8 Types of Data Processing – A Comprehensive Guide
Introduction
Data Processing is a method of manipulation of data. It means the conversion of raw data into meaningful and machine-readable content. It basically is a process of converting raw data into meaningful information. “It can refer to the use of automated methods to process commercial data.” Typically, this uses relatively simple, repetitive activities to process large volumes of similar information. Raw data is the input that goes into some sort of processing to generate meaningful output.
Types of Data Processing
There are different types of data processing techniques, depending on what the data is needed for. In this article, we are going to discuss the five main types of data processing.
1. Commercial Data Processing
Commercial data processing means a method of applying standard relational databases, and it includes the usage of batch processing. It involves providing huge data as input into the system and creating a large volume of output but using fewer computational operations. It basically combines commerce and computers for making it useful for a business. The data that is processed through this system is usually standardized and therefore has a much lower chance of errors.
Many manual works are automated through the use of computers to make it easy and error-proof. Computers are used in business to take raw data and process it into a form of information that is useful to the business. Accounting programs are prototypical examples of data processing applications. An Information System (IS) is the field that studies such as organizational computer systems.
2. Scientific Data Processing
, Unlike commercial data processing, Scientific data processing involves a large use of computational operations but lower volumes of inputs as well as outputs. The computational operations include arithmetical and comparison operations. In this type of processing, any chances of errors are not acceptable as it would lead to wrongful decision-making. Hence the process of validating, sorting, and standardizing the data is done very carefully, and a wide variety of scientific methods are used to ensure no wrong relationships and conclusions are reached.
This takes a longer time than in commercial data processing. The common examples of scientific data processing include processing, managing, and distributing science data products and facilitating scientific analysis of algorithms, calibration data, and data products as well as maintaining all software, calibration data, under strict configuration control.
3. Batch Processing
Batch Processing means a type of Data Processing in which a number of cases are processed simultaneously. The data is collected and processed in batches, and it is mostly used when the data is homogenous and in large quantities. Batch Processing can be defined as the concurrent, simultaneous, or sequential execution of an activity. Simultaneous Batch processing occurs when they are executed by the same resource for all the cases at the same time. Sequential Batch processing occurs when they are executed by the same resource for different cases either immediately or immediately after one another.
Concurrent Batch processing means when they are executed by the same resources but partially overlapping in time. It is used mostly in financial applications or at places where additional levels of security are required. In this processing, the computational time is relatively less because applying a function to the whole data altogether extracts the output. It is able to complete work with a very less amount of human intervention.
4. Online Processing
In the parlance of today’s database systems, “online” signifies “interactive”, within the bounds of patience.” Online processing is the opposite of “batch” processing. Online processing can be built out of a number of relatively more simple operators, much as traditional query processing engines are built. Online Processing Analytical operations typically involve major fractions of large databases. It should therefore be surprising that today’s Online analytical systems provide interactive performance. The secret to their success is precomputation.
In most Online Analytical Processing systems, the answer to each point and click is computed long before the user even starts the application. In fact, many Online processing systems do that computation relatively inefficiently, but since the processing is done in advance, the end-user does not see the performance problem. This type of processing is used when data is to be processed continuously, and it is fed into the system automatically.
5. Real-Time Processing
The current data management system typically limits the capacity of processing data on an and when basis because this system is always based on periodic updates of batches due to which there is a time lag of many hours in happening of an event and recording or updating it. This caused a need for a system that would be able to record, update and process the data on as and when basis, i.e. in real-time which would help in reducing the time lag between occurrence and processing to almost nil. Huge chunks of data are being poured into systems off organizations, hence storing and processing it in a real-time environment would change the scenario.
Most organizations want to have real-time insights into the data so as to understand the environment within or outside their organization fully. This is where the need for a system arises that would be able to handle real-time data processing and analytics. This type of processing provides results as and when it happens. The most common method is to take the data directly from its source, which may also be referred to as a stream, and draw conclusions without actually transferring or downloading it. Another major technique in real-time processing is Data virtualization techniques where meaningful information is pulled for the needs of data processing while the data remains in its source form.
6. Distributed data processing
Distributed data processing (DDP) is a technique for breaking down large datasets and storing them across multiple computers or servers. In this type of processing the task is shared by several resources/machines and is executed in parallel rather than being run synchronously and arranged in a queue. Because the data is processed in a shorter period, it is more cost-effective for businesses and allows them to move more quickly. Also, the fault tolerance of a distributed data processing system is extremely high.
7. Multi-Processing
Multiprocessing is a type of data processing in which two or more processors work on the same dataset at the same time. In this multiple processors are housed within the same system. Data is broken down into frames, and each frame is processed by two or more CPUs in a single computer system, all working parallel.
8. Time-Sharing Processing
The central processing unit (CPU) of a large-scale digital computer interacts with multiple users with different programs almost simultaneously in this type of processing. It is possible to solve several discrete problems during the input/output process because the CPU is significantly faster than most peripheral equipment (e.g., printers and video display terminals ). The CPU addresses each user’s problem sequentially, but remote terminals have the impression that access to and retrieval from the time-sharing system is instantaneous because the solutions are immediately available as soon as the problem is fully entered.
