Data Transformation | Data Science

Data Transformation in Data Science: In this tutorial, we are going to learn about the Data Transformation, its stages, Data Transformation challenges, how data is transformed, etc.
Submitted by Kartiki Malik, on March 17, 2020

Data Transformation

Data transformation is the method of changing knowledge from one format or structure into another format or structure. Data transformation is crucial to activities like data integration and data management. Data transformation will embody a variety of activities: you may convert data sorts, cleanse data by removing nulls or duplicate data, enrich the info, or perform aggregations, reckoning on the wants of your project.

Typically, the method involves 2 stages,

In the initial stage, you:

Perform data discovery wherever you establish the sources and data sorts.
Determine the structure and data transformations that require to occur.
Perform data mapping to outline however individual fields are mapped, modified, joined, filtered, and aggregative.

In the second stage, you:

Extract data from the first supply. The vary of sources will vary, together with structured sources, like databases, or streaming sources, like measurement from connected devices, or log files from customers victimization your internet applications.

Perform transformations -You rework the info, like aggregating sales knowledge or changing date formats, piece of writing text strings, or connection rows and columns.

Send the info to the target store. The target can be a knowledge base or a data warehouse that handles structured and unstructured data.

Why rework Data?

You might need to remodel your knowledge for a variety of reasons. Generally, businesses need to remodel knowledge to create it compatible with different data, move it to a different system, be a part of it with different knowledge, or mixture data within the knowledge.

For example, contemplate the subsequent scenario: your company has purchased a smaller company, and you wish to mix data for the Human Resources departments. The purchased company uses distinct info than the parent company, thus you'll get to do some work to make sure that these records match. Every of the new staff has been issued an Associate in Nursing worker ID, thus this could function a key. But, you'll get to modification the information for the dates, you'll get to take away any duplicate rows, and you'll get to make sure that there aren't any null values for the worker ID field so that all staff is accounted for. These crucial functions are performed in an exceedingly area before you load the info to the ultimate target.

Other common reasons to remodel data include

You are moving your data to a replacement data store; for instance, you're moving to a cloud data warehouse and you wish to alter the info sorts.

You want to affix unstructured data or streaming data with structured data thus you'll be able to analyze the info along.

You want to feature data to your knowledge to counterpoint it, like acting lookups, adding geolocation knowledge, or adding timestamps.

You want to perform aggregations, like comparison sales knowledge from totally different regions or totaling sales from different regions.

How Is Data Transformed?

There are many alternative ways to remodel data:

Scripting. Some firms perform data recordation via scripts victimization SQL or Python to jot down the code to extract and transform the info.

On-premise ETL tools: ETL (Extract, Transform, Load) tools will take a lot of the pain out of scripting the transformations by automating the method. These tools are usually hosted on your company's web site and will need intensive experience and infrastructure prices.

Cloud-based ETL tools. These ETL tools are hosted within the cloud, wherever you'll be able to leverage the experience and infrastructure of the seller.

Data Transformation Challenges

Data transformation may be troublesome for a variety of reasons:

  • Time-consuming:
    you'll get to extensively cleanse the info thus you'll be able to rework or migrate it. This could be extraordinarily long and could be a common grievance amongst knowledge scientists operating with unstructured data.
  • Costly:
    Reckoning on your infrastructure, remodeling your knowledge might need a team of specialists and substantial infrastructure prices.
  • Slow:
    As a result of the method of extracting and reworking knowledge may be a burden on your system, it's typically worn-out batches, which implies you'll get to wait up to twenty-four hours for the successive batch to be processed. This could value your time in creating business selections.


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