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What is an ETL Process, and Why Your Project May Need an ETL Tool

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 to a unified format, and load it into your database. This is what the ETL process actually looks like.

What ETL stands for

ETL is an automated process of integrating data from multiple sources into a central repository, such as a database or a data warehouse. ETL includes three steps, defined by each letter of its name:

E – extract data

Data is extracted from a source or an array of sources onto a staging server. 

T – transform data

The extracted data is checked against a series of validation rules that transform data before loading into the central repository. These rules may include the conversion to a single format, sorting and/or ordering, cleansing, pivoting and many more. 

L – load data

The transformed data is loaded into the data warehouse or data lake in batches or all at once, depending on your needs.

So, if you need to automate data import from different sources into one database, you’ll need to implement an ETL process.

ETL or ELT: What is the difference? 

With ETL (Extract – Transform – Load), the data reaches the repository in its transformed state. ETL is used in the vast majority of data warehouses. 

With ELT (Extract – Load – Transform), the data is first loaded to the database and then transformed. ELT is typically used with massive data lakes and noSQL databases.

The difference between ETL and ELT lies in the presence of a staging server. Since an ELT pipeline doesn’t have it, the data load is faster than with ETL. You should use ELT processes when you need to load huge data sets to a data lake immediately. The drawback, of course, is that the repository gets unrefined data.

Difference between data warehouses and data lakes

A data warehouse is a repository of structured data from multiple sources. Data warehouses contain up-to-date data that can be used for reporting and analytics. A warehouse may comprise multiple tools to represent data and make it accessible.

Examples of cloud data warehouses: BigQuery, Microsoft Azure SQL Database, Amazon Redshift. 

A data lake is a repository of all structured and unstructured data that may require manual processing by the end user. The data in data lakes is used for reporting, advanced analytics and advanced machine learning. Read more about the differences between a data hub, a data lake and a data warehouse

Examples of cloud data lakes: Google Cloud Storage, Amazon S3, Apache Hadoop.

How to manage an ETL process and can I do this myself? 

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 providing system architecture for the entire pipeline
    • Manage the development of the system and documenting its requirements
  • Data modeling
    • Define data formats (models) required for the data warehouse
    • Outline the transformation stage
    • Define technologies and solutions to implement formatting
    • Testing
  • Data warehouse architecture
    • Define data warehouse architecture (data marts, accessibility, queries, metadata, etc.)
    • Define tools and solutions to load data
    • Testing
  • ETL pipeline development
    • Implement ETL tools to extract data from sources and upload 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 

For this, you’ll need an ETL developer or a data engineering team to build and maintain the ETL infrastructure. The team may include data architects, data engineers, data analysts, data scientists, and other dedicated experts who will manage the ETL process.

However, small to medium business owners are not necessary to carry such a burden of tasks. They can benefit from preset solutions with the ready-to-go integrations between data sources and data destinations. So, they won’t have much trouble with setting up their ETL pipeline. 

ETL pipeline

An ETL pipeline is a kind of a 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.

To tailor a fully customized ETL pipeline, data engineers may use different technologies, such as Python, R, Perl, SQL, etc. However, today there are many ready-to-use solutions that allow data engineering teams to cut much of the scope and speed up the setup. 

ETL tools

An ETL tool is a software that implements all steps of the ETL process:

  • E – extracts data from one or multiple sources
  • T – transforms data for the destination repository 
  • L – loads data to the destination repository 

ETL tools are mostly classified by hosting type:

  • Cloud-based: for example, Xplenty, AWS Glue, Skyvia,
  • On-premise: for example, Informatica PowerCenter, Talend, Pentaho.

Additionally, ETL tools can be differentiated by the time of data flow – batch and real-time solutions. 

Batch ETL tools, such as Informatica PowerCenter or IBM InfoSphere DataStage use batch processing – they complete the data pipeline in batches of ETL jobs. 

Real-time ETL tools, such as Hevo or Xplenty, provide the most efficient time-to-insight ratio by loading data in the shortest possible time.

How it works: ETL pipeline use case

Let’s take a look at a simple ETL pipeline in real life. A small e-commerce business uses multiple tools in operational activities, including Airtable, WordPress, Shopify, Hubspot, and Xero. Some data is also stored in three CSV files on a company Google Drive. These are the sources we will extract data from. This is the E step.

Google Sheets is the destination database where the historical data is to be stored. Actually, many small and medium-size businesses use spreadsheets as a database, so we will load the extracted data to Google Sheets. This is the L step.

You’ve noticed that the T step, data transformation, is missing. Actually, it’s not, since the data will be transformed (converted to a single format) by the ETL tool we will use – The tool provides out-of-the-box integrations for numerous apps, such as Pipedrive, WordPress, Airtable and others. You can also set up custom integrations via APIs, as well as extract data from CSV and XLSX files. Google Sheets is the target destination supported by Let’s see what the ETL pipeline setup looks like:


You need to install from the Google Workspace Marketplace.

Set up the necessary importers

We have five apps (Airtable, WordPress, Shopify, Hubspot, and Xero) and three CSV files to extract data from. In total, this is eight data sources, and we need to set up a separate importer for each. For this, open your target Google Sheets doc, go to the Add-ons tab, select => Open dashboard, and click the +Add Importer button. 

Select the corresponding importer (for example, Hubspot) and set it up. Here are the guides where you can learn how to set up each of them: doesn’t provide a ready-to-use integration with Shopify for now. However, you can use the JSON Client importer to extract data via Shopify API and load it to Google Sheets.

When setting up each importer, make sure to enable the automatic data refresh. This will automate the ETL pipeline according to the custom schedule you require.

Here is how the array of importers will look on the dashboard.

Click Import All, and will extract – transform – load data from your sources to Google Sheets. After the initial import, the data will be imported automatically according to the specified schedule. 

The described ETL pipeline is rather simple, since we did not do much data transformation. If required, you’ll have to do this step manually in your spreadsheet using QUERY, FILTER or other Google Sheets functions. So, in our case, we had an ETLT (extract – transform – load – transform) pipeline 🙂

Nevertheless, small business owners who rely on spreadsheets will benefit from this flow. Larger companies, of course, choose other tools and solutions to cover their ETL process needs. 

Best practices for choosing an ETL tool

ETL tools speed up the ETL pipeline and save time for ETL developers. So, the choice of the tool should be most efficient, otherwise all the benefits will be spoiled.

ETL pipeline requirements

Before analyzing the market of ETL tools, an ETL developer or another person in charge should define the current and expected requirements: 

  • Sources to extract data from 
  • Destinations to load data to
  • Rules to formatting data 
  • Maximum supported data volume 
  • Frequency of data updates (for real-time solutions)

These insights will shape a clear idea of what integrations you will be looking for. It’s crucial to look to the future and review the ETL tools from a long-term standpoint. So, take into account the data sources and/or destinations that you may be using in years to come.

Pricing model

The variety of ETL tools in the market results in a wide range of pricing models. Examine the options that you can afford, considering the defined requirements for your ETL pipeline. 

Set of ETL tools

You’re lucky to find one tool that will cover all your integration requirements. However, the truth is that you may be needed to onboard a few solutions for a single ETL pipeline. It’s OK and likely better than building a custom ETL process from scratch.

Do I need ETL 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!

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