Introduction
The index is named as a design in SQL server stored or maintained wilt in-memory structure or on disk related with a table or views, which is utilized principally to recognize a specific set or a row Table or Views. Indexes in SQL are the individual lookup tables, which are utilized by the data set internet searcher to accelerate the general information recovery.
The use of the index in SQL is to rapidly discover the data in a data set table without looking through each row of it. In SQL Index, it is basic to keep up more extra storage to make a copy duplicate of the data set. Tables in SQL server are contained inside database item holders that are called Schemas. The schema likewise fills in as a security limit, where you can restrict data set client authorizations to be on a particular schema level as it were. To know what are the different types of Indexes in SQL Server, then read this article to explore them and have a better understanding of them.
Different Types of Indexes in SQL Server
There are various types of indexes in SQL server:
1. Clustered Index
Clustered Index stores and sorts rows of data in a view or table depending on their central values. There may be an instance of having just one clustered index in each table, as it can empower the client to store data in a solitary request. Clustered indexes stores data in an arranged way, and in this way, at whatever point data is contained in the table in an arranged manner implies it is orchestrated with a clustered index.
At the point when a table contains a clustering in SQL server, it is named a clustered table. A clustered index is liked to utilize when adjustment of gigantic information is needed in any data set. If the data put away in a table or data set are not organized in descending or ascending requests, at that point, the data table is named as a heap.
2. Non-Clustered Index
It represents a structure, which is isolated from data rows. This type of index in SQL server covers the non-clustered key values, and each worth pair has a pointer to the data row that comprises vital significance.
In the non-clustered index, the client can undoubtedly add non-key to the leaf level, as it sidesteps the current index key cut-off points and performs completely covered recorded questions. A non-clustered index is made to improve the general exhibition of much of the time posed inquiries, which are not covered by grouped things.
Clustered vs. Non-clustered index in SQL server is that the non-clustered index stores the data at one area and indices at another area, while the clustered index is a kind of index that sorts the data rows in the table on their key values.
3. Column store Index
A column store index is one of the types of indexes in SQL Server that has a standard type of index with regards to putting away and questioning enormous data warehousing truth tables. This is an index of SQL, which was intended for development in the presentation of inquiry in the event of jobs with huge measures of data.
The column-store index empowers putting away information inside little impressions, which helps in speeding up. The use of this index takes into account the client to get IO with multiple times higher inquiry execution when contrasted with conventional column arranged capacity. For examination, the Columnstore Index gives a significant degree to have a preferable exhibition over other records in SQL. Column store index esteems from a similar area have comparative qualities, which expands the general pace of information compressions.
4. Filtered Index
A filtered index is one of the types of indexes in an SQL server that is made when a column has just a few applicable numbers for questions on the subset of values. If, when a table comprises heterogeneous data rows, a separated list is made in SQL for at least one sort of data.
5. Hash Index
Hash Index is one of the types of indexes in SQL server that slots containing a pointer or an array of N buckets and a row on each slot or bucket. It utilizes the Hash function F (K, N), where N is several buckets and K is critical. The capacity delineates the key relating to the bucket of the hash index. Every bucket of the Hash Index comprises eight bytes, which is utilized to stock the memory address of the connected rundown of basic sections.
6. Unique Index
The unique index in the SQL server confirms and guarantees that the index key doesn’t contain any copy esteems and along these lines, empowers the clients to examine that each row in the table is exceptional in either way.
The unique index in SQL is extraordinarily utilized when the client needs to have an extraordinary trait of every information. It permits people to guarantee the data respectability of each characterized section of the table in the data set. This index likewise gives extra data about the data table, which is useful to question enhancers.
Types of Pages in SQL server
- Data Pages
- Bulk Changed Map
- Text/Image Pages
- Page Free Space
- Index Allocation Map
- Secondary Global Allocation Map
- Differential Changed Map
- Global Allocation Map
Types of the Database in SQL server
- tempdb
- msdb
- Master
- Model
Conclusion
To create an index in the SQL statement is utilized to make files in tables. Indexes are utilized to recover information from the data set more rapidly than something else. The clients can’t see the lists, they are simply used to accelerate queries/searches.
An Index is a key worked from at least one column in the information base that speeds up getting rows from the view or table. This key aids a Database like MySQL, SQL Server, Oracle, and so on to discover the row related to key qualities rapidly.
An index stores the total information in the table, which is coordinated coherently with rows and columns, and truly kept up and put away in line shrewd data known as row store and if the records are stored away in segment insightful data, known as Columnstore.
If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional.
ALSO, READ
Conclusion
This is a basic introduction to the concept of data processing and its five main types. All the types have been discussed briefly, and all these methods have their relevance in their respective fields, but it seems in today’s dynamic environment, Real-time and online processing systems are going to be the most widely used ones.
If you are interested in making a career in the Data Science domain, our 9-month-long (Live Online sessions) Postgraduate Certificate Program in Data Science and Machine Learning course can help you immensely in becoming a successful Data Science professional.
