Data processing is an essential part of data science. It involves operations like data extractions, manipulation, analysis, and storage. It is important as the data collected in raw format is not usable.
Data is being generated every time you interact with the internet. It could be for bank transactions, Instagram posts, shopping on an e-commerce platform, or just browsing the internet. Even while reading this blog, data is being generated. Companies analyze this data to bring you a customized user experience. Without Data Processing, the companies lose their competitive edge in the market.
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What is Data Processing?
The process of transforming raw or unprocessed data into a clean and readable format is called data processing. When we say data is transformed, we mean that we will be applying multiple data operations, like removing null data, sorting it, filtering it, applying a dataframe, etc., to make the raw data more readable. Usually, data processing is done by either a Data Engineer or a Data Scientist.
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Need for Data Processing
Data processing is required to transform unprocessed data into information that can be used in decision-making. It helps them spot patterns and trends and make educated decisions. After data processing, the processed data can be used to track consumer trends, measure consumer behavior, and create customer segments. With its help, businesses will be able to customize their goods and services according to customer preferences. It will increase sales and customer satisfaction.
Let’s take the example of Zomato. For Zomato, delivering the food to the location is the most important part of their workflow. To accomplish this, they use the previous data to analyze and predict the traffic for the next weekend. This helps them manage delivery agents for hassle-free deliveries.
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Stages of Data Processing
The Data Processing cycle is a set of practices used to transform unusable data into information.
There are six stages of Data Processing:
- Data Collection: Data collection is the first stage of Data processing, wherein data is collected from various valid sources. The source of the data must be trustworthy, as the outcome or inferences drawn from the data depend on the quality of the generated data. Raw data can contain null values, user behaviors, some symbols, website cookies, and all other impurities.
- Data Preparation: Data preparation also called pre-processing or Data Cleaning, it is the second stage of data processing. The main goal of this stage is to bring out the best data for business intelligence. In this stage, we get rid of bad data (redundant, incomplete, or incorrect.) by using multiple transformation operations like filtering, sorting, and multiple data manipulation techniques.
- Data Input: Data input is the third stage of data processing. In this stage, you will see the raw data taking a readable form for the first time. Here, data is usually converted into a readable format using programming languages like Python or R, and then the data is stored in some data warehouse like Redshift or some CRM like Salesforce or Zoho.
- Processing: Processing or data processing is the fourth stage. In this stage, we use multiple machine learning algorithms along with frameworks like Spark, Pyspark, and libraries like Pandas, Koalas, etc to perform data transformation. The process or steps are subject to change based on the data source and its intended use.
- Data Output: It is an interpretation stage wherein the data is checked and visualized to see if further processing is required or not. At this stage, the data is made available to the members of the organization to perform analysis on the data.
- Data Storage: Data storage is the last stage in the data processing lifecycle. In this stage, the processed data is stored along with the metadata on some data lake or S3 glacier. It can be easily accessed by the members of the organization for further use. Storing the data properly also allows us to retrieve the data and use it as a data input during the next data processing cycle.
Types of Data Processing
There are several types of data processing, based on the source of the data, at what interval the data is processed, and how the data is processed.
Here are a few of them:
| Types of Data Processing | Description |
| Batch Processing | Processing huge amounts of data periodically in batches. Example: Payroll System. |
| Real-Time Processing | Data is processed as soon as it is received and given as input. Example: Stock Market Analysis. |
| Online Processing | Data is processed while the user is interacting with the system. Example: Bank Transaction. |
| Multi Processing | Data is processed using two or more CPUs. Example: Weather Forecasting. |
| Distributed Processing | Data is distributed and processed across multiple interconnected computers or nodes. Example: Big Data Processing. |
Examples of Data Processing
Modern businesses and organizations depend heavily on data processing because it enables them to draw market trends and insights that help them grow.
Here are a few examples from the real world where data drives a company towards its goal through better decision-making.
1. Let’s take the example of Swiggy. Swiggy is a doorstep food delivery app in India. It identified a problem statement that a customer, while ordering, has to set a delivery address and then go through several serviceable restaurants, followed by each of their menus, just to get an answer to “What to order?”.
To overcome this problem and ease the process for the customer, Swiggy introduced Swiggy Suggests an Artificial Intelligence that will suggest cuisines to customers based on previous data. Here, data processing must have played a very important role because, to achieve that level of accuracy, data has to be in its best form.
2. Next, let’s take an example of Netflix. Netflix is an OTT platform for entertainment which is available worldwide. It has one of the world’s best recommendation systems, that recommends media to its users. According to Netflix, 80% of the content watched by a user comes from recommendations. This level of accuracy wouldn’t have been possible without a well-trained machine learning model.
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The Future of Data Processing
The amount of data generated by technology and companies is continuing to expand tremendously, and the data being generated is becoming more powerful, complex, and huge. Therefore, a lot of resources are required to store and process it.
The future of data processing is cloud computing, wherein we will be using the services of public clouds like Microsoft Azure, Amazon Web Services (AWS) and Google Cloud Platform (GCP) for data processing. Previously, we used some on-premise systems to process this huge amount of data, but it was not feasible as it cost a lot.
Technologies like the public cloud will help reduce cost and improve the efficiency of the life cycle. Public clouds are affordable, and can be scaled easily as the company grows in size.
Technologies like distributed processing which includes Hadoop, MapReduce, Spark are continuously evolving. Therefore, cloud-based distributed processing is the future.
Through the use of these technologies, data processing will be more accurate, efficient, and automated, allowing quicker and wiser decision-making.
Conclusion
I hope this blog has been a great help to you in understanding data processing. Best of luck!
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