Most companies rely on big data to make crucial internal and external decisions. These decisions are the backbone of business intelligence, innovation and growth.
But how do businesses collate and decipher the data from their leads, customers, employees, SaaS apps and other channels? They use data integration.
ELT or extract, load, and transform is a data integration process where collected data is extracted, sent to a data warehouse, and then transformed into data that is actually useful for analysts.
In this article, we explain the ELT process, list the differences between two standard data integration processes — ELT and ETL, and the benefits of fully-automated ELT.
What is ELT?
Extract, load, transform (ELT) is a data integration process that moves data directly from source to destination for analysts to transform as needed.
ELT streamlines data pipeline creation and maintenance. Moreover, ELT pipelines can be built and managed using cloud-based software like Fivetran.
ELT is a newer data integration method than ETL (Extract, Transform, Load). The latter was invented in the 1970s and catered to the technical limitations of that time.
Since storage and bandwidth were finite and restricted in the 70s, ETL focuses on minimizing the usage of these resources. In recent decades, these restrictions are no longer present, and ELT can help organizations collect, refine and accurately analyze large-scale data with much less menial effort.
How ELT works
The ETL workflow has three main steps:
- Extract: Raw data is copied or exported from sources like applications, web pages, spreadsheets, SQL or NoSQL databases and other data sources. This data can be structured or unstructured. Once exported, the data is moved to a staging area.
- Load: The exported data is transferred from the staging area to centralized storage like a data lake or cloud data warehouse. Since ELT is faster than ETL and loads data before transformation, companies can use it for fast processing during work or peak customer hours.
- Transform: The data in storage is then transformed using schema. Analysts use transformations to decipher raw data and gain valuable insights. This process includes all operations that alter or create values, such as cleaning, calculating, translating, authenticating, encrypting or formatting data. These operations can be complicated and usually obscure or alter the raw values from the source.
The ELT process aims to convert raw data into data sets of different formats that data analysts can use to derive insights that drive business decisions.
For example, unstructured data from a marketing campaign can be converted into definite metrics that help analysts understand whether the campaign is on track and contributes to larger organizational goals.
ETL vs. ELT
ETL dates from the 1970s, when storage, computation and bandwidth were scarce. To preserve these resources, ELT transforms data before and limits the volume of stored data.
Generations of data engineers have developed and used ETL tools. So, it remains the norm in data integration, and the two terms (ETL and data integration) are often used interchangeably.
Despite ETL’s success, the critical weakness of traditional ETL tools, such as Pentaho, Informatica and SSIS, is that data is transformed before it is loaded.
This process obscures the raw, original values and means that engineers must rebuild the entire pipeline whenever the source changes or analysts need a new data model.
Consequently, stoppages and extra engineering resources are required whenever your data source adds a new field or your company needs a new metric.
Companies must add additional data sources, collect a larger volume of data and track new metrics to propel business growth. But the brittleness of ETL turns the opportunities offered by new data into a heavy technical burden.
By contrast, the ELT process extracts and loads raw data with no assumptions about how the data will be used.
ELT is a product of the modern cloud and the plummeting storage, computation and bandwidth costs in recent decades.
Without constraints on these critical data integration elements, there is no reason to follow an integration architecture that focuses heavily on preserving those resources.
The promise of fully automated ELT
ELT has seven key advantages that make it ideal for companies looking to improve their data integration and analysis processes.
1. Faster data availability
ELT separates the loading and transformation processes, and data is immediately loaded to a cloud-based data warehouse or data lake.
So analysts get faster access to the information they need. Unlike in ETL, they don’t have to wait for transformation to be completed before they can explore the data.
This is a significant advantage when large amounts of data are generated in a short time, such as streaming or stock market data. In these scenarios, delays such as those caused by the ETL method, where data is transformed before being sent to storage, have big consequences.
ELT is ideal when analysts and end users need to manipulate and gain insights in near real-time, by utilizing change data capture.
Providers of purpose-built, fully managed ELT have done comprehensive research to understand every idiosyncrasy of the data sources they connect to. They also regularly pressure-test their software against a variety of use cases.
