What Is Data Processing?
Data in its raw form is not useful to any organization. Data processing is the method of collecting raw data and translating it into usable information. It is usually performed in a step-by-step process by a team of data scientists and data engineers in an organization. The raw data is collected, filtered, sorted, processed, analyzed, stored and then presented in a readable format.
Data processing is crucial for organizations to create better business strategies and increase their competitive edge. By converting the data into a readable format like graphs, charts and documents, employees throughout the organization can understand and use the data.
Data processing is, generally, “the collection and manipulation of items of data to produce meaningful information.” In this sense it can be considered a subset of information processing, “the change of information in any manner detectable by an observer
Generally, there are six main steps in the data processing cycle:
Step 1: Collection
The collection of raw data is the first step of the data processing cycle. The type of raw data collected has a huge impact on the output produced. Hence, raw data should be gathered from defined and accurate sources so that the subsequent findings are valid and usable. Raw data can include monetary figures, website cookies, profit/loss statements of a company, user behavior, etc.
Step 2: Preparation
Data preparation or data cleaning is the process of sorting and filtering the raw data to remove unnecessary and inaccurate data. Raw data is checked for errors, duplication, miscalculations or missing data, and transformed into a suitable form for further analysis and processing. This is done to ensure that only the highest quality data is fed into the processing unit.
Step 3: Input
In this step, the raw data is converted into machine readable form and fed into the processing unit. This can be in the form of data entry through a keyboard, scanner or any other input source.
Step 4: Data Processing
In this step, the raw data is subjected to various data processing methods using machine learning and artificial intelligence algorithms to generate a desirable output. This step may vary slightly from process to process depending on the source of data being processed (data lakes, online databases, connected devices, etc.) and the intended use of the output.
Step 5: Output
The data is finally transmitted and displayed to the user in a readable form like graphs, tables, vector files, audio, video, documents, etc. This output can be stored and further processed in the next data processing cycle.
Step 6: Storage
The last step of the data processing cycle is storage, where data and metadata is stored for further use. This allows for quick access and retrieval of information whenever needed, and also allows it to be used as input in the next data processing cycle directly.
Distributed data processing definition
What do you mean by distributed data processing?Distributed data processing is diverging massive amount of data to several different nodes running in a cluster for processing. All the nodes execute the task allotted parallelly, they work in conjunction with each other connected by a network. The entire set-up is scalable & highly available.
distributed data processing Distributed data processing allows multiple computers to be used anywhere in a fair. One computer is designated as the primary or master computer. … It is important to plan how to set up the primary and remote computers before the distributed data processing utility is configured.
What Are the Types of Distributed Data Processing?
There are primarily two types of it. Batch Processing & Real-time streaming data processing.
Batch processing is the traditional data processing technique where chunks of data are streamed in batches & processed. The processing is either scheduled for a certain time of a day or happens in regular intervals or is random but not real-time.
Real-time Streaming Data Processing
In this type of data processing, data is processed in real-time as it streams in. Analytics is run on the data to get insights from it.
A good use case of this is getting insights from sports data. As the game goes on the data ingested from social media & other sources is analyzed in real-time to figure the viewers’ sentiments, players stats, predictions etc.
Distributed Data Processing 101 – The Only Guide You’ll Ever Need – 8bitmen.com
How Does Distributed Data Processing Work?
In a distributed data processing system a massive amount of data flows through different sources into the system.This process of dataflow is known as data ingestion.
Once the data streams in there are different layers in the system architecture which break down the entire processing into several different parts.
Let’s quickly have an insight into what they are:
Data Collection & Preparation Layer
This layer takes care of collecting data from different external sources & preparing it to be processed by the system.
When the data streams in it has no standard structure. It is raw, unstructured or semi-structured in nature.
It may be a blob of text, audio, video, image format, tax return forms, insurance forms, medical bills etc.
The task of the data preparation layer is to convert the data into a consistent standard format, also to classify it as per the business logic to be processed by the system.
The layer is intelligent enough to achieve all this without any sort of human intervention.
Data Security Layer
Moving data is vulnerable to security breaches. The role of the data security layer is to ensure that the data transit is secure by watching over it throughout, applying security protocols, encryption & stuff.
Data Storage Layer
Once the data streams in it has to be persisted. There are different approaches to do this.
If the analytics is run on streaming data in real-time in- memory distributed caches are used to store & manage data.
On the contrary, if the data is being processed in a traditional way like batch processing distributed databases built for handling big data are used to store stuff.
Data Processing Layer
This is the layer contains logic which is the real deal, it is responsible for processing the data.
The layer runs business logic on the data to extract meaningful information from it. Machine learning, predictive, descriptive, decision modelling are primarily used for this.
Data Visualization Layer
All the information extracted is sent to the data visualization layer which typically contains browser-based dashboards which display the information in the form of graphs, charts & infographics etc.
Kibana is one good example of a data visualization tool, pretty popular in the industry.
Advantages and disadvantages of distributed data processing – IT Release
Advantages of distributed data processing (DDP)
Some companies buy a mainframe and supercomputers to do large-scale processing online but it cost those a hundred thousand dollars. Buying mainframe and supercomputers tend to centralized processing and if that computer malfunction then all company data get into risk. On the other hand, doing processing by connecting personal computers from different locations can save money because they cost them a thousand bucks. Data is also distributed so adding and removing nodes (computers) can be easy. To achieve distributed networking we can use Beowulf cluster technology. In Beowulf cluster, remote computers are assigned processing through network switches and routers.
Easy to replace remote computers:
Microsoft Windows server has a feature called failover clustering that helps to remove faulty computers. If any computer on the network fails or corrupted by some means then that computer is automatically replaced by other computers.
Managing data on online server solves slow processing. On the personal computer, we can do extra tasks also. Doing extra tasks consumes processor power. But the online computer is dedicated to one type of processing and it is more likely to increase processing powers. Database server can only handle database queries and file server stores files. So data processing is optimized.
Easy to expand:
Suppose your company needs more data processing than expected then you can easily attach more computers to the distributed network.
Adding and removing computers from the network cannot disturb data flow. All data from different computers are processed in parallel. Parallel processing means data is updated at the same time from all nodes.
The overall performance of the company gets better and data is filtered and processed more rapidly in the distributed environment.
Backup of data:
Data can be backup from any computer connected to the network. So the user can backup data at a different time and work with that data locally and then upload the data to the server.
Local data synchronization:
All the computers on the network can have local storage of important data. Suppose there are different office branches interconnected to each other. All branch computers are interlinked with the main branch office. All office branch computers have a local copy of data. Office users edit and update data and then upload to the main server. So the data is synced and available to all computers. Working locally with data is easy and fast and when the user thinks that his work is complete then at the end of the day he can sync that data with the main server.
If some data like the database is a loss in any computer then it can be recovered by another interconnected computer i.e. main database server.
Disadvantages of distributed data processing (DDP)
Computers attached in DDP are difficult to troubleshoot, design and administrate.
Planning data synchronization is difficult:
Doing the correct synchronization of data is difficult to develop. Sometimes data is updated in wrong order. So administrators have to keep the focus on it before making a distributed network.
If the unauthorized computer is connected to a distributed network then it can affect other computer performance and data can be a loss also.
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