The Basics of ETL Tools: 

  • Extract, Transform, Load (ETL) is the primary, older method of loading data from one system to another, but you may choose to use another method.
  • ELT (Extract, Load, Transform) is a newer method that extracts the data and immediately loads it onto the target or destination system before the data is transformed.
  • Most data warehouse use cases will leverage ETL, which supports complex transformation without requiring that it first be loaded on your server.
  • Due to its nature, the primary benefit of using ELT is its rapid ingestion speeds. You can achieve rapid transfer speeds because the data isn't being cleansed before it's dumped into your warehouse.
  • Choosing the right ETL tool is critical to your overall data warehouse structure, but you’re not alone in your search. There are a few options depending on their overall ETL needs.

Table of Contents

  1. The Basics of ETL Tools
  2. Overview of Big Data 
  3. What Is ETL?
  4. ETL vs. ELT
  5. ETL and OLAP Data Warehouses
  6. The Technical Aspects of ETL and ELT
  7. ETL: To Tool or Not To Tool
  8. Cloud-Based ETL Tools vs. Open-Source ETL Tools
  9. The Benefits of ETL Tools
  10. Final Thoughts

The Basics of ETL Tools

ELT (Extract, Load, Transform) is a newer method that extracts the data and immediately loads it onto the target or destination system before the data is transformed.

ELT (Extract, Load, Transform) is a newer method that extracts the data and immediately loads it onto the source system before the data is transformed before it's dumped into your warehouse.

Choosing the right ETL tool is critical to your overall data warehouse structure, but you’re not alone in your search. There are a few options depending on their overall ETL needs, data schemas, and operational structure. 

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If your business has a data warehouse, you've used ETL (Extract, Transform, Load). Whether loading data from your sales stack into your warehouse or constructing simple pipelines between essential 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?

Overview of Big Data 

More than 64.2 zettabytes of data will be up for grabs by next year. For businesses, that data represents golden opportunities for growth and success, both immediately and in the future. Businesses tapping into big data are seeing profits jump, particularly as more businesses ramp up in this post-pandemic era.

CEOs have been saying for years that failure to embrace big data will leave companies crippled and at a significant disadvantage. But now, 97% of businesses are investing in big data. Already, companies are using data warehouses at ever-greater numbers, with 54% of organizations already adopting data solutions. 

A massive 90% of business leaders say that data literacy is a key component of their company's success and continued growth. They also plan to use the data to make better business decisions about the efficacy of their marketing efforts and what’s going on with their customers. 

To leverage big data meaningfully, brands have three core tools:

While the data warehouse is 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 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 developing a warehouse solution.

But how does ETL work, and do you need to use an ETL tool to load data from one system to the next successfully? As we explain the importance of ETL and data warehouse, you’ll better understand how to analyze and use your data. 

What Is ETL?


ETL is a data integration process that encompasses three steps — extraction, transformation, and loading. In a nutshell, ETL systems take large volumes of raw data from multiple sources, convert them for analysis, and load that data into your warehouse. Let's cover the three primary ETL steps.


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

A big challenge during data extraction is how your ETL tool handles structured and unstructured data. Those unstructured items (e.g., emails, web pages, etc.) can be difficult to extract without the right tool. You may have to create a custom transfer solution if you choose a tool with poor unstructured data capabilities.


The data cleaning and organization stage is the transformation stage. Data from multiple source systems will be normalized and converted to a single format, improving data quality and compliance. 

ETL yields transformed data through these methods:

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

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Finally, data extracted to a staging area and transformed is loaded into your data warehouse. Depending on 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.


Extract, Transform, Load (ETL) is the primary, older method of loading data from one system to another, but you may choose to use another method. ELT (or Extract, Load, Transform) is a newer method that extracts the data and immediately loads it onto the source system before the data is transformed.

There are pros and cons to both ETL and ELT. Most use cases will leverage ETL, as not all warehouses are able to support the transformations via ELT.

If you have a smaller data set and data security is your top priority, ETL is probably the preferred option for your company. 

We see the value of ELT when we talk about massive data lakes, where you need more flexibility for processing unstructured and structured data. With ELT, you can perform basic transformations without data staging. Since there isn't a staging server, ELT is typically insufficient for your average query run. 

Because of its nature, the primary benefit of using ELT is its rapid ingestion speeds. You can achieve rapid transfer speeds because the data isn't being cleansed before it's dumped into your warehouse. You’re dumping raw data directly to your data warehouse, which may be cause for concern 

Therefore, we recommend 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. In most of your use-case scenarios, the ETL processes will allow you to better secure, consolidate, and use your data in ways that make the most business sense for your company. 

ETL and OLAP Data Warehouses

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Data engineers have been using ETL for more than two decades to integrate diverse data types into online analytical processing (OLAP) data warehouses. The reason for doing this is simple: to make data analysis easier. 

Typically, business applications use online transactional processing (OLTP) database systems. These are optimized for writing, updating, and editing the information inside them. They’re not good at reading and analysis. However, online analytical processing database systems are excellent at high-speed reading and analysis. ETL must transform OLTP information to work with an OLAP data warehouse. 

During the ETL process, information is:

  • Extracted from various relational database systems (OLTP or RDBMS) and other sources.
  • Transformed within a staging area into a compatible relational format and integrated with other data sources.
  • Loaded into the online analytical processing (OLAP) data warehouse server.

In the past, data engineers hand-coded ETL pipelines in R, Python, and SQL; and that can still be a laborious process that could take months to complete. Of course, hand-coded ETL may still be necessary in some cases, but you now have greater flexibility for how and when you’re required to tap into that cumbersome coding process. 

