If your business has a data warehouse, then you've used ETL (or Extract, Transform, Load). Whether you were loading data from your sales stack into your warehouse, or you were constructing simple pipelines between basic apps, ETL is the lever that unlocks the value of your data warehouse.

But what is ETL? And what kind of ETL tool should you choose to help you flesh out your ETL experience?

Let's talk about the basics of ETL and ETL tools.

The Rise of Big Data

By next year, there will be over 44 trillion GB of data up for grabs. For businesses, that data is gold. Businesses that are tapping into big data are seeing profits jump from 8 - 10%. Plus, around 80% of CEOs say that failure to embrace big data will leave companies crippled and at a significant disadvantage.

So, it's no wonder that data warehouses are considered a significant asset by over 70% of businesses this year. A massive 90% of IT leaders plan on increasing BI spending this year to accommodate this landfall of data.

When it comes to leveraging big data meaningfully, brands have three core tools:

  1. Data warehouses
  2. ETL tool
  3. BI tools
  4. Cloud-based ETL Tools vs. Open Source ETL Tools

While the data warehouse acts as the storage place for all your data and BI tools serve as the mechanism that consumes the data to give you insights, ETL is the intermediary that pushes all of the data from your tech stack and customer tools into the data warehouse for analysis. The ETL phase is where your business will spend a good chunk of its time and energy when developing a warehouse solution.

But, how does ETL work, and do you really need to use an ETL tool to successfully load data from one system to the next?

What is ETL?

ETL (or Extract, Transform, Load) is a process of data integration that encompasses three steps — extraction, transformation, and loading. In a nutshell, ETL takes raw data from multiple sources, converts it for analysis, and loads that data into your warehouse. Let's cover the three primary ETL steps.


In the first step, data is extracted from a source (e.g., Salesforce, Google AdWords, etc.) into a staging area. The staging area acts as a buffer between the data warehouse and the source data. Since data may be coming from multiple different sources, it's likely in various formats, and directly transferring the data to the warehouse may result in corrupted data. The staging area is used for data cleansing and organization.

A big challenge during the extraction phase is how your ETL tool handles structured and unstructured data. All of those unstructured items (e.g., emails, web pages, etc.) can be difficult to extract without the right tool, and you may have to create a custom solution to assist you in transferring unstructured data if you chose a tool with poor unstructured data capabilities.


The data cleaning and organization stage is the transformation stage. All of that data from multiple source systems will be normalized and converted to a single system format — improving data quality and compliance. During the transformation stage, data may undergo any of the following:

  • Cleaning
  • Filtering
  • Joining
  • Sorting
  • Splitting
  • Deduplication
  • Summarization


Finally, data that has been extracted to a staging area and transformed is loaded into your data warehouse. Depending upon your business needs, data can be loaded in batches or all at once. The exact nature of the loading will depend upon the data source, ETL tools, and various other factors.


While ETL is the primary method of loading data from one system to another, some choose to use another method. ELT (or Extract, Load, Transform) extracts the data and immediately loads it onto the source system BEFORE the data is transformed. You'll typically see ELT in use with Hadoop clusters and other non-SQL databases.

There are pros and cons to both ETL and ELT. But, the vast majority of data warehouse use cases will leverage ETL. We start to see the value of ELT when we talk about massive data lakes. Since there isn't a staging server, ELT is typically poor for your average query run. The primary benefit driving ELT is that it has rapid ingestion speeds due to its nature. That being said, the speed is rapid because the data isn't being cleansed before it's dumped into your warehouse.

We heavily recommend that you avoid ELT processes unless you're working on a data lake project or something that requires a bunch of data immediately, regardless of the value of that data.

ETL: To Tool or Not to Tool

Should you use ETL packaged tools OR should you patch together a library, framework, and other open-source solutions? Better yet, should you just do the whole ETL process by hand?

