- Data Engineering - Home
- Data Engineering - Introduction
- Data Engineering - Data Collection
- Data Engineering - Data Storage
- Data Engineering - Data Processing
- Data Engineering - Data Integration
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- Data Engineering Useful Resources
- Data Engineering - Useful Resources
- Data Engineering - Discussion
Data Engineering - Data Processing
Raw data is not useful until it is processed data into usable information. Data processing involves collecting, sorting, analyzing, and then presenting raw data in a readable format. This process is typically carried out by the data scientists and data engineers.
Data processing is a crucial organization that develops better business strategies and gain more competitive storage processing. By converting data into formats like crafts, graphs and documents, employees can easily access the information.
Stages of Data processing
Data Processing involves various stages to transform raw data into meaningful information. Here are some main ideas of data processing −
Preparation: Data preparation involves organizing, cleaning, and formatting raw data. Irrelevant information is filtered out, errors are corrected, and the data is structured to specifies the efficient analysis in a subsequent processing stage.
Input: During the data input stage, the prepared data is entered into a computer system. This can be done automatically or manually, depending on the data type and the system used.
Data Output: The results of processing the data is presented in a comprehensible format during the data output stage. This includes the reports, graphs, charts and other visual representations that facilitate understanding and decision making based on the analyzed data.
Collection: This process begins with gathering raw data from various sources. This stage is difficult for further processing, ensuring a relevant dataset for analysis. Data can be collected through databases and sensors.
Types of Data Processing
Here are the five types of data processing −
Automatic Data processing: Automatic Data Processing(ADP) uses computers and software to automate data processing tasks. This includes methods like batch processing and real-time processing, which manages large amounts of data efficiency with minimal human environment.
Real-time Data processing: Real-time data processing handles data as soon as it is generated. This is essential for time-sensitive applications, providing instant responses and updates. This is commonly used in the financial transactions and monitoring systems.
Electrical Data Processing: This uses the computer to process and analyze data, which improves the speed and accuracy over manual and mechanical methods.
Manual Data processing: This involves humans to process the data without machines or electronic devices. This includes tasks like manual calculations, recording and sorting that makes it a time consuming process.
Mechanical Data Processing: This process uses devices like punch cards and mechanical calculators to process the data. This is not similar with the speed and capabilities of electronic methods.
The Future of Data Processing
The evolution of data processing is moving towards a future where analytics is central. Traditionally, data is processing involved organizing and manipulating data for various uses. Now, the focus is shifting to extracting meaningful insights, patterns and knowledge from data. Organizations are recognizing the value in not just managing data but using it strategically. The future of data processing integrates advanced analytics, machine learning, and artificial intelligence to derive and support informed decision making.