What is Wrangling Process?
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.
How does the Wrangling Process work?
The Wrangling Process involves several steps, including:
- Collecting data from various sources, including databases, APIs and files.
- Cleaning the data by removing errors, inconsistencies and missing values.
- Transforming data by normalizing, merging, splitting, and summarizing it.
- Enriching data by adding external data sources and variables to make it more useful and valuable.
Why is Wrangling Process important?
Data Wrangling is essential for several reasons, including:
- Reducing errors and inconsistencies in data.
- Making raw data more useful and valuable for downstream analyses and applications.
- Improving the accuracy and reliability of analyses and applications.
- Optimizing storage and processing of data.
What are the most important Wrangling Process use cases?
The most important use cases for Data Wrangling include:
- Data preparation for Business Intelligence, Analytics, and Reporting.
- Data preparation for Machine Learning and Artificial Intelligence.
- Data integration and migration between different systems and applications.
- Data quality management and control.
What are other technologies or terms that are closely related to Wrangling Process?
Other technologies and terms that are closely related to the Wrangling Process include:
- Data Integration
- Data Cleansing
- Data Enrichment
- Data Preparation
Why are Dremio users interested in Wrangling Process?
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.
