ETL, short for extract, transform, and load is an integration process that merges data from multiple sources into a single consistent data warehouse.
The E in ETL stands for extraction, which is the process of extracting the data from a given source(or multiple sources. Moving on, the T which stands for transformation is the process of transforming the data format from the multiple data sources provided into a single consistent format, this helps in keeping the data consistent. Lastly, the L which stands for loading is the process of storing the final transformed data into a single data source.
Originally created for big data collection, the ETL process was first introduced to allow for computational data analysis more frequently used in data products.
While ETL can be performed alone, there are two different additional layers that can be added to help in the ETL process. These two processes are data ingestion and data wrangling. In the rest of this article, we will explain what is the difference between data ingestion and data wrangling along with when to use which layer. Note that both of these processes can be utilized with the addition of adding the ETL layer after it.
Data Ingestion
Data ingestion is a broad concept that specifies how data can be extracted and imported from multiple sources into a single data source for immediate use. The main idea behind performing data ingestion is the collection of similarly formatted data at a quick and fast pace. Data ingestion focuses mainly on the movement of the data from one source to the next, without giving much thought to any required data transformation.
The data ingestion process contains 4 layers, starting with processing the incoming data, prioritizing and validating data, and finally the storage of data for immediate use.
Data ingestion comes in 3 different types: real-time processing, batch-processing, and micro-processing. Each data ingestion category affects how much data is ingested per a given amount of time.
While ETL can be thought of as a pipeline that performs the specific process of extracting, transforming, and loading data, data ingestion focuses more on how the data is sourced and stored for further use, thus performing data ingestion alone is not sufficient in most cases and it must be followed by an ETL pipeline to transform the data into a single format.
Data Wrangling
On the other hand, we have data wrangling. Although the ETL process can be used within a data wrangling process or by itself, at its core the ETL process is used for simple format semi-structured data (SQL databases), while data wrangling is more specialized in storing data of complex, unstructured (nonSQL databases), and diverse formats. Such data may be in a raw format having no limits on how unstructured it may be.
Data wrangling requires a great understanding of the data set at hand. A general idea of what the user is trying to achieve from the data at hand is necessary, prompting lots of work that needs to be performed prior to performing the ETL process.
The data wrangling pipeline starts with data discovery, structuring, cleaning, enriching, and finally validation of the data.
It is worth noting that in most cases performing data wrangling is not sufficient as it also needs to be followed by an ETL pipeline. As data wrangling performs the process of extracting and transforming data, data wrangling is used to transform a complex structured data set into a more structured one, after that the ETL process is used to transform the simple extracted data into a single format and finally store the data in a single source dataset.
Data Ingestion vs Data Wrangling
Although they may seem similar initially, data ingestion and data wrangling are different at their core, as they perform different tasks. For starters, data ingestion focuses mainly on the process of moving data from one or multiple sources to a single data source. In cases where different data formats are extracted from different data sources, a simple ETL process will be required to have a final consistent data set to perform the necessary data analysis on, which is done using data products.
On the other hand, data wrangling is quite similar to the ETL process but on a more complex scale. While data wrangling also known as data cleaning, data remediation, or data munging, is also used to merge different data sources into a single storage space, data wrangling is more accustomed to dealing with big data inconsistencies, such as missing data points(gaps or empty cells), deleting irrelevant data, merging different data formats into a single one, and so on. All in all making sense of hard to work with data.
Conclusion
To gather data different measures will be required depending on the data at hand. In cases where fast data loading is required especially for structured or semi-structured data sets then the best option is to perform data ingestion followed by the ETL process in order to transform the data into a final and consistent form. While in cases where the data provides weak structure and inconsistency be it missing data points, gaps, or irrelevant data then utilizing data wrangling and then ETL will return the best results.
Whether it is to create a machine learning model, analyze customer data for further profits, or trying to identify hidden patterns, a consistent and reliable database is of utmost importance to start with.
