Data preparation is the process of cleaning, structuring, and enriching raw data, including unstructured or big data. The results are consumable data assets used for business analysis projects.
In the data science community, data preparation is often called feature engineering . Although data prep and feature engineering are used interchangeably, feature engineering relies on domain-specific knowledge compared to the standard data prep process. Feature engineering creates “features” for specific machine learning algorithms, while data prep is used to disseminate data for mass consumption.
Both data preparation and feature engineering are the most time-consuming and vital processes in data mining . Having data prepared correctly improves the accuracy of the outcomes. However, data preparation activities tend to be routine, tedious, and time-consuming.
Historically, data transformation was the “T” in the ETL process — extract, transform, and load. ETL developers, and eventually data engineers, would transform data as part of a larger, more complex process, marking the data ready for analytics.
In the era of Hadoop and data lakes, both data engineers and analysts found themselves working with data that was much more diverse and complex in terms of format. Early data preparation tools emerged to simplify the transformation of complex data into formats that were ready for analytics.
Conceptually, data preparation and data transformation are similar. However, the introduction of cloud data warehouses and the new ELT model of processing introduced a significant difference. Data preparation tools used their own processing engines (like Spark), while data transformation tools relied on the scalable modern cloud data warehouses such as Snowflake for their processing power.
Next-generation data transformation tools like Datameer have embraced the principles of self-service for non-technical team members, the need to support multiple personas (technical and non-technical) and collaboration among these personas, and integration with cloud data warehouses like Snowflake for their processing power.
Datameer, a powerful SaaS data transformation platform that runs in Snowflake, combines to provide a highly scalable and flexible environment to transform your data into meaningful analytics. With Datameer, you can empower your non-technical analytics team members to work with complex data without the need to write code, facilitate collaboration amongst technical and non-technical team members, fully enrich analytics datasets, generate rich documentation, maintain full audit trails, and deploy and execute data transformation models directly in
Datameer’s self-service Excel-like interface, rich catalog-like data documentation, data profiling, and a rich array of functions available through a graphical formula builder allow your analytics teams to quickly perform data preparation. They can also do so in collaboration with more technical data engineers in a process where data engineers build base models from raw data, then analysts shape and organize the data to their specific needs.
Datameer supports all the critical aspects of data preparation, including data cleansing, data blending, advanced transformations, data enrichment, data grouping, and organization, and data science-specific functions.
In 2023, Datameer continues to provide a universal tool for all your data transformation needs, whether data engineering, analytics engineering, or preparation of data, and facilitates cataloging and collaboration across all these functions.