EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login
Home Data Science Data Science Tutorials MySQL Tutorial What is Data Processing?
Secondary Sidebar
MySQL Tutorial
  • Database
    • What is Data Modeling
    • What is Data Processing
    • DBMS Architecture
    • DBMS Keys
    • Careers in Database Administration
    • What is MySQL Database
    • MySQL Relational Database
    • How to Connect Database to MySQL
    • MySQL Database Repair
    • RDBMS Interview Questions
    • DBMS Interview Questions
  • Basic
    • MySQL floor
    • MySQL DESCRIBE table
    • MySQL encryption
    • Introduction to MySQL
    • Is SQL Server a Database?
    • What is MySQL
    • Is MySQL Programming Language
    • MySQL Server
    • MySQL AB
    • MySQL Community Server
    • How To Install MySQL
    • MySQL Versions
    • MySQL OpenSource
    • MySQL GUI Tools
    • MySQL Grant
    • MySQL Error 1064
    • MySQL Drop Database
    • MySQL not equal
    • MySQL SELECT INTO Variable
    • MySQL Commands
    • MySQL Operators
    • What is MySQL Schema
    • Wildcards in MySQL
    • MySQL Constraints
    • MySQL Administration
    • MySQL Data Type
    • MYSQL COMMIT
    • MySQL FORMAT
    • Timestamp to Date in MySQL
    • MySQL DATEDIFF
    • MySQL?Incremental Backup
    • MySQL JSON Data Type
    • MySQL ENUM
    • MySQL Default Port
    • Cheat Sheet MySQL
  • Queries
    • MySQL Queries
    • MySQL Query Commands
    • SELECT in MySQL
    • MySQL INSERT IGNORE
    • MySQL having
    • ORDER BY in MySQL
    • MySQL Cheat Sheet
    • MySQL ORDER BY Random
    • MySQL ORDER BY DESC
    • MySQL GROUP BY
    • MySQL GROUP BY Count
    • MySQL GROUP BY month
    • MySQL WHERE Clause
    • MySQL WITH
    • MySQL FETCH
    • MySQL DDL
    • MySQL DML
    • MySQL WHERE IN Array
    • MySQL Fetch Array
    • MySQL ISNULL
    • MySQL Index Types
    • Mysql? Export Schema
    • Amazon RDS for MySQL
    • MySQL greatest
  • Functions
    • MySQL Function
    • MySQL Aggregate Function
    • MySQL String functions
    • MySQL Date Functions
    • MySQL Window Functions
    • MySQL Math Functions
    • MySQL Boolean
    • Cursor in MySQL
    • Condition in MySQL
    • MySQL BETWEEN
    • Insert in MySQL
    • MySQL IFNULL()
    • MySQL TIMESTAMPDIFF()
    • MySQL COALESCE()
    • MySQL count()
    • MIN() in MySQL
    • MySQL Numeric
    • MySQL field()
    • MySQL FIND_IN_SET()
    • MySQL avg()
    • MySQL MAX() Function
    • MySQL BIN()
    • MySQL Concat
    • MySQL DECODE()
    • MySQL REGEXP_REPLACE()
    • MySQL Asynchronous
    • MySQL innodb_buffer_pool_size
    • MySQL key_buffer_size
    • MySQL TRUNCATE()
    • MySQL ROW_NUMBER()
    • NOT in MySQL
    • MySQL IN Operator
    • LIKE in MySQL
    • ANY in MySQL
    • MySQL NOT IN
    • MySQL CHECK Constraint
    • MySQL DISTINCT
    • MySQL ALL
    • MySQL Union
    • MySQL UNION ALL
    • MySQL EXISTS
    • MySQL ON DELETE CASCADE
    • MySQL REGEXP
    • MySQL Index
    • MySQL Add Index
    • MySQL REINDEX
    • MySQL UNIQUE INDEX
    • MySQL Clustered Index
    • MySQL? InnoDB Cluster
    • Table in MySQL
    • ALTER TABLE MySQL
    • MySQL Temporary Table
    • MySQL Clone Table
    • MySQL Repair Table
    • MySQL Lock Table
    • MySQL Optimize Table
    • TRUNCATE TABLE MySQL
    • MySQL Table Dump
    • MySQL Update Set
    • MySQL ALTER TABLE Add Column
    • MySQL RANK()
    • MySQL CTE
    • MySQL LAG()
    • MySQL GROUP_CONCAT()
    • MySQL EXTRACT()
    • MySQL REPLACE
    • MySQL AUTO_INCREMENT
    • MySQL SYSDATE()
    • MySQL NULLIF()
    • MySQL Substring
    • MySQL SUBSTRING_INDEX()
    • MySQL LOWERCASE
    • MySQL Row
    • MySQL NOW
    • MySQL CEIL
    • MySQL Alias
    • MySQL Trigger
    • MySQL SHOW Triggers
    • MySQL UPDATE Trigger
    • MySQL DELETE Trigger
    • MySQL AFTER UPDATE Trigger
    • MySQL Stored Procedure
    • ROLLUP in MySQL
    • MySQL? INSTR()
    • MySQL Subquery
    • MySQL Timestamp
    • MySQL? Hour()
    • MySQL MOD()
    • MySQL DATE_FORMAT()
    • ALTER Column in MySQL
    • MySQL Rename Column
    • MySQL Interval
    • MySQL CURDATE
    • MySQL BIT
    • MySQL Binlog
    • MySQL Average
    • MySQL TEXT
    • MySQL SHOW
    • MySQL Offset
    • MySQL Timezone
    • mysql_real_escape_string
    • MySQL Datetime
    • MySQL DATE_SUB()
    • MySQL FULLTEXT
    • MySQL DATE_ADD()
    • MySQL sum()
    • MySQL Merge
    • MySQL BigInt
    • MySQL ROUND
    • MySQL VARCHAR
    • MySQL Decimal
    • MySQL Limit
    • MySQL today()
    • MySQL WEEKDAY
    • MySQL Split
    • MySQL Create Function
    • MySQL BLOB
    • MySQL encode()
    • MySQL Primary Key
    • MySQL Foreign Key
    • Unique Key in MySQL
    • MySQL Drop Foreign Key
    • MySQL DROP TRIGGER
    • MYSQL Database
    • Delete Database MySQL
    • MySQL Root
    • MySQL Root Password
    • MySQL Client
    • MySQL Users
    • MySQL?User Permissions
    • MySQL add user
    • MySQL List User
    • MySQL Show Users
    • MySQL User Password
    • MySQL?Cardinality
    • MySQL Workbench
    • MySQL Backup
    • MySQL REVOKE
    • MySQL Dump
    • MySQL Cluster
    • MySQL Partitioning
    • MySQL Full Text Search
    • MySQL Admin Tool
    • MySQL Export Database
    • MySQL Export to CSV
  • Joins
    • Joins in MySQL
    • MySQL Outer Join
    • Left Outer Join in MySQL
    • MySQL Self Join
    • Natural Join in MySQL
    • MySQL DELETE JOIN
    • MySQL Update Join
    • MySQL Cross Join
  • Advanced
    • MySQL Formatter
    • MySQL TINYINT
    • MySQL Grant All Privileges
    • MySQL DROP TABLE
    • MySQL rename database
    • MySQL Flush Privileges
    • MySQL super Privilege
    • MySQL Character Set
    • MySQL Log File
    • MySQL Flush Log
    • Grant Privileges MySQL
    • MySQL WHILE LOOP
    • IF Statement in MySQL
    • MySQL CASE Statement
    • MySQL IF Function
    • MySQL IF EXISTS
    • MySQL UUID
    • Views in MySQL
    • MySQL Replication
    • MySQL Partition
    • Toad for MySQL
    • Navicat for MySQL
    • MySQL AES_Encrypt
    • MySQL Performance Tuning
    • MySQL Transaction
    • MySQL? sort_buffer_size
    • MySQL? Sync
    • MySQL? Query Cache
    • MySQL Collation
    • MySQL ODBC Driver
    • MySQL Partitioning
    • MySQL InnoDB
    • MySQL Float vs Decimal
    • MySQL Union vs Union All
  • Interview Questions
    • MySQL Interview Questions

