Companies use different tools to store their operational data. For example, development teams use Jira, UX designers – Figma, marketers – Google Analytics, and so on. To make the data accessible for reporting, analytics, and machine learning models, it should be stored in a single place or repository. For this, you need to extract data from the sources, transform the data into a unified format, and load it into your database. This is what the ETL process actually looks like.
What is ETL
ETL is an automated process of integrating data from multiple sources into a central repository, such as a database or a data warehouse. It includes three steps which, if you look at their first letters, explain the ETL meaning well:
Being an integral element of business intelligence systems, ETL automates the process of data transfer from source to destination and contributes to discovering valuable business insights.
Benefits and challenges of ETL
|Dataflow automation||Data latency|
|Improved maintenance and traceability||Data source limitations|
|Enhanced performance to BI||Data validation|
|Improved data quality||Resources for implementing ETL|
|Information clarity and completeness||Data corruption due to ETL pipeline errors|
The more spread the data is across different apps, the harder it is to get the big picture of the business. Having a reliable, automated ETL solution can have a paramount impact on an organization’s ability to understand its own data and make informed decisions. At the same time, automations save time and help avoid common errors that typically arise when data is moved manually between different apps or locations.
And once you have an ETL process sorted out, you’re bound to see some positive outcomes for your organization. It could mean, for example, retaining more customers as Tradezella did. For others, organizing their data can open up opportunities for revenue growth, as was the case for Mailtrap.
ETL and data integration
In view of the above, you may have a concern about what’s the difference between an ETL process and a data integration process. Both concepts are closely related, and, as a matter of fact, Extract Transform Load is an approach to data integration.
The main difference is that data integration does not involve transforming data to provide you with a unified view of your data taken from multiple sources. The Extract Transform Load process changes the format of information at the transformation step.
You may need to know what the difference is between both terms to make a proper choice.
ETL and reporting
These two concepts are complementary to each other within business intelligence:
- An ETL process is responsible for collecting information from data sources and transferring it into one repository.
- A reporting process is responsible for accessing the information in the repository and presenting it to the stakeholders in a legible format, such as a dashboard.
Each concept can exist separately from another. However, if you use a bundle of ETL + reporting, you can benefit from their synergy in terms of automating reporting and speeding up the decision-making.
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How ETL works
To automate data import from different sources into one database, you’ll need to implement an Extract Transform Load process, which covers the following steps:
E – Extraction of data
At the first step of the ETL process, the data is extracted from a source or an array of sources onto a staging server. The sources from which the data is extracted can include:
- Databases and data warehouses (Airtable, BigQuery)
- Cloud storage (Google Drive, OneDrive)
- E-commerce platforms (Shopify, WooCommerce)
- Analytics services (Google Analytics, Mixpanel)
- Project management tools (Trello, Jira)
- CRM tools (Pipedrive, HubSpot)
- And many more
T – Transformation of data
The extracted data is checked against a series of validation rules that transform data before loading it into the central repository. These rules may include:
- Conversion to a single format
- Sorting and/or ordering
- and many more.
L – Loading of data
The transformed data is loaded into the destination (data warehouse, data lake, or another repository) in batches or all at once, depending on your needs.
What is an ETL pipeline
An Extract Transform Load pipeline is a kind of data pipeline in which data is extracted, transformed, and loaded to the output destination. ETL pipelines typically work in batches, i.e., one big chunk of data comes through ETL steps on a particular schedule, for example, every hour.
ETL technology – example
Let’s take a look at a simple ETL pipeline in real life. A small e-commerce business uses different tools in its operational activities – some for tracking sales, others to run marketing, accounting, logistics, and more. They chose to employ an ETL platform – Coupler.io in this case – to get all their data into one place and analyse it. They hope to understand better where their business is at the moment and make future decisions in a data-driven way.
Sales data of this e-commerce company is spread between HubSpot and Airtable, with some historical data residing also in a .csv file.
Their first step will be extracting all the relevant data from all three sources. As you can guess, this is the E (Extract) in ETL. Using Coupler.io, they connect to each tool and decide what to fetch. Here’s a sample setup:
Next, they aim to Transform the data. Rarely, the raw data exported from apps is ready for analysis right from the get-go. Much more often, you need to pick the columns you need for your analysis, apply filters, perform some calculations to derive new metrics, etc. All of this is possible in Coupler.io, so our e-commerce company gladly takes their time preparing its data for analysis.
Finally, it’s time to load the processed data. There are several options here. With Coupler.io, you can load data to a spreadsheet app (Excel, Google Sheets), a data warehouse (BigQuery), or directly into a data visualization tool (Looker Studio). The company we discuss chooses a Google Sheets file where the data is quickly brought.
Once set up, an ETL process will run automatically according to a schedule they choose, for example, daily.
Coupler.io offers over 200+ integrations, with more and more coming soon. You can give it a try on a free 14-day trial, no credit card is required. Here are some of the popular integrations available:
- Airtable to Google Sheets
- WordPress to Google Sheets
- Hubspot to Google Sheets
- Xero to Google Sheets
- CSV to Google Sheets
- Shopify to Google Sheets
How to manage an ETL process
If you’re planning to build a custom ETL pipeline from scratch, you’ll have to handle a lot of tasks, such as:
- ETL process management
- Outline the Extract – Transform – Load process and provide system architecture for the entire pipeline
- Manage the development of the system and document its requirements
- Data modeling
- Define data formats (models) required for the data warehouse
- Outline the transformation stage
- Define technologies and solutions to implement formatting
- Data warehouse architecture
- Define data warehouse architecture (data marts, accessibility, queries, metadata, etc.)
- Define tools and solutions to load data
- ETL pipeline development
- Implement ETL tools to extract data from sources and upload it to the staging area
- Set up the data formatting processes, such as cleansing, mapping, filtering, etc.
- Implement the process of loading the formatted data to the data warehouse
- Validate data flow
- Test speed
You’ll need an ETL developer or a data engineering team to build and maintain the ETL infrastructure. The team may include data architects, engineers, analysts, scientists, and other dedicated experts who will manage the ETL process.
However, small to medium business owners do not need to carry such a burden of tasks. They can benefit from preset solutions with ready-to-go integrations between data sources and data destinations. So, they won’t have much trouble with setting up their ETL pipeline.
ETL future development
The traditional Extract Transform Load concept is a good solution for relational processing. However, its main limitation should be taken into account:
When the data volume goes up – the ETL performance goes down.
In view of this, some data analysts forecast the future transformation of ETL into ELT pipelines. The Extract Load Transform approach lets you get your data as-is and manipulate it immediately. This is beneficial for unstructured or semi-structured information and lets you start getting value from it immediately.
At the same time, the more obvious scenario of ETL evolution includes the advent of data management frameworks. This means that traditional ETL will combine data integration with data management. So, the users will have an out-of-the-box hybrid solution to streamline their ETL pipelines.
Do I need an Extract Transform Load process for my project?
You tell us 🙂 If you leverage multiple tools in your workflow and spend hours grabbing data from them manually, then you MUST implement an ETL pipeline. This will save you tons of time and, I believe, budget.
No business can survive without a proper analysis of data taken from different sources. Analyzing data is crucial, and the best way to do this is when you have all your data in one place. Good luck!Back to Blog