The Complete Guide To ELT (Extract, Load, Transform) for Data Workflows

Organizations today face a critical challenge: extracting meaningful insights from explosive data growth while their traditional ETL processes struggle to keep pace. As data volumes multiply and sources diversify, the conventional approach of transforming data before loading it creates bottlenecks, delays insights, and limits business agility.

This widening gap between data collection and actionable intelligence threatens competitive advantage in markets where speed matters.

ELT (Extract, Load, Transform) represents a fundamental shift that aligns with