A Big Data stack has several layers that take your data from source to analytics tools. Extract, Transform, Load tools integrate data from sources into a data warehouse or lake. Business intelligence solutions use centralized data for their analytic needs. An ETL tool such as Integrate.io offers a user-friendly experience for ingesting data from many sources, transforming it as needed, and sending it to the next layer. Here’s how you can implement this handy tool in your organization.

Table of Contents

  1. ETL Process Overview
  2. ETL vs. ELT
  3. Setting Up Data Pipelines in Integrate.io
  4. Get the Most Out of Your Big Data

ETL Process Overview

Integrate.io processes raw data in a three-stage process, extracting it from the source, transforming it as needed for the analysis tools, and loading it into the data lake, warehouse, or Salesforce final destination.


The extraction process takes raw data from one or more data store sources and shifts it into a staging area. Once the data reaches this location, it can be better organized and cleaned before it is integrated into your warehouse or lake.


Your data stores may include many formats. However, before you can effectively use this data with a BI tool, you need to transform it into the correct structure. Compliance measures must also be considered in this process.

Examples of transformation types available with Integrate.io include:

  • Cleaning: Data goes through a cleaning process to remove irrelevant data, imprecise data, and other low-quality issues. 
  • Deduplication: Improve analytic speed and performance by removing duplicated data in your data warehouse or lake.
  • Aggregation: Group datasets by specific fields to look at the information in aggregate. 
  • Limiting: You can control how many records are in the dataset’s output. You’re also able to limit data on a per partition or per group basis.
  • Cross joining: You combine two data inputs with one another.
  • Sorting: Data can be pre-sorted into descending and ascending order.
  • Filtering: Filter data input to remove extraneous data easily and extract only what you need.
  • SELECT: Use this transformation component to perform encryption, hashing, and data masking. Unlike the other transformations on this list, which are table-level, SELECT is used on a field or column basis.
  • WINDOW: Apply window functions to your incoming data, such as ranking the data or creating running totals. 

For more information on our transformations, refer to the Integrate.io Knowledge Base.


Your data is transformed based on your requirements and it’s ready to go to your data lake or warehouse. You can send the prepared data in batches or wait until a full dataset is ready to move with incremental loading and bulk loading.


When you explore your data stack options, you may see ELT mentioned alongside ETL solutions. Extract, Load, Transform tools switch the order of the transform and load steps, making a significant difference in how you use it.

What Are The Benefits of ELT?

ELT goes beyond simply working with structured data. It also supports raw, unstructured, and semi-structured data, which provides greater flexibility than ETL. While ETL can ingest unstructured data, it can’t move it through its system without transformation. ELT information, on the other hand, gets loaded into the data warehouse or lake immediately, as it does not go through the intermediatory steps of staging, cleansing, and transformation before loading.

The data is then transformed on an ad-hoc basis once it’s loaded into the data store, so you only process data as it’s needed for reports, metrics, visualizations, and other operations. If you want to take full advantage of ELT capabilities, you need to use a data lake over a warehouse. Your raw data loads directly into the lake, whereas a warehouse relies on structured data.

Since you transform data on-demand, you don’t go through the process of setting up data pipelines before ingestion happens. You can even store an amount of data that you don’t currently have the tools to structure for future use.

How ETL Stands Out Versus ELT

ETL works as an ongoing integration process with a clear and well-defined workflow. These solutions extract structured and unstructured data from their data sources and change them into a relational format for the data warehouses. If the data is not in this format, it’s unable to move to data warehouses as they have SQL-based data structures.

The ETL process has several advantages compared to ELT. The first is that data analysis happens faster, as the ingested data is pre-structured to work with these systems. It’s a more mature technology with twenty years of history, leading to greater stability, access to data engineers and data scientists, proven tools, and efficiency.

If your organization falls under strict data protection regulations, you need a way to identify affected data and transform it to maintain compliance. Common operations for maintaining data privacy of this sensitive information include filtering it out entirely, masking it, or using field-level encryption. You have better security over these records when the transformation takes place before the load.

Which Should You Use?

Integrate.io supports both ETL and ELT processes on our platform. ETL works best when you have smaller structured data sets that require complex transformations and are going into a data warehouse. ELT supports data of all types that need to be quickly ingested into a data lake. As your data management needs change over time, periodically assess whether you need to shift your strategy.

Of course, you don't necessarily need to choose between them. ETLT is a hybrid option that increases your data ingestion speed while still meeting data quality and compliance standards. In ETLT, the raw data gets extracted to a staging area, where it undergoes light-duty transformation such as format conversion, removal of sensitive data, and cleansing. This data loads into the data warehouse where it can undergo additional, more complex transformations processed by the data warehouse.

Setting Up Data Pipelines in Integrate.io

Integrate.io is a cloud-based ETL tool that simplifies the process of creating data pipelines. Its out-of-the-box functionality supports many popular data sources. You don’t need to hand-code data pipelines, as both no and low-code functionality is available.

Integrate.io uses a graphical interface to walk you through each step of setting up a dataflow or workflow package. You can start completely from scratch or use one of the pre-made templates available on our platform. These templates cover many common use cases and reduce the time it takes to get started.  

Below are the steps for creating a new pipeline in Integrate.io. For more guidance, refer to this demo video that outlines each step of creating a pipeline using MongoDB as an example:

  1. Navigate to Packages on the Integrate.io dashboard.
  2. Click “New package.”
  3. For a data pipeline, choose the “Dataflow” package option.
  4. You can select a pre-defined template at this point with the “Templates” dropdown menu.
  5. If you’re creating a from-scratch dataflow, click “Add Component” and select from the available data sources.
  6. Configure each component you add by clicking in the rectangle. You set up the connection by clicking “+New” in the menu.
  7. You may need to make changes to your data repositories prior to Integrate.io accessing them. For example, your firewall rules may need reconfiguring, so Integrate.io can connect.
  8. For sources that are not natively supported by Integrate.io, use the REST API component if they support this API.
  9. Choose your schema and define the source properties for that connection. Integrate.io detects the schema and also provides a small data preview.
  10. Now that your data source is set up, you can configure the desired transformations by hovering over the component and clicking the “+” sign.
  11. Use the same menu to set the destination for this dataflow. Add the “Destination” component and fill in the properties of your endpoint.
  12. Click to map your input schema to the appropriate table columns in your endpoint.
  13. You can use the convenient “Auto-fill” option while mapping your schema to automate this process.
  14. Click “Save.”
  15. The next step is to validate your package to check for any errors during the configuration process. Click “Save and validate.”
  16. You’re ready to run your package after validation. You can use “Save & Run job” on the free sandbox or in one of your production clusters. Select the appropriate cluster and click “Run job” to execute your first package. You’ll receive ongoing updates from the platform while it progresses, along with any errors that may have occurred. Integrate.io also shows you a small sample of that job’s data output.

Get the Most Out of Your Big Data

Integrate.io’s ETL solution is a powerful and cost-effective fit for your data stack’s ingestion layer, and it avoids confusing jargon and overly complex configuration. Improve your business agility by scheduling an intro call and obtaining your risk-free 14-day trial of Integrate.io today.