Understanding Data Processing | iCert Global

A tremendous amount of data is generated every day through activities like online shopping, social media, and payments. Statista states that by 2025, the world will have generated 175 zettabytes of data. With more and more people accessing the internet, it is extremely important to understand and handle this data. Data processing assists in converting raw data into useful information.

What is data processing?

Raw data is of no value to any company. Data processing is when we process raw data and make it valuable. Data scientists and engineers usually do it in phases. They obtain the raw data first, clean and organize it, sort it, process it, analyze it, store it, and present it in an understandable form.

Data processing is important. It helps businesses make better decisions. It also helps them stay ahead of the competition. By transforming data into usable formats like charts, graphs, and reports, employees can better understand and use the information.

There are typically six principal steps in the data processing cycle:

Step 1: Collection

The initial step in data processing is obtaining raw data. The type of raw data obtained matters as it affects the outcome. Obtaining data from reliable sources is required to provide results that are credible.

Raw data may have:

• Turnover statistics
• Website data
• Company profit/loss statements
• User behavior

Step 2: Preparation Data preparation, or cleaning, is sorting and classifying the raw data to eliminate unwanted or erroneous information. It is looking for errors, duplicates, or missing information.

Step 3: Input Here, the cleaned and prepared data is converted into a computer-readable format and fed into the system. Data may be entered manually, scanned from paper documents, or even uploaded from digital sources such as APIs or databases

Step 4: Data Processing

We employ a number of ways and methods by means of implementation through this step. Machine learning and AI assist us with processing the data and obtaining desired results.

Step 5: Output After processing the data, it is displayed to the user in a easy-to-grasp manner, like graphs, tables, reports, or videos. The output can be saved and used in the next data processing cycle.

Step 6: Storage The last step is to save the metadata and data for future reference. This is convenient for retrieving information later, and it can also be utilized in the subsequent data processing cycle.

Data Processing Methods

There are five separate methods of processing data: manual, mechanical, electronic, distributed, and automatic. Let's discuss them all a little further:

1. Manual Data Processing:  In manual data processing, everything is done by hand. People gather, filter, sort, and calculate data without the use of machines or software.

2. Mechanical Data Processing : Mechanical data processing utilizes tools or equipment like calculators, typewriters, and printing presses. It is quicker and less error-prone than manual work. However, it can still be tedious with gigantic data.

3. Electronic Data Processing :  Through this, the most recent technology like computers and data processing software is used to process data.

4. Distributed Processing : Distributed processing refers to dividing the job among multiple devices or computers. This is faster and more reliable since there are multiple systems involved.

5. Automatic Data Processing : Automatic data processing employs software to carry out procedures automatically. It accelerates, reduces errors, and enables individuals to focus on significant tasks rather than repeating the same thing repeatedly.

Data Processing Tools

Some of the most commonly used tools that organizations use in order to handle, process, and analyze massive amounts of data are as follows:

1. Apache Hadoop : is a computer program that helps in storing and handling much information on a very large number of computers. Hadoop can work with big data and do so effectively.

2. Apache Spark : Apache Spark is another fast and free tool. It is faster than most other tools since it works on data in memory and not on the disk.

3. Google BigQuery : Google BigQuery is a web-based tool through which users analyze big datasets quite quickly. Huge data can be processed quickly with it, that is, within seconds. Google BigQuery gets along well with other Google Cloud services too.

4. Talend : Talend is a business solution that makes it possible to connect and work with data from any place. Talend simplifies data cleansing, processing, and data movement.

5. Microsoft Azure : Data Factory Microsoft Azure Data Factory is a cloud-based service to assist businesses in designing and orchestrating data pipelines. Both real-time data and batch data can be processed by it, and it is well-suited for use with other Microsoft Azure products.

Examples of Data Processing

Data processing is taking place everywhere in the world, but we are not even aware. Some of these are actual applications of data processing:

1. Stock Trading Platforms Stock trading platforms process real-time market information, verifying numerous transactions per second.

2. Personalization in E-commerce Online stores study customer browsing and buying history to suggest products. This improves the shopping experience and boosts sales.

3. Ride-Hailing Apps Ride-hailing  apps such as Uber use geolocation and real-time traffic information. They utilize the data to determine best routes, apply dynamic pricing, and dispatch drivers to riders efficiently and quickly.

How to obtain Big Data certification? 

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Conclusion

Data is increasing at a very fast pace, and the demand for professionals to process and manage it is increasing. Data processing is becoming faster and efficient with the development of technology such as cloud computing. iCert Global's Professional Certificate Program in Data Engineering provides you with hands-on training to achieve your goals in data engineering. It is a wonderful chance to build your career in data engineering.

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