ELT: Transforming Data Pipelines for the AI Era
ELT: Transforming Data Pipelines for the AI Era
Unlock the power of modern data pipelines with ELT
Unlock the power of modern data pipelines with ELT
Table of Contents
For decades, traditional ETL (Extract, Transform, Load) processes have been the backbone of data integration, meticulously crafting structured data for analysis. But today's organizations are swimming in an ocean of data that's growing exponentially, both in volume and variety.
This data deluge is pushing the limits of conventional approaches. As data cloud platforms and AI reshapes the data landscape, a more flexible and scalable methodology is stepping into the spotlight.
In this guide, we'll dive into ELT (Extract, Load, Transform)—a modern approach that's revolutionizing how organizations handle their data pipelines, taking full advantage of cloud-native architectures while enabling more agile and efficient data workflows.
What is ELT (Extract, Load, Transform)?
ELT (Extract, Load, Transform) is a data integration process where data is extracted from various sources, loaded into a target cloud data platform, such as a data warehouse or data lakehouse in its raw form, and then transformed within that target system.
Unlike the traditional ETL processes we've been using since the 1970s, ELT switches up the order and location of data transformation, offering some serious advantages for today's data architectures.
Here's how the ELT process breaks down:
- Extract: Pull data from various sources, keeping its original format and structure intact.
- Load: Dump that raw data straight into your target data warehouse or data lake without any preprocessing.
- Transform: Perform transformations within the destination system, tapping into the processing power of modern cloud platforms.
How ELT differs from traditional ETL
The big difference from ETL is when and where the transformations happen. In ETL, data gets transformed in a separate processing server before loading. With ELT, you load raw data directly into your target system and perform transformations using the data warehouse's own computing muscle.
Shifting transformations to the data warehouse meshes better with cloud-native architectures and brings several perks.
Benefits of ELT
So, what are the benefits of ELT in modern data environments?
- Immediate data availability
With ELT, data is loaded into the target system immediately after extraction, without waiting for prior transformations. This means raw data becomes available for querying and analysis as soon as it's in the data warehouse.
Analysts and data scientists can access up-to-date information faster, enabling more timely insights and decision-making.
- Enhanced flexibility
Because raw data is stored in the data warehouse, you can redefine or adjust your data transformations at any time without the need to re-extract or reload data from sources. This flexibility allows you to adapt quickly to changing business requirements, data models, or analytic needs.
You can iterate on your transformations more rapidly, improving the agility of your data processes.
- Better performance
Modern cloud data warehouses like Databricks are designed for high-performance data processing. By performing transformations within these systems, you can take advantage of their massively parallel processing (MPP) architectures and optimized query engines.
This leads to faster transformation times and the ability to handle complex transformation logic efficiently.
- Improved scalability
ELT allows you to scale your data processing as your data volumes grow. Since you're leveraging the elastic compute resources of cloud data warehouses, you can scale up or down based on demand without significant infrastructure changes.
This is especially beneficial for organizations dealing with big data, as it provides the ability to process and transform large datasets without performance degradation.
- Cost-effectiveness
By eliminating the need for dedicated ETL servers for data transformations, you reduce infrastructure costs. With ELT, the transformations happen within the data warehouse, making better use of the resources you're already paying for.
Additionally, cloud-based data warehouses often offer cost-effective pricing models, such as pay-as-you-go, which can further optimize costs.
When to use ELT
ELT shines in modern data environments where flexibility, scalability, and cloud-native capabilities are key. Here are some scenarios where ELT is the way to go:
Cloud data warehouse implementations
If you're working with cloud data warehouses like Databricks, or implementing cloud data engineering with Spark, ELT is a natural choice. These platforms offer massive parallel processing capabilities that make in-warehouse transformations super efficient and represent leading cloud data engineering solutions.
You can tap into their computational power to transform data after loading, all while taking advantage of cloud scalability and cost optimization.
Data for AI and real-time analytics
Dealing with large volumes of unstructured or semi-structured data to feed into AI platforms? ELT can be more efficient than traditional ETL.
When organizations face high-velocity data streams that demand near-real-time analytics, ELT emerges as a game-changing solution. By loading raw data first and transforming it later, you can rapidly ingest and process diverse data types—from social media feeds to sensor data— without being constrained by predefined transformation rules.
This flexibility becomes particularly valuable in data lakehouses, where schema-on-read approaches allow for more dynamic data handling.
Perhaps most importantly, ELT shines in environments with unpredictable or seasonal workloads. Whether you're dealing with holiday season retail surges or variable streams of user-generated content, ELT's elastic scaling capabilities ensure your data pipeline can expand and contract seamlessly with demand.
This adaptability, combined with the ability to apply different transformation rules to the same raw data, makes ELT an increasingly attractive choice for modern data architectures.
In shirt, you'll benefit from ELT when you're:
- Processing high-velocity data streams that need near-real-time analytics.
- Working with diverse data types that require flexible transformation rules.
- Managing data lakes or lakehouses where schema-on-read approaches make sense.
