ETL and Data Wrangling: Users, Data Structure, and Use Cases
When you’re choosing how you prepare your data before moving it into another repository, you want to make the right data management decisions. You have options available, two of which are ETL and data wrangling. Which is the right one for you?
In this article, we’ll explore who uses each method, the types of data used, and the use cases for each technique. You’ll have better insight into how both of these options benefit your organization.
ETL: what it is, who uses it, and why
ETL stands for “extract, transform, and load.” As the name implies, data is extracted from one source, transformed into a predefined format, and then loaded into a destination such as a data lake or a data warehouse. Business intelligence or report generator applications access that information to deliver insights to decision-makers. (Related: ETL vs. Data Preparation)
Who uses ETL? These procedures and tools were designed for use by IT professionals. ETL requires highly structured policies and active monitoring by skilled employees.
ETL works best with structured information, such as databases, ERP, SAS, or CRM applications. This technique isn’t appropriate for complex, raw data sources that require a great deal of extraction and derivation.
What are the best use cases for ETL? These processes are suited for moving legacy data from a database into a data lake or a data warehouse. For example, if you wanted to move information out of your legacy CRM system and into a data warehouse so it would be easier to query that information, ETL would be the technique you would use.
Watch our webcast
Making the Case for Legacy Data in Modern Data Analytics Platforms
Watch this webinar to learn best practices for integrating legacy data sources, such as mainframe and IBM i, into modern data analytics platforms such as Cloudera, Databricks, and Snowflake.
Data wrangling: what it is, who uses it, and why
Data wrangling is also known as data preparation. What differentiates data wrangling from ETL is that this method is very much self-serve data preparation. Instead of information being solely the provenance of IT, data is now in the hands of the people who use it on a daily basis: line-of-business users.
What kind of information is data wrangling good for and how do you ensure you’re doing it correctly? It’s meant for the complex, raw data sources that need substantial extraction and derivation – the information that ETL handles, but at a different stage. Moreover, data wrangling is good for information with schema that aren’t known in advance ahead of time. Practically speaking, it means that the person analyzing the data determines how the information will be leveraged.
The use cases for data wrangling are what experts define as “exploratory in nature.” Typically, users use data wrangling when they’re working with a new data source (or more than one data source) before they launch a data analytics initiative. For example, you might be looking at customer reactions on LinkedIn before you start a larger analytics project.
The right data wrangling techniques are crucial for your overall data management strategy. You might decide to implement both data wrangling and ETL, depending on your situation.
Learn best practices for integrating legacy data sources, such as mainframe and IBM i, into modern data analytics platforms such as Cloudera, Databricks, and Snowflake. Watch our webinar: Making the Case for Legacy Data in Modern Data Analytics Platforms