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.
Data transformation has historically been the “T” of the ETL process – extract, transform, and load. ETL developers, and eventually data engineers, would transform data as part of a larger, more complex process, in order to mark the data ready for analytics. One reason why the data transformation was the domain of these highly technical teams was that the target structures in a traditional data warehouse or mart were highly complex – e.g. star- and snowflake-schemas.
In the Hadoop and data lake era both data engineers and analysts were thrust into working with data that was much more complex in terms of diversity and format. BI tools in this era were not equipped to handle such data. Early data preparation tools came on to the market to make it much easier to transform complex data into analytics-ready formats consumable by BI tools. Eventually, the BI tools started to introduce their own data preparation within their suites.
Conceptually, data preparation and data transformation are similar. The introduction of cloud data warehouses and the new ELT model of processing introduced one major difference – data preparation tools used their own processing engines (Spark, etc.), while data transformation tools relied on the scalable modern cloud data warehouses such as Snowflake for their processing power.
Many data preparation tools were designed to be self-service for analysts and data scientists, with methods of transforming data without writing code. Initial data transformation tools in the ELT stack such as dbt focused on using SQL coding as the primary means to transform data, pushing the domain back to more technical, programming-savvy staff.
Next-generation data transformation tools such as Datameer also facilitate data preparation by embracing:
Datameer is a powerful SaaS data transformation platform that runs in Snowflake – your modern, scalable cloud data warehouse – that combines to provide a highly scalable and flexible environment to transform your data into meaningful analytics. With Datameer, you can:
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:
Datameer can provide the universal tool for all your data transformation needs, whether data engineering, analytics engineering, and analyst or data scientist data preparation, and facilitate cataloging and collaboration across all these functions.
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