You must be wondering about the meaning of ETL. So, what is ETL?
It might sound like some sort of funny slang or abbreviation for a videogame or anything like that, but we promise that is not. ETL might be the answer for working with loads of data.
Well, simply put, it can be considered the holy grail for data processing.
In a time when organizations and companies are now dealing with escalating amounts of data, ETL has something to say. It works its magic to undercover the most valuable data-driven insights hidden among the chaos.
What is ETL?
First things first. Let’s break this thing down!
ETL stands for: Extract, Transform and Load.
By definition, it is a data integration process referring to the previous mentioned – interrelated – steps (Extract, Transform and Load) used to synthesize, blend or combine data from multiple sources into a single database, data hub, data store, data warehouse, or data lake.
Throughout the process, data is extracted from a source, then converted (a.k.a. transformed) into a new format that will be analyzed and loaded into a database or other system, thus driving business decisions, whilst improving performance.
ETL: step by step
As we said before, ETL defines the end-to-end process of data processing, starting with a full extent of data (both structured and unstructured data and derived from several sources and/or teams across the world) to the point where it’s useful for business purposes.
This is how things go down:
Step 1: Extraction
This is the first step of ETL. It’s where raw data is copied, retrieved or exported from one or more data source’s locations to a staging area.
Those sources can be multiple, such as:
- Relational and non-relational databases
- Web pages / Email
- On-premises
- Legacy
- Analytics and monitoring tools
- SaaS applications (namely CRM – Customer Relationship Management – and ERP – Enterprise Resource Planning – systems)
- Flat files (like XML, JSON, CSV, Microsoft Excel spreadsheets, or others)
- APIs (Application Programming Interfaces)
- System logs and metadata
- SQL or NoSQL servers
ETL falls into two distinct categories: batch ETL (which is the traditional way of performing ETL – in batches, given that it collects data sourced within specified time intervals) and real-time ETL (also known as streaming ETL or stream processing ETL), where data goes through the ETL pipeline as soon as it’s made available by a data source).
Then when the extraction of the data is loaded to a staging area, it leads us to the next step.
Step 2: Transformation
So now that the data has been retrieved from multiple sources, it’s time to transform it so that it can be properly used. This involves taking the data, cleaning it, and putting it into a common format, which is now suitable for storing in the intended database, data warehouse, data store or data lake.
Through the transformation process there’s a lot taking place to ensure data quality and accessibility, namely:
- Clean the data to take out all the duplicates and out-of-date records;
- Filter, validate and authenticate the data;
- Rearrange unstructured data into the pretended structured format;
- Performing audits to ensure data quality and compliance;
- Organize the data in a certain order;
- Merge multiple tables;
- Limit the extracted data to specific fields;
- Remove, encrypt or even protect data overseen by industry or governmental regulators;
- …
This is considered to be the most important step of the ETL process. It ensures improve data integrity and makes it usable.
Step 3: Loading
Last but now least: the loading process. This is where true magic happens.
By now all the collected data is fully formatted and ready to be inserted into the target database/data hub/datastore/data warehouse/data lake (whatever it is that you targeted). And that’s what loading is: it’s the process of inserting that formatted data into the target destination.
For companies using ETL, this is quite a simple, automated, well-defined, continuous and batch-driven process, and it usually takes place during off-hours, thus ensuring that the traffic on the source systems occurs when the data warehouse has less usage.
Why is ETL important for companies and organizations?
ETL has been evolving over the years and it’s now considered a must-go for companies dealing with an ever-growing data flow coming from multiple sources.
So, with data increasingly complex data strategies ETL makes it possible for organizations to transform massive quantities of data into usable business intelligence for analysis, allowing to:
- Deliver a single point-of-view – through ETL companies and organizations can combine databases and multiple forms of data into a single and unified view, making it easy to analyze and make sense of large data sets;
- Provide historical background – it allows combining legacy data from a company or organization alongside new data collected from recent platforms or sources, giving a long-term view of the data;
- Improve efficiency and productivity – the ETL process and software automates the data migration process, thus allowing developers to devote their time to innovation.
Here are some ETL uses
Like we said multiple times before, ETL enables organizations to gather all the relevant intel in one place, making it easy to analyze and/or use. Based on this executives, managers, and stakeholders become able to make informed business.
Here’s how ETL is commonly used:
Data warehousing
ETL is frequently used to move data to a data warehouse.
Machine Learning and Artificial Intelligence
Machine Learning (ML) is used to make sense of data without using programming analytical models, instead, the system learns by using Artificial Intelligence (AI) techniques. ETL can be used to move the data for ML purposes.
Marketing data integration
ETL can also be used to collect and prepare marketing data (such as customer, social networking, and web-analytics data) into one place to be analyzed and develop marketing plans.
Internet of Things data integration
Internet of Things (IoT) collects connected devices capable of gathering and transmitting data through sensors embedded in hardware. With ETL, it’s possible to move data from multiple IoT sources to a single place to be analyzed.
Database replication
With ETL data can be replicated from source databases – like Oracle, Cloud SQL for MySQL, Microsoft SQL Server, Cloud SQL for PostgreSQL, MongoDB, or others into a single data warehouse.
Cloud migration
ETL is used to run data and applications migration from on-premises to the cloud, allowing companies to make applications more scalable, to secure their data while saving some money.
The top ETL tools for 2021
ETL is now essential for companies and organisations, but luckily there are plenty of ETL software tools available.
When it comes to picking a tool for your business, there are just so many great options out there, it’s quite a challenging task to choose the one.
To help you out, we partnered with Ricardo Fernandes, co-founder of InnerData, to put together a top 10 list of ETL tools:
What’s in the bag for the future ETL?
The traditional ETL system is based on relational processing, where due to the data increases, which leads to performance deterioration.
Plus, this model follows a Schema-on-Write approach, used in database architectures. This means that it starts with a schema definition, following the data processing/transformation and finally loading the data, which is read and then comes back to the schema defined in the first place. This means that one has to know, first-hand, what is going to be used.
So, what’s next?
ELT. Or let’s just say Extract, Load, and Transform.
With this – small – order change, data gets leveraged via a data warehouse to do basic transformations. The ELT process works closely with data lakes, a specific kind of data store which accepts any kind of structured or unstructured data.
These don’t require any data transformation before loading it. Meaning that any type of raw data can be immediately loaded into a data lake, despite the format or absence of data whatsoever.
This represents a major advantage when compared to ETL. The ELT enables bigger flexibility and simplicity in storing new and unstructured data. ELT also allow saving any type of information without any sort of previous transformation and structuring, providing immediate access to the data.
