed. Data needs to be processed into information. However, there’s a problem lying with big data. Is it sure that processing data from native object format to a meaningful insight will always be worth? Now, let’s consider the example of weather forecasting. Predicting weather and weather forecasts do have value. But, could these weather forecasts outclass the costs of processing all the real-time data into a weather report?
Evolution of big data:
Ultimately, we can characterize big data by 3Vs, namely: extreme volume of data, wide variety of data, and the velocity of data.
- Volume: Organizations depends on various forms to collect data. It includes relative sources such as business transactions, social media responses, and direct machine-to-machine data. Earlier, storage was an issue to deal with, but now the advancement of technologies has reduced the burden.
- Variety: Data is available in variety of forms like structured, unstructured, or semi-structured data. Traditional databases gets data in all these forms like text files, email, audio, video, business transactions, etc.
- Velocity: The path of data and its speed is unparallel. But, it has improved in timely manner. Sensors, RFID tags, and smart metering creates a need to deal with torrents of data at real-time.
Today, big data is not just data, it has become a particular subject. It involves various tools and techniques along with variety of frameworks.
It allows to analyze the data completely for better decisions and strategies for the betterment of organization.
Categories Under Big Data:
Big data works on different data produced by devices and their applications. Here are some categories of use of big data:
- Black Box Data: It is used by air crafts’ technical ground staff to store a large amount of information. It includes the conversation among crew members, communication with the department, or any alert messages passed.
- Social Media Data: Social media platforms such as Facebook and Instagram contains huge amount of information posted by millions of people around the world.
- Stock Exchange Data: It contains the complete information about in and out of business transactions. It stotrs all the details about buy, sell, buyer and seller in terms of share with different companies.
- Power Grid Data: It contains information about a particular node in terms of base station.
- Transport Data: It stores all the information about various transport sectors such as model, amount, capacity, time, distance, availability of vehicle, etc.
- Search Engine Data: Search engines need to store a large amount of data requests and information from different sources of database.
Importance of Big Data:
The importance of big data can be determined on the basis of how you deal with the data you own. Data fetched from various sources can be analyzed to solve the problem in terms of:
- Reduced Time
- Cost cutting
- Research and Development
- Better decision-making
Using big data with smart analytics can have a great impact on your business strategies. You can have the following benefits:
- Find the root cause of problems, defects, issues, and causes in real time operations.
- Better understand customer’s habit of purchasing goods at the point of sale.
- Change or adjust strategies in real time.
- Detect the problem or issues before it endangers your organization.
Users of Big Data Technology:
Big data technology is used by number of business organizations as well as government organizations. Organizations such as banking, government, education, manufacturing, retail, health care, etc needs to store large amount of real time data. All these organizations need to manage and analyze big data to develop better services and offerings to its customers. They need to minimize risks, deal with real time data, and also take steady decisions to avoid huge losses.
Utilisation of Big Data
Converting big data into small and specific parts of information gives a straightforward idea to organizations. They can easily understand what their customers want, what can help in cost cutting, and what methods and decisions can improve their organization. Hence, they can get specific information on every aspect of their data and can act accordingly. There is a huge demand for Big Data technologies in the field of IT.
Big Data Technologies:
Different big data technologies are available in the market from number of vendors such as IBM, Microsoft, Amazon, etc. You must pick up a particular technology depending on your use.
Operational Big Data:
It includes applications that provide interactive and real time capabilities where large amount of data is constantly captured and stored. For example: MongoDB and NoSQL.
Analytical Big Data:
It includes systems that provide analytical capabilities for complex analysis using massively parallel processing database systems. Also, MapReduce system provides a unique method for analyzing data that SQL can’t deal with.
Big Data will always remain important for organizations and its betterment. Hence, its demand will always continue in the IT field.