Data transformation is the process of converting data from one format to another, generally to meet the requirements of the destination platform.
Companies are collecting data from a multitude of disparate sources, requiring data transformation to enable compatibility.
Once converted data is at hand, it's essential to follow data warehousing best practices to protect your data.
To best understand data transformation, it's important to appreciate the process and the motivations behind it.
Data transformation entails a variety of procedures. Many people immediately consider data conversion, which encompasses data extraction and scrubbing. It might also include cleansing data and aggregating data.
The overall process of data transformation sets out to make data compatible. Without it, data scientists run the risk of compliance problems in new data warehouses.
As companies seek to process more data, they run into challenges with disparate data types. Tradtionally it has been difficult to aggregate and query structured and semi-structured data within the same data platform.
The ETL (Extract, Transform, and Load) model is generally relied upon as an efficient means of data transformation. Snowflake is an example of data warehousing that can native support semi-structured data alongside relational (or structured) data.
The process can be broken down into two parts: the data discovery and planning phase, and the data extraction, cleansing and delivery phase.
The first part is more about researching and planning, while the second part involves handing the data.
REASONS TO TRANSFORM DATA
Data transformation may occur when data is being moved or when various data types need to be analyzed together. It also happens when information is being added to existing data sets, and when users want to aggregate data from multiple data sets.
Even within these examples, the common thread is compatibility.
SNOWFLAKE AND DATA TRANSFORMATION
Snowflake, the cloud data platform, offers secure data sharing that eliminates the need for data extraction or transformation between departments, geographies, or partners. For primary data source loading, Snowflake works with a range of data integration partners and allows users to choose either ETL or transform data after loading (ELT). Snowflake removes the worry from data integration and allows you to focus on results.
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