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Data is a collection of different factors that help in decision-making and conveying information. Data plays an important role in today’s world. All of our information is saved in some format. We have seen a renaissance in data collection and formation in the past few years. The process that helps in analyzing, structuring, and accessing data is known as data transformation. Data transformation plays an important role in computing. It is the process of converting, cleansing and structuring data into a usable format. Raw data is challenging to understand for engineers and other specialists; however, a transformation of data helps them convert it into a useful and meaningful context.
This blog post from the experts at AllAssignmentHelp.com will explore everything about data transformation. We will look into its introduction, benefits, types, challenges, and process.
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Data Transformation – What Is It?
In the field of computing, concepts of data transformation tend to play a vital role. Transformation is mainly useful for researchers who are planning to analyze the collected data. In this regard, it is observed that to interpret the result of the collected data, the researcher has to transform the data into some specific format that is readable to his or her system or software. If it is not done, then, in this situation, a researcher will not be able to attain its goals and objectives effectively. Thus, it can be said that data transformation is regarded as the systematic process of converting data from one format to another. It is regarded as one of the most fundamental aspects of the concept, such as data integration.
Data integration is the process of combining the data that is being gathered from different sources, and it will also provide the user with a unified view of the data in an effective way.
On the other hand, it can also be said that there are different types of activities involved in the concept of data transformation. It comprises converting the types of data, performing the cleaning of the data by removing different duplicates and null data, enriching the data, carrying out the aggregations, etc. Besides this, there are some benefits associated with the data transformation. However, discussion about the same will be carried out later. If you ever need to write an assignment on the transformation of data and need more skills, you may seek assignment help online. The online writers will craft your best assignments, following your requirements and university guidelines, and that too within the deadline.
The Different Data Transformation Processes
In the above section of this blog post, we have briefly discussed the concept of data transformation, what it is, and other introductions. Now let us move forward and examine the different processes involved in data transformation. Before moving on to the discussion, have you ever thought about what methodologies companies use in data transformation? Well, some businesses prefer to share their data and other information manually. However, some think that manual data sharing is not safe and use different transformation tools to share their partial and full data. No matter whether the company uses manual or automated data transfer, the process of sharing remains the same. Below, we have listed the steps involved in the transformation of data from one end to the other.
1- Data discovery
The process of data transformation begins with the discovery of data. Following the given context, it can be said that in the respective phase, mainly profiling of the data is performed. However, to provide the same assistance, different profiling tools are being used effectively. Among all, the written profiling script is being used in this. This is used to get a better idea of the characteristics and structure of data.
2- Data mapping
In the second step, it is defined how different individual fields can be mapped, joined, modified, aggregated, and fitted together. This is done to produce the final desired output. The mapping of data is done by the developer from the time they start working on the specific type of technology. This step in the data transformation process is called a filter, where one can filter out all the unwanted things and execute a smooth run.
3- Code generation
It is regarded as the third step of data transformation. Herein, different executable code is generated that will help in the process of transforming the data as per the desired data mapping rules. Typically, different data transformation technologies are used here to generate the code. Developers use different coding languages, like SQL, Python, R, and others. In this stage, code generators work closely with transformation technologies and help in creating a visual design atmosphere.
4- Code Execution
In the respective step, the code that is generated above is sent for execution purposes. The codes that are executed are integrated into the transformation tool. The code execution is known to be the last step in the transformation of data before transferring it to human users. In this step, the code is executed daily or hourly, which helps in the transmission of data.
5- Data review
Once the data is executed, it is time to review the received data. This step will help in reviewing, checking, and correlating all terms of error. It is the final step in which the programmer or analyst will get an idea of whether the output of the data is fulfilling all the requirements of data transformation or not.
6- Data sending
So far, we have discussed all the other steps of data transformation that will help ensure a correct and appropriate implementation. In this final step, the data is transmitted to its final destination.
The above-mentioned steps are an example of the different steps involved in the transformation of data. However, there is one thing: there is no standard step that one should follow; you may alter steps as per their requirements and the one that suits their data team.
