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The wrangling Process, also known as Data Wrangling, involves cleaning, transforming and structuring raw, complex and often unstructured data, resulting in more valuable formats for business intelligence, analytics and other uses. It often involves removing errors and inconsistencies, replacing missing or erroneous data, and converting data into a structure that is useful for downstream analyses and applications. Data Wrangling often takes a significant amount of time and resources, but it is a critical step in data processing and analysis.
The Wrangling Process involves several steps, including:
Data Wrangling is essential for several reasons, including:
The most important use cases for Data Wrangling include:
Other technologies and terms that are closely related to the Wrangling Process include:
Dremio users are interested in Data Wrangling because it helps them optimize their data processing and analytics workflows. Dremio's Data Lake Engine simplifies and accelerates Data Wrangling and other data preparation processes by allowing users to access, join, and transform massive amounts of data across multiple data sources and formats without requiring data movement or pre-processing. This provides users with faster, more efficient, and more accurate data discovery, integration, transformation, and analysis.