Data transformation is the process of altering data's properties to improve its accessibility or storage requirements. Changes can be made to the structure, values, or presentation of data. After data has been extracted or loaded, transformation is typically performed in the context of data analytics (also known as ETL or ELT).
An analyst or engineer will decide on the data structure as the transformation is being made. Most frequently encountered data transformations include:
- Data transformations are construcive if they result in the creation of new data or in the duplication or replication of existing data.
- When a system is destructive, it deletes information permanently from a database.
- After the transformation, the data are normalised to ensure they fit within the specified limits.
- Database changes may involve renaming, repositioning, or merging columns.
The method by which the information is altered.
ELT (Extract Load Transform) or ETL (Extract, Load, and Transform) is the most common method used for transforming data in a cloud data warehouse (Extract Transform Load). In contrast to other solutions, ELT involves loading all data into cloud storage, transforming it, and then adding it to a warehouse. More and more groups are opting for ELT as the price of cloud storage decreases.
In most cases, the following six steps make up the transformation process:
The Discovery Process In this first phase, data teams' primary goal is to learn about and locate relevant raw data. By profiling data, analysts and engineers can learn more about the changes that are required.
Data analysts make choices about how to transform, match, filter, join, and aggregate data during this "mapping" phase.
In the "Data Extraction" stage, information is gathered from one system and moved to another. Both structured (such as databases) and unstructured (such as text files) sources can be mined (such as event streaming and log files).
In order to ensure that the code was implemented correctly and appropriately, it must be reviewed afterward.
The final step is to deliver the data to its final destination. A data warehouse or similarly organised database may be the end goal.
Varieties of information reformatting
The two most common ways to transform data in the cloud are with tools that are based on scripting or code, and tools that require little to no coding at all. To have the most customization options, the most leeway in how the data is transformed, and the most say over the industry standard, scripting tools are the gold standard. Nonetheless, low-code solutions have made great strides, especially in the past few years.
The Benefits of Transforming Data
Streamlining Procedures: Transformed data is more manageable for both humans and computers. The transformation process involves assessing and adjusting data in order to enhance data storage and accessibility.
Due to the numerous dangers it poses, poor data quality must be addressed: Data transformation can help your business get rid of quality issues and reduce the likelihood of misunderstandings.
Quicker Searches: You can improve analysis efficiency by optimising query speed and business intelligence tools. We achieve this by storing data in a warehouse after it has been standardised and converted to the appropriate format.
Data Management Made Easier: A sizable chunk of data transformation is spent on metadata creation and lineage tracking. When put into practise, these methods can drastically simplify data management for teams. As businesses grow, they will need information from a wider range of sources, so this is becoming an increasingly important factor to take into account.
More generally applicable: Transformation makes it easier to get the most out of your data by standardising and cleaning it up so that it can be used in more contexts.
There are numerous benefits to using data transformation techniques, but it is important to be aware of the few potential drawbacks as well.
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