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Data Integration And Transformation

In data science, data integration and transformation are the necessary steps while doing data preprocessing. In data integration, different tricks are made to combine data gathered from various sources. It is a similar process that is done in the warehouse. But in data transformation, different data files are merged and deleted according to the data science models’ requirements. On the other hand, data transformation data is a process to transform data into different forms according to the needs of the data science and machine learning algorithm. Here we will discuss these methods in detail.  Also, check this

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Data Science Courses in Hyderabad
Data Science Courses in Hyderabad

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What is Data Integration:

It is the process of combining similar types of data gathered from different sources. These sources can be different databases, data warehouses, data cube files, or flat files. This process is used to transfer data from source systems to the target systems. There are several techniques for data integration. We will discuss them briefly. Become a Data Scientist with 360DigiTMG PG Diploma in Data Science and Artificial Intelligence! Get trained by the alumni from IIT, IIM, and ISB.

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Extract, Transform and Load:

In this technique, data is gathered from disparate sources of data. Then data is harmonized and transmitted to a single place called a warehouse.

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Extract, Load, and Transform:

This technique is part of big data and is mostly not considered in data mining techniques. In the first step, data is only loaded to bid data systems. It is loaded and transformed late at the time of need. 

Data Change Capture:

If some changes occur in the data in the database or real-time data source, then it is necessary to apply changes to all the data and warehouse repositories. Don’t delay your career growth, kickstart your career by enrolling in this Data Science Training Institute In Chennai

Data Virtualization:

In this technique, data is virtually merged with other data files in the warehouse to use them later for different purposes. Want to learn more about data science? Enroll in the data science course in pune with placement guarantee to do so.

What is Data Transformation:

The data extracted from the source of the data is not always in the required format. This raw data is transformed into the required format according to the requirements. The destination systems of the data can have different format requirements of the data according to its needs. There are various techniques used for the transformation of the data. We have discussed some of the leading data transformation methods below:

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Data Normalization:

If the data attribute values are not in a specific pattern, you need to scale all the data attributes’ values into one particular range. For example, for numeric attribute values, you can scale data values to a range of min and max values.

Data Smoothing:

In this technique, the noise from the data is removed to make it smooth and efficient according to the requirements of the algorithm to be applied. Moreover, outlier values are removed from the data. The regression and clustering techniques are used for this purpose. Looking forward to becoming a Data Scientist? Check out the data science training in bangalore and get certified today.

Data Generalization:

In this technique, data is mapped to categorical values to make it simple according to the situation. For example, the age attribute is divided into three sub-categories: young, middle age, and old age categories. This technique is used to convert complex data into simpler formats. As a result, data becomes easily understandable. 

We have discussed the main techniques of data preprocessing in data science. Keep in touch with our platform for more similar articles.

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