The design considerations involved in developing a robust ELT solution, along with the cost of time, money, labor and morale, are substantial.
This extensive research has led to the creation of comprehensive cloud-based data pipeline management platforms like Fivetran. This software is future-proof and secure and supports continuous updates.
Analysts and companies using cloud software have a dynamic and easily customizable platform that enables them to gather, combine and examine data faster. These tools also involve outsourced compliance and security, relieving yet another technical burden.
3. Lower costs
ELT uses existing warehouse or cloud resources for data transformation and does not require an interim data system or external servers. This saves companies thousands of dollars that would have otherwise been spent on ordering, installing and configuring new equipment.
Fully-managed ELT solutions are also affordable, as there’s no need to transfer data out of the cloud for analysis. The more steps within your integration pipeline, the more money you spend on resources.
ELT is also a faster process than ETL. It allows for easier customization of transformation models, so organizations don’t need to hire a team of engineers to regularly modify their workflow.
4. Regular updates
ELT platforms can harness the latest technologies to consistently improve data collection, storage, compliance and security.
With a fully-managed, outsourced and automated solution, the provider’s Service-Level Agreements (SLAs) can dictate how to continuously adapt your data pipelines to changes at the source and provide your analysts with new data.
All your data from various sources are centralized in one place and continuously updated without human intervention.
This persistent, up-to-date data repository gives your teams the bandwidth to tackle new questions as they emerge, accelerate project timelines and create new insights from existing datasets.
Fully managed ELT solutions also update incrementally, resulting in faster turnaround times and a lower impact on production source systems.
A company that allows an outside vendor to build and maintain a data pipeline automates pipeline maintenance tasks including, but not limited to:
- Modifying data extraction scripts to accommodate frequent API changes
- Normalizing data source extracts into a queryable schema
- Keeping that schema updated with new data or new structures as they are created in the data source
Choosing a data integration tool with fully managed ELT frees your team from designing, building and maintaining a complex piece of software. This reduces the time spent on engineering and enables analysts to reliably and rapidly access their data.
Since ELT makes no assumptions about how your teams will use the data, you can create standardized data models for all users. Standardized schemas mean the ELT pipeline can be outsourced and automated.
Outsourcing your data integration allows you to rapidly narrow time-to-insight and saves you valuable engineering resources for the initial build and ongoing maintenance.
Automated ELT means automated data integration at any level of technical ability, empowering your analysts to work with minimal delay.
Moving transformation to the end of the process turns transformation from an engineering-centric activity baked into data pipelines to an analyst-centric activity performed in a data warehouse.
This means that changes to downstream business and analytics requirements, i.e., data models for dashboards and reporting, don’t affect the pipeline.
It also facilitates custom data pipelines that can support varying businesses and objectives. For example, when analysts require a new metric to be measured, they don’t need to modify the entire pipeline painstakingly.
Instead, they can just edit the transformation models, which only affects the last stage of the integration process.
As business needs change, companies using a cloud-based infrastructure can quickly scale up or down as needed. This is mainly because there is no need to build on-premise infrastructure.
Scaling when you have a fully-managed ELT solution like Fivetran consists of upgrading your subscription models to get more connectors and analytics-ready schemas.
If business objectives have changed and you need to scale down, you can opt for a more suitable subscription plan.
This is a simpler, faster and cheaper process than altering ETL pipelines and provisioning physical servers and other hardware.
Advanced ELT platforms with a good security SLA also ensure data security through encryption and anonymization, eliminating the need to home brew security protocols.
How to get started with fully managed ELT
With a dynamic data pipeline management tool, all it takes to set up integrations is to provide or obtain a user account(s) with the appropriate access levels. This is typically a one-step process.
From there, automated data connectors will do a full historical load of all the data available in the data source and will keep that dataset updated as changes are read from the source.
You can set up integrations in minutes and never have to check back on the pipeline as your data source evolves.
To learn more, explore this FAQ about how to take advantage of a fully managed ELT solution.