Related Courses

MS SQL Certification Courses

Oracle Certification Courses

PL/SQL Certification Courses

What is Data Processing?

By Afshan BanuAfshan Banu

What-is-Data-Processing

Introduction to Data Processing

Data processing is the collecting and manipulation of data into the usable and desired form. The manipulation is nothing but processing, which is carried either manually or automatically in a predefined sequence of operations. In past, it is done by manually which is time-consuming and may have the possibility of errors during in processing, so now most of the processing is done automatically by using computers, which do the fast processing and gives you the correct result.

The next point is converting to the desired form, the collected data is processed and converted to the desired form according to the application requirements, that means converting the data into useful information which could use in the application to perform some task. The Input of the processing is the collection of data from different sources like text file data, excel file data, database, even unstructured data like images, audio clips, video clips, GPRS data, and so on. The commonly available data processing tools are Hadoop, Storm, HPCC, Qubole, Statwing, CouchDB and so all

And the output of the data processing is meaningful information that could be in different forms like a table, image, charts, graph, vector file, audio and so all format obtained depending on the application or software required.

How Data is Processed?

Data processing starts with collecting data. The data collected to convert the desired form must be processed by processing data in a step-by-step manner such as the data collected must be stored, sorted, processed, analyzed, and presented.

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

So this broadly divided into 6 basic steps as following discussion given below.

