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ETL Pipeline

Reliability, performance, and flexibility are the main quality criteria for data systems. On-premise ETL systems can offer security and privacy, but they can be slow due to insufficient power and performance of processors, networks, disks, and local computing clusters. In addition, over time, new data formats emerge, and radically different requirements are imposed, reducing the appeal of once-popular solutions. So what to do?

We at Data Crafts know all about it, as we have extensive experience creating data management systems. In this article, we will reveal the essence and capabilities of the ETL Pipeline and compare ELT vs ETL so that you can choose the best data management methodology for your business.

What Is an ETL Pipeline?

So, let’s define ETL meaning. ETL, which stands for Extract Transform Load, refers to the conventional methods used for loading data from various existing systems (CRM, social media platforms, web reporting, etc.) into Data Warehouses or Data Lakes. This involves extracting the required data from the sources and then aligning it with the parameters and functions of the target base before loading it into the Analytics database or Data Warehouse.

ETL process in detail

ETL, which stands for Extract Transform Load, refers to the conventional methods used for loading data from various existing systems (CRM, social media platforms, web reporting, etc.) into Data Warehouses or Data Lakes. This involves extracting the required data from the sources and then aligning it with the parameters and functions of the target base before loading it into the Analytics database or Data Warehouse.

ETL benefits

ETL process offers several benefits to businesses and we will explore some of the key benefits of ETL in more detail.

  • Centralization and standardization of data make it readily available for analytics and decision-making;
  • Freedom from the technical tasks of moving and maintaining data allows you to focus on more focused work;
  • Deeper analytics once the information from the underlying transformation is exhausted;
  • Moving to cloud-based software services simplifies data processing. Instead of batch processing, they can implement continuous processing methods without compromising current processes

Most tools work optimally with on-premises storage servers, making the entire process costly, but they comply with HIPAA, CCPA, and GDPR standards. The methodology eliminates sensitive or important data to protect it from hackers before sending it to storage. 

The ETL approach, which combines Data Ingestion platforms like Airbyte, Fivetran, and Stitch with Cloud Data Warehouses like Google BigQuery, Redshift, and Snowflake, eliminates problems such as pre-transformation, manual encoding, and data cleaning.

Here are some advantages of such a solution:

  • Separate workload clusters allow you to run different computing clusters while maintaining data consistency and integrity;
  • High-performance JSON queries enable complex analytics;
  • Data warehouse resources can be scaled up without disrupting existing questions;
  • Data can be recovered in up to 90 days

Capacity-based computation partitioning makes it easier to change data in the warehouse. You can use ETL to provide greater agility for your IT and analytics departments. Ultimately, it empowers decision-makers and gives them a competitive advantage. 

It’s a great approach when you need a specific data format because it changes the data before it’s uploaded. It works best when there is a mismatch in supported data types between source and destination, limitations in the target server’s ability to scale, or the need for access to a community of experts.

ETL disadvantages

Nevertheless, traditional solutions can slow down your growth and efficiency because:

  • Local infrastructure requires increased hardware and maintenance costs;
  • Scalability is insufficient as data volume and speed requirements are growing and cannot always be met in the local environment;
  • There is a risk of losing some data during transformation;
  • Local pipelines are less reliable;
  • There is no support for data lakes

What is an ETL data pipeline?

ETL data pipeline is a broader concept of getting up-to-date data. The process includes copying, moving data from on-premises to cloud storage, and merging with other sources. It refers to the entire available set of processes applied when moving from data and does not necessarily include conversion or loading. For example, the upload process may activate another workflow.

ETL data pipeline

What Is ELT?

ELT (Extract Load Transform) is a data integration process that involves extracting data from various sources and loading it into a target system, such as a data warehouse or data lake, before transforming the data into the desired format.

This methodology uses the above processes in a slightly different sequence: 

  1. Extraction;
  2. Loading to an intermediate server;
  3. Transformation within the processing server

The data arrives at the storage (on the target server) without processing, and the conversion is done as needed by the capabilities of the target system. The method is faster than ETL because data loading is done only once, and the data size does not affect the speed of the process. It is usually less time-consuming and it preserves all the data and its integrity, so it allows changes to historical data.


Pros of ELT

Let’s delve into the essential advantages of ELT:

  • Much greater scalability since it doesn’t require provisioning hardware;
  • Shorter time to insight and action, as it enables faster and easier processing of data;
  • Significantly cheaper as it abstracts all the system maintenance and administration;
  • Improved stability and reliability of the system, with less downtime;
  • Stores raw data, making it easier to analyze each phase of the process, as well as speeding up problem discovery;
  • Unprocessed data is easily accessible, which simplifies the development process

Disadvantages of ELT

Such a progressive approach also has its disadvantages. So, let’s take a closer look at them:

  • The need for data lakes;
  • Loss of control for runtime tracking and analysis

The method doesn’t require additional hardware costs, but sometimes it doesn’t offer sufficient support for existing source systems. Since the data is entered into the repository in raw form, leakage, and tampering pose risks. Moreover, if storage is used overseas, it may violate international standards.

Nevertheless, ELT is quickly becoming the industry norm, and many businesses are turning to data engineering consultants for guidance on implementing it. The method is suitable when the data must be converted to match the mode of the target database. Cloud repositories can accept raw data, so conversion does not require pre-loading. The data can be downloaded and converted at the same time.

It’s worth using if you have the right experts, a reliable cloud database, and no GDPR or regulatory requirements. In addition, storing raw data creates a rich historical repository for business intelligence when complete data sets are required.

In other words, ELT is more flexible, efficient, and scalable, especially for processing large volumes and developing powerful business intelligence. And that is why it is gradually replacing ETL. Let’s move to ETL and ELT comparison.

ETL vs ELT: What to Consider When Making Your Choice?

When you need to move large amounts of data, ETL is the perfect solution. It is because you don’t need to do it in real-time, so you can extract the data over some time and process it later. It is done when you need to integrate marketing data into a more extensive system, for example. You must collect data on how they behave to improve the customer experience or create incentives for customers. This data type should be processed differently because you need to explore other variables for more accurate analysis. Some tools can process the data in real time and customize the interaction with the user according to the input.  

Cloud solutions are cost-effective because they come with existing infrastructure and high-capacity storage. They are also secure solutions, so it is unlikely that someone will steal your data if it is stored in the cloud. It’s also easy to manage data flows and integrate with cloud solutions, so they’re famous.

Open-source solutions are the way to go if you’re looking for minimal cost. Remember that they will be more user-friendly, and you may need your in-house developer. 

One problem with off-the-shelf solutions is their need to meet specific business goals. They are designed to accommodate a large user base and allow you to control marketing or sales campaigns. However, they could be better suited for sophisticated analytics. In other words, you may need features or filters that are specifically designed for your business. 

When developing, two issues need to be addressed ─ speed and scalability. If you need speed, you should focus on a low-latency tool. For an in-depth and comprehensive analysis, the data scientist may want a device with more input capabilities.


The main difference between ETL and ELT is that ETL methodology has been in use for a long time, so there is a sufficient number of tools, developments, and experienced professionals. ELT is a relatively new practice, so there needs to be more expertise and appropriate professionals to implement it. 
But you don’t need to look for them or wait for them to appear. Our experts will gladly help you to create an effective ELT data pipeline. Book a free consultation today and improve your company with advanced business intelligence services tomorrow!

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