- Handling seasonal or variable workloads that need elastic scaling.
Business-driven scenarios
Consider ELT when your organization has reached a pivotal point in its data strategy, particularly when the need for rapid insights becomes critical for decision-making. The ability to quickly access and analyze data can transform how your organization responds to market changes and customer needs, making faster data-driven decisions possible at every level.
Business users across departments can access and analyze data according to their specific needs, without waiting for IT intervention or complex data preprocessing. The potential democratization of data access, combined with flexible transformation capabilities, means teams can iterate and experiment with different analytical approaches without having to restart the entire data processing pipeline.
One of ELT's most compelling advantages lies in its cost efficiency, particularly in cloud environments. By loading data first and transforming it only when needed, organizations can optimize their resource usage and avoid unnecessary processing costs.
This approach allows for more precise control over cloud computing resources, ensuring you're only paying for the transformations that deliver actual business value, while maintaining the flexibility to modify these transformations as your needs evolve.
In short, ELT is great when your organization needs:
- Faster insights for data-driven decisions.
- Self-service analytics capabilities for business users.
- Cost optimization through efficient use of cloud resources.

The ETL modernization imperative - ELT's impact
Legacy ETL processes can be costly and slow down access to data—as this major healthcare network discovered. Switching from ETL to ELT is more than just rearranging steps—it's a fundamental modernization of data integration that aligns with cloud-native architectures and today's data needs.
This transformation is essential for organizations aiming to stay ahead in the rapidly evolving ETL modernization context.
Here are seven key benefits driving the push to modernize from ETL to ELT:
1. Improved efficiency
ELT leverages the computational power of modern cloud data warehouses, enabling more efficient data processing. By pushing transformations to the data warehouse, you're tapping into scalable resources that can handle intensive workloads.
This shift reduces the time it takes to process and prepare data for analysis, leading to quicker insights and decision-making.
Modern tools like Prophecy's Data Transformation Copilot aim to speed up data pipeline creation. These tools focus on reducing the time and effort required to build and deploy data workflows.
2. Increased flexibility
ELT offers greater adaptability to changing data schemas and formats. Since raw data is loaded into the warehouse before transformation, you can adjust your transformation logic as needed without reloading data.
This flexibility is crucial in today's fast-paced business environment, where data requirements can change rapidly.
Platforms like Prophecy's low-code designer offer a visual interface that facilitates adjustments to data pipelines and supports diverse programming models for data transformations.
3. Cloud optimization
ELT is designed for modern cloud data platforms. By performing transformations within the cloud data warehouse, you optimize resource utilization and benefit from the scalability and cost efficiencies of cloud infrastructure, leveraging the latest data engineering advancements.
This approach aligns perfectly with organizations migrating to or building on cloud-native architectures.
Prophecy is designed to enhance data workflows by integrating with modern cloud data platforms, focusing on performance and scalability in cloud environments.
4. Scalability
ELT is better suited for larger datasets and real-time processing. By leveraging the scalable compute resources of cloud data warehouses, you can efficiently process large volumes of data and handle peak loads without performance hits.
This scalability is essential for organizations dealing with big data and real-time analytics.
5. Foundation for data democratization
ELT can make more data available in an organization by lowering the barrier to data transformation in data cloud platforms. With the right tools professionals with a business background can take part in developing ELT data pipelines, bringing more team members into the data preparation and manipulation process, and further scaling data transformation.
For instance, Prophecy's visual interface and low-code data transformation tools aim to simplify data pipeline development for data cloud platforms like Databricks, making it more accessible to a broader range of data professionals.
Choose tools that are designed to enable users to build and modify data transformations, encouraging collaboration in the data environment.
6. Cost-effectiveness
ELT is more cost-effective for large-scale data operations. By eliminating the need for separate ETL servers and leveraging the pay-as-you-go model of cloud data warehouses, you can reduce infrastructure and operational costs.
Tools like Prophecy aim to improve cost-effectiveness by automating ETL conversion and standardizing operations, which may lead to reduced development and maintenance expenses.
7. Accelerated development
ELT enables rapid development and deployment practices. With transformations happening within the data warehouse, you can iterate faster and deploy changes more quickly. This agility is vital in a competitive market where time-to-insight can be a differentiator.
Prophecy offers features designed to enhance the development process, including tools that facilitate team collaboration and options for customizing data operations to meet specific business needs.
These capabilities assist organizations in advancing their data projects to meet business needs efficiently.
Embracing data modernization with ELT
The move to ELT (Extract, Load, Transform) is a significant evolution in how organizations handle data. By leveraging cloud-native architectures and modern data warehouses, you can unlock new levels of flexibility, scalability, and efficiency in your data pipelines.
To facilitate this transition, adopting tools that simplify ELT processes is crucial. Prophecy provides a platform designed to enhance data engineering processes through visual development, AI, and low-code interfaces.
Explore data modernization with ELT and discover how Prophecy can enhance your data operations. To see Prophecy in action, request a demo.
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