  • Data Collection
  • Storage of Data
  • Sorting of Data
  • Processing of Data
  • Data Analysis
  • Data Presentation and conclusions

Let’s discuss in details one by one:

All in One Data Science Bundle(360+ Courses, 50+ projects)
Python TutorialMachine LearningAWSArtificial Intelligence
TableauR ProgrammingPowerBIDeep Learning
Price
View Courses
360+ Online Courses | 50+ projects | 1500+ Hours | Verifiable Certificates | Lifetime Access
4.7 (86,650 ratings)

data collection

1. Data Collection

As already we have discussed the sources of data collection, the logically related data is collected from the different sources, different format, different types like from XML, CSV file, social media, images that is what structured or unstructured data and so all.

storage of data

2. Storage of Data

The collected data now need to be stored in physical forms like papers, notebooks, and all or in any other physical form. Now because of the data mining and big data, the collection of data is very huge even in structured or unstructured form. The data is to be stored in digital form to perform the meaningful analysis and presentation according to the application requirements.

sorting of data

3. Sorting of Data

After the storage step, the immediate step will be sorting and filtering. The sorting and filleting are required to arrange the data in some meaningful order and filter out only the required information which helps in easy to understand visualize and analyze.

processing of data

4. Processing of Data

A series of processing or continuous use and processing performed on to verify, transform, organize, integrate, and extract data in a useful output form for farther use.

 analysis

5. Data Analysis

Data analysis is the process of systematically applying or evaluating data using analytical and logical reasoning to illustrate each component of the data provided and to get the concluded result or decision.

presentaion

6. Data Presentation and Conclusions

Once we come to the analysis result it can be represented into the different form like the chart, text file, excel file, graph and so all.

Single software or a combination of software can use to perform storing, sorting, filtering and processing of data whichever feasible and required. It may be carried out by specific software as per the predefined set of operations according to the application requirements.

Different Types of Output

The different types of output files as –

  • Plain text file – These are exported as notepad or WordPad files. These are the simplest form of the data file.
  • Table/ Spreadsheet – In this file format, the data represent in rows and columns, which help in easy understanding and analysis of data. This file format to perform various operations like filtering & sorting in ascending/descending order and statistical operations as well.
  • Graphs and Charts – The graphs and charts format is standard features in most of the software. This format is very easy to analyze the data, not required to read each numeric data which takes a time consuming only in one look can understand and analyze the data.
  • An Image File or Maps/Vector – If the application required to store and analyze with spatial data the option to export the data into image file and maps file or vector files is of great use.

Along with these, the other format can be software specific file formats which can be used and processed by specialized software.

Different Methods

There are mainly three methods used to process the data, these are Manual, Mechanical, and Electronic.

1. Manual: In this method data is processed manually. The entire processing task like calculation, sorting and filtering, and logical operations are performed manually without using any tool or electronic devices or automation software.

2. Mechanical – In this method data is not processed manually but done with the help of very simple electronic devices and a mechanical device for example calculator and typewriters.

3. Electronic – This is the fastest method of data processing and also modern technology with the modern required features like highest reliability and accuracy. This method is achieved by the set of programs or software which run on computers.

Types

On the basis of steps they performed or process they performed. It likes:

  • Batch Processing (In batches)
  • Real-time processing (In a small time period or real-time mode)
  • Online Processing (Automated way enter)
  • Multiprocessing (multiple data sets parallel)
  • Time-sharing (multiple data sets with time-sharing)

Why We Should Use Data Processing?

Now a day’s data is more important most of the work are based on data itself, so more and more data is collected for different purpose like scientific research, academic, private & personal use, commercial use, institutional use and so all. It is necessary to process this collected data so that all the above – mentioned steps are used for the processing which is stored, sorted, filtered, analyzed, and presented in the required usage format. The time consuming and complexity of processing depending on the results which are required. In the case of huge data collection or the big data they need for processing to get the optimal results with the help of data mining and data management it becomes more and more critical.

Conclusion

It is the conversion of the data to useful information. The data processing is broadly divided into 6 basic steps as Data collection, storage of data, Sorting of data, Processing of data, Data analysis, Data presentation, and conclusions. There are mainly three methods used to process that are Manual, Mechanical, and Electronic.

Recommended Articles

This has been a guide to What is Data Processing?. Here we discussed how data is processed, different method, different types of outputs, tools, and Use of Data Processing. You can also go through our other suggested articles to learn more –

  1. Data Visualization Tools
  2. What is Data Warehouse?
  3. What is Data Visualization
  4. Python Multiprocessing | How to Create?
Popular Course in this category
All in One Data Science Bundle (360+ Courses, 50+ projects)
  360+ Online Courses |  1500+ Hours |  Verifiable Certificates |  Lifetime Access
4.7
Price

View Course

Related Courses

MS SQL Training (16 Courses, 11+ Projects)4.9
Oracle Training (14 Courses, 8+ Projects)4.8
PL SQL Training (4 Courses, 2+ Projects)4.7
1 Shares
Share
Tweet
Share
Primary Sidebar
Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

ISO 10004:2018 & ISO 9001:2015 Certified

© 2022 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

SPSS, Data visualization with Python, Matplotlib Library, Seaborn Package

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more