This is a complex question. It will really depend on your business needs, time commitment, schemas, integrations, and overall ETL needs. If you're looking to perform a few really simple jobs, you might be able to custom code a Python solution for your ETL needs. If you're handling jobs that are a little bigger, you can use workflow orchestrators like Apache Airflow, or you can simply use pandas to create a solution.

When we talk about ETL tools, we mean full-blown ETL solutions. So, Apache Airflow and Luigi certainly qualify as tools. But, so do many of the cloud-based tools on the market.

Cloud-based ETL Tools vs. Open Source ETL Tools

Choosing the right ETL tool is a critical component of your overall data warehouse structure. There are a few different options that businesses can choose depending upon their overall ETL needs, data schemas, and operational structure.

Cloud-based ETL tools like Xplenty offer rapid, real-time streaming, quick integrations, and easy pipeline creation. The primary benefit of cloud-based ETL tools is that they work immediately out-of-the-box. Plus, they're hyper-useful for a variety of ETL needs, especially if the majority of your warehouse exists in the cloud (i.e., Redshift, Snowflake, or Big Query).

Open source ETL tools come in a variety of shapes and sizes. There are ETL frameworks and libraries that you can use to build ETL pipelines in Python. There are tools and frameworks you can leverage for GO and Hadoop. Really, there is an open-source ETL tool out there for almost any unique ETL need. The downside, of course, is that you'll need lots of custom coding, setup, and manhours getting the ETL operational. Even then, you may find that you need to tweak your ETL stack whenever you introduce additional tasks.

The Benefits of ETL Tools

Why use ETL tools in the first place? After all, you can certainly hand-code each ETL process if you were so inclined. So, why bother?

  1. Scalability: Trying to scale-out hand-coded ETL solutions is difficult. As schema complexity rises and your tasks grow more complex and resource-hungry, establishing solid pipelines and deploying the necessary ETL resources can become impossible. With cloud-based ETL tools like Xplenty, you have unlimited scalability at the click of a button.
  2. Simplicity: Going from a hand-coded ETL solution using SQLAlchemy and pandas with rpy2 and parse to something as simple as a cloud-based ETL can be lifechanging. The benefits of having all of your needs layered into one tool saves you time, resources, and lots of headaches.
  3. Out-of-the-box: While open source ETL tools like Apache Airflow require some customization, cloud-based ETL tools like Xplenty work out-of-the-box.
  4. Compliance: The overwhelming nature of modern data compliance can be frightening. Between GDPR, CCPA, HIPAA, and all of the other compliance and privacy nets, using an ETL tool that bakes compliance into its framework is an easy way to skip difficult and risky compliance setups.
  5. Long-term costs: Hand-coded solutions may be cheaper up-front, but they will cost you in the long run. The same thing could be said about open source ETL tools. Since you have to spend time and energy on modification, you're forced to onboard early or risk delaying project launches. Cloud-based ETL tools handle maintenance and back-end caretaking for you.

Why Xplenty?

Xplenty gives you the power to create rapid, hyper-visualized data pipelines that are infinitely scalable and incredibly intuitive. With tons of integrations, service hooks for your existing monitoring systems, and untouched elasticity and scalability, Xplenty has the features you need to create a successful data warehouse.

Do you want:

  • An incredibly dynamic interface that lets you build scalable pipelines without code?
  • A pipeline tool with a REST API connector for Rest Web Services?
  • The ability to ETL to major platforms like Salesforce?
  • A G2 certified Leader in the ETL space?

If so, Xplenty may be for you! Contact the Xplenty team now to learn how to accomplish all of the above.

Final Thoughts

ETL (Extract, Transform, Load) is a process that loads data from one system to the next and is typically used for analytics and queries. There are plenty of ETL tools on the market. Most businesses will have to choose between hand-coding their ETL process, coding with an open-source tool, or using an out-of-the-box cloud-based ETL tool. The ETL tool that you choose will significantly impact your daily workflows, so we recommend doing the research and heavily vetting your tools pre-adoption.