However, modern ETL solutions like allow data teams to skip hand-coding and automatically integrate the most popular data sources into their data warehouses. This seamless integration has dramatically increased the speed of setting up your ETL pipeline while eliminating the risk of human error in the validation process.

As a recent user already noted, “The speed and consistency of are impressive, and it more than makes up for what a few tools in our kit may be lacking." Our goal here is to precisely deliver what you need in your toolkit while providing the level of functionality that will continue to meet your data management needs in the future. 

Once the data is integrated into your data warehouse, the highly efficient nature of the OLAP data system allows you to access the stable, rapid analysis you need for your business. Then, you can pull across only the most updated data for future data transfer jobs. 

ELT and Data Lakes

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In contrast to the more widely used ETL, ELT introduces more flexibility into the data transformation/integration process. By loading data into a "data lake" instead of a structured OLAP data warehouse, you can upload and save many structured and unstructured information for use at a later time (which you can also accomplish with ETL).

ELT and data lakes take advantage of the high-powered processing offered by modern, cloud-based data warehousing solutions like Snowflake, Google BigQuery, and Redshift. These data warehousing solutions are so powerful that they can perform data transformations on the fly, allowing ELT to skip the staging area and transform only the data you need to analyze at the moment.

In other words, ELT performs transformations right before introducing the data to your BI tools. Because ELT and data lakes work with raw, unstructured information, they allow you to introduce unstructured information like emails and written customer survey responses to machine learning algorithms to derive new insights.

The Technical Aspects of ETL and ELT

It's essential to pay close attention to the following when designing your ETL and ELT processes:

  • Ensure accurate logging: It's vital to ensure your data system provides "accurate logging" of new information. To ensure accurate logging, you'll need to audit data after loading to check for lost or corrupt files. With proper auditing procedures, you can debug your ETL/ELT process when data integrity challenges arise (as they invariably do).
  • Flexibility to work with diverse sources of structured and unstructured data: Your data warehouse may need to integrate information from many incompatible sources like PostgreSQL, Salesforce, Cassandra, and in-house financial applications. Some of this information could lack the data structures required for analysis. You need to design your ETL/ELT process to deal with all forms of data—structured and unstructured.
  • Stability and reliability: ETL/ELT pipelines often crash and run into problems due to being overloaded. Your goal should be to build a fault-tolerant system that can recover after a shutdown so your data can move without getting lost or corrupted, even in the face of unexpected issues.
  • Designing an alert system: To ensure the accuracy of your business insights, an alert system that notifies you of potential problems with the ETL/ELT process is essential. For example, you’ll want to receive notifications and reports for expired API credentials, bugs related to third-party APIs, connector errors, general database errors, and more.
  • Strategies to speed up the flow of data: When data warehouses and BI platforms have access to information that is up-to-date, they offer better, more accurate insights at a moment’s notice. Therefore, it’s essential to focus on reducing data latency, i.e., the time it takes for a data packet to move from one area of the system to the next.
  • Growth flexibility: Your ETL/ELT solution should be flexible to scale up and down according to your organization’s changing data needs. This will save money on cloud-server processing and storage fees while providing the ability to scale up as required.
  • Support for incremental loading: Using change data capture (CDC) speeds up the ETL process by permitting incremental loading. This lets you update only a small part of your data warehouse while ensuring data synchronicity.

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 do the whole ETL process by hand?

This is a complex question, so the answer is not simple. The best process for your business will 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 custom code a Python solution for your ETL needs. If you're handling a more significant job, you can use workflow orchestrators like Apache Airflow or simply use pandas to create a solution. When we talk about ETL tools, we mean full-blown ETL solutions. 

Apache Airflow and Luigi qualify as tools, but so do many cloud-based tools on the market. So, you must determine what type of ETL tools you need, whether a cloud-based or open-source tool may be more effective, and which option(s) will best deliver the results for your company. 

Cloud-Based ETL Tools vs. Open-Source ETL Tools

Choosing the right ETL tool is critical to your overall data warehouse structure, but you’re not alone in your search. There are a few options depending on overall ETL needs, data schemas, and operational structure.

Cloud-based ETL tools like 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 various ETL needs, mainly if most 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. There is an open-source ETL tool for almost any unique ETL need.

The downside, of course, is that you'll need lots of custom coding, setup, and staff hours to get the ETL operational. Support and maintenance of the open source ETL tools are MUCH more difficult on the customer side as well. You may 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 hand-code each ETL process if preferred. So, why bother?

  • Scalability: Trying to scale our 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, you have unlimited scalability at the click of a button.
  • 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 life-changing. The benefits of having all of your needs layered into one tool save you time, resources, and lots of headaches.
  • Out of the box: While open-source ETL tools like Apache Airflow require some customization, cloud-based ETL tools like work out of the box.
  • Compliance: The overwhelming nature of modern data compliance can be frightening. Between GDPR, CCPA, HIPAA, and all other compliance and privacy needs, using an ETL tool that bakes compliance into its framework is an easy way to skip complicated and risky compliance setups.
  • Long-term costs: Hand-coded solutions may be cheaper up-front but will cost you in the long run. The same thing could be said about open-source ETL tools. Since you must 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 allows you to create rapid, hyper-visualized data pipelines that are infinitely scalable and intuitive. With tons of integrations, service hooks for your existing monitoring systems, and natural elasticity and scalability, 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 powerful platforms like Salesforce?
  • A G2 certified leader in the ETL space?

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 must choose between hand-coding their ETL process, coding with an open-source tool, or using an out-of-the-box cloud-based ETL tool. 

Final Thoughts

The ETL tool you choose will significantly impact your daily workflows, so we recommend researching and heavily vetting your tools pre-adoption based on your target database. If you have questions, call our team to learn how can solve your unique ETL challenges.