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Data Transformation: What Are its Types
The data transformations are of two types: batch data transformation and interactive data transformation. Let us now briefly look into this type and find out what it is.
Batch data transformation
Traditionally, the data transformation activity is performed in bulk or batches. In this regard, in the respective form of data transformation, the developers used to write the code, and they also used to perform the implementation of different transformation rules in the data integration tools in an effective way. After doing the same, they perform the execution of the generated code on a large volume of data. The respective process tends to follow a linear set of steps. In other words, it can also be depicted that batch data transformation is regarded as the cornerstone of different types of data integration technologies that comprise data warehousing, application integration, and data migration.
Interactive transformation
It is being regarded as another type of data transformation. It is the type of emerging capability that tends to allow business users and analysts to perform direct interaction with a large dataset with the help of a visual interface. Here, the features of the data are understood, and the data is also corrected with the use of simple interactions. However, it can be said that the respective type of data transformation also follows a similar type of data integration step, which is followed by the batch data transformation. However, in interactive data transformation, it is not necessary to comply with the linear fashion that is effectively done in batch data transformation. If you are eager to know more about the transformation of data, you may get programming language assignment help and develop your knowledge about the same.
Data Transformation Types Based on Categories
Batch data transformation and interactive data transformation are the two types based on which the transformation of data is classified. However, the transformation of data is also classified based on different categories. Most students get confused between these two types and start to pay for online class help. By hiring online writers, you may easily earn top grades on your assignments and exams. However, it is still important to develop your knowledge of the data transformation types and how they are categorized. To ease the student’s pressure and reduce their confusion, we classified the types into two different sections. First, we have discussed interactive and batch data transformation. Let us now look at the other types that help in transferring data in groups over time.
- Transformation of data through scripting tools
- The use of on-premises ETL tools for data transformation
- Destructive data transformation
- Cloud-based ETL tools
- Low-/No-Code Tools
- Constructive and destructive data transformation
- Structural transformation of data
- Aesthetic transformation of data
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Transformation of Data: What Are Its Techniques?
The major work of data transformation is to cleanse and organize the onsite data for future use. However, there are multiple techniques in this process that reduce the workload and structure all kinds of data in business organizations. There are nine techniques that help in the effective organization, analysis, and structuration of the data. They are listed below.
- Revising: This technique identifies and removes duplicates.
- Manipulation: It is a process where different alterations are made to make the data more readable. It also converts unstructured data into structured data and further summarizes aggregate values.
- Separating: Splitting is the process of creating distinct columns for each of the values in a single column that has multiple values.
- Integrating: It combines information from several tables and resources.
- Data smoothing: It is an important technique that helps remove noisy, distorted, or meaningless data from the dataset. Data smoothing further helps identify specific patterns or trends.
- Data aggregation: Here, unprocessed data is gathered from several sources and transformed into a summary format. Any organization that gathers a lot of business data ought to make use of this method. It will assist them in gathering various data, analyzing it, and centrally storing it.
- Discretization: Interval labels are formed in continuous data using this data transformation approach in an effort to improve its efficiency and facilitate analysis. Compact categorical data can be produced with the use of the decision tree method.
- Generalization: The idea of hierarchies is used to convert low-level data attributes into high-level attributes. Crystal-clear data snapshots are produced by layering on top of ever more comprehensive summary data.
- Attribute construction: This is a stage in the transformation of datas where new attributes are created from the existing set. It is an important technique in the mining process.
Major Reasons for Data Transformation
There are some major reasons examined that led to the activity, such as data transformation.
- The need tends to arise when an individual moves the data to the new data store. For instance, when a person moves data to the cloud data warehouse, it needs to change the type of data.
- This is also performed when unstructured data is joined with structured data. This is done with the aim of performing the data analysis and tools.
- It will also need to be performed when an individual wants to add information to the data in order to enrich it. This involves the addition of geolocation data and performing lookups, etc.
- The transformation of data will also need to be performed when aggregation needs to be performed, which involves the comparison of sales data from the varied regions. Therefore, it can be said that they are being regarded as the main reason behind the transformation of data.
- The transformation of data is used in many areas, like machine learning, digital transformation, and business. It helps in data preprocessing, data preparation, and making accurate predictions.
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Three Ways to Transform Data
There are three major ways in which the data can be transformed. They are:
- Scripting: One way to transform data is through scripting. There are many companies that tend to perform transformation of data with the help of scripting. However, for the given purpose, they tend to use means like SQL and Python. These techniques are used to write the code and perform the extraction of data.
- On-premise ETL tools: Here, ETL (Extract, Transform, and Load) tools are being used in order to take much of the pain out of scripting the transformation. The given tools are primarily hosted on the site of the company. On the other hand, they require infrastructure costs as well as extensive expertise.
- Cloud-based ETL tools: The respective tools are being hosted on the cloud, which will allow an individual to leverage the infrastructure as well as the expertise of the vendor in an effective way. Hence, these are some of the main ways that transformation is being performed by the individual. In this way, only the individual can convert data as per his or her desire.
Challenges Associated With the Data Transformation
Raw data is inconsistent, imperative, and repetitive. To extract reliable data, transformation is an important tool. However, according to a recent survey, it has been found that nearly 52% of companies have not leveraged their data and analytics, and this is the reason why they are found to be less competitive. Many more companies fall behind their data-driven goals and have not followed an internal data culture. Let us not find the reason why companies are falling behind with their data. The following could be some of the possible reasons:
- Time-consuming: The first challenge of transformation is time-consuming. Before starting the transformation, the programmer will have to clean the data. The given thing is time-consuming. It is regarded as one of the most common complaints that always comes from the side of a data scientist.
- It is costly: The second most significant challenge that is related to data transformation is that it is costly. Here, it almost depends on the infrastructure. The process of transforming the data requires highly specialized and skilled people. This is because, to do the data transformation work, an individual should have good technical knowledge. Further, it also involves infrastructure costs. It increases the overall time spent doing the respective function.
- It is slow: Data extraction puts a lot of burden on the computer’s system. Thus, due to the presence of a given aspect, it is often performed in batches. This means that a programmer will need to wait for 24 hours to see the processing of the next batch. The given thing will cost the firm time in the process of making the business decision. By taking significant actions about these challenges, the whole activity of transformation of data can be made much easier and more effective.
What Are the Benefits of Data Transformation?
Now, the discussion will be carried out on the main benefits that are associated with the concept of data transformation. Detailed explanations of the same are given below:
- Higher data quality: It is regarded as the first benefit that is related to data transformation. This happens because here, an individual tends to convert its data into a high-quality format.
- It also helps reduce the number of mistakes. While writing the data, an individual sometimes makes various types of mistakes, such as duplication of data, missing values, etc. Thus, with the help of data transformation, such mistakes can be detected and significant actions can be taken to resolve them.
- It also assists in the task of making the query time much faster, and it also enhances the retrieval time.
- In this situation, minimal resources are needed to perform the manipulation of the data effectively.
- It also helps in the task of performing better and the effective organization of the data.
- The transformation of data helps enhance data quality and improve end-user accessibility.
- The data here is more usable in a case like business intelligence. Thus, some major benefits are related to the concept, such as data transformation in an effective way.
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Frequently Asked Question
| Question 1: What does ETL in data transformation stand for? Answer 1: ETL stands for extraction, transformation, and loading of data and information. |
| Question 2: Is data transformation difficult? Answer 2: As a beginner, transforming data might be a little complex. However, with continuous skills and developing knowledge, you may become confident with transformation and integrating custom data fields. |
| Question 3: What are a few examples of the transformation of data? Answer 3: Data aggregation, data cleansing, data duplication, data filtering, data joining, data spitting, data validation, data mapping, and data integration are a few examples of it. |