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What is Data Transformation?

Data transformation is the process of converting raw data into a format or structure that would be more suitable for model building and also data discovery in general.
What is Data Transformation?

Data transformation is the process of changing the format, structure, or values of data. For data analytics projects, data may be transformed at two stages of the data pipeline. Organizations that use on-premises data warehouses generally use an ETL (extract, transform, load) process, in which data transformation is the middle step. Today, most organizations use cloud-based data warehouses, which can scale compute and storage resources with latency measured in seconds or minutes. The scalability of the cloud platform lets organizations skip preload transformations and load raw data into the data warehouse, then transform it at query time — a model called ELT ( extract, load, transform).

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Processes such as data integration, data migration, data warehousing, and data wrangling all may involve data transformation.

Data transformation may be constructive (adding, copying, and replicating data), destructive (deleting fields and records), aesthetic (standardizing salutations or street names), or structural (renaming, moving, and combining columns in a database).

An enterprise can choose among a variety of ETL tools that automate the process of data transformation. Data analysts, data engineers, and data scientists also transform data using scripting languages such as Python or domain-specific languages like SQL.

Data Transformation and Feature Engineering
How to Choose the Appropriate Technique for Your Data
https://towardsdatascience.com/data-transformation-and-feature-engineering-e3c7dfbb4899

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