If you are considering a career in data, more so, engineering and or analytics you probably have come across this two terms, ETL and ELT now they might look plenty complicated at first glance but I am about to explain to you both, in simple straight forward words.
So lets dive right in:
just what is ETL?
Extraction transformation and loading or simply known as ETL is a fundamental part of gathering data from multiple sources and it follows exactly that order that is stated in the name, it simply brings together very key and fundamental principles of not only assembling data but also refining it and making it ready for experts and laymen alike, to the final product achieved.
let's break it down together
EXTRACTION
As the name states this is the process by which data gurus source raw data from multiple places, in its most unstructured and random of forms, this sources can include:
- databases
- Application programming interface(API's)
- Internet Content
- economy data
- Real estate data
- weather data
- Surveys and Interviews
It is however, to be noted that sources of data are not only limited to the above.
so just what is the purpose of this raw data collection, and in comes:-
TRANSFORMATION
You guessed it it's actually all in the name, transformation simply refers to the ways by which data is cleaned, structured standardized and made suitable for both analysis and storage.
There are several key important ways and reason as to why raw data requires transformation this are:
- Data cleaning.
This simply involves removing errors, unwanted values and or null values.
- Structural conversion.
Very key in adding or removing columns and rows for either aesthetics purposes or relevance, standardizing ranges as well as putting the structure in itself incase you are working with an unstructured form of raw data.
- Destructive Transformation.
Sometimes a data set can have bits and pieces that are either not needed or have been made irrelevant by various reasons, this could even be data already in storage this formula removes such unwanted information.
- Attribution.
This Attribution process simply is the restructuring of various features of raw or already existing data, to for example, if you already have date of birth you can simply add a new feature under "age", same case for if you have total price and production cost you can change both to "profit" or "loss" for simpler analysis.
In doing this things data experts employ tools like:
- Microsoft excel
- Microsoft Power BI
- Databased (DBT) tools for SQL
- Pandas(Python)
- Informatica
Safe to say that data cleaning is one of, if not the most important part of data integration, once data safely passes through this process it is ready for:
LOADING
Data Loading as the name suggests is the process of moving data from a source into a target system, usually for storage, analysis, or processing.
data can be loaded into either:
- Data lake
- Data Warehouse
- Staging area
- Repository for analytics and reporting
That is a simple ETL pipeline at a glance.
Here is a simple diagram of an ETL data integration method.
ELT
ELT as the name suggests and drawing from my above explanation simply changes between transforming before loading as in ETL and instead choses to load before transforming ELT this can be for several main reasons this include:
- Scalability.
Modern cloud data warehouses have been designed to handle massive amounts of data, and are able to handle transformations at much higher speeds while also saving greatly on cost unlike past tools where you needed several tools to do the job.
- Agility and flexibility.
Since the raw data is already loaded data analysts and scientist can create new models and transformations easily as business needs changes inevitably with time, without necessarily having to alter or overhaul the initial ingestion pipeline.
- Simplified pipeline management.
Seeing that data is loaded immediately from extraction it reduces the total workflow and or workload needed in the transformation process and reduces the amount of tools needed for data integration.
- Reduced Ingestion Time.
By doing away with the transformation process it greatly reduces the amount of time it takes for data to be made available in the target system, which is sometimes critical for real time or near real time reporting.
- Support for Unstructured Data.
ELT is best suited for handling unstructured data such as JSON files, images and videos as it involves storing them in their raw form and only transforming them at the demands of analysis.
Below is a simple ELT data integration illustration.
When to use ETL VS ELT
Complex Analytics
ETL and ELT can both be used for complex analysis that will have multiple data formats from varied sources, data Engineers may set up ETL pipeline for some of this sources and or target databases then use ELT for various others depending on need and of course cost.
IOT Applications
Internet of things(IOT) applications that tend to use sensor data streams will often prefer ETL over ELT to for example:
- Receive data from different protocols and convert it into standard data formats for use in cloud workload
- Cleanse, deduplicate, or fill missing time series data elements.
- When calculating values from data sources that differ on the local device, and send filtered values to the cloud backend.
- In filtering high-frequency data, performing average functions on large datasets, then loading averaged or filtered values at a low cost.
Experimentation
Data Engineers sometimes due to one reason or another, or even due to business demands will conduct experiments that are crucial of course to the growth of any business, this are aimed at:
- discovering new data sources for analytics
- Trying out new ideas to answer business queries
- help businesses move with changing times
- Cut cost
Among others, ETL here is more suitable as it employs different tools in data pipelines thus can give a more in depth review of both data structures and the data itself.
In conclusion
On whether to use ETL or ELT in your business, the simple answer is there is no simple answer. However, both of this can be used separately or in a complimentary manner depending on the needs and size of a business or on the operation in question.
It also depends on other factors such as:
- Overall cost
- flexibility
- Scalability
- Security
- speed
- personnel experience
Among others, both are however equally up to the task and can handle well when used properly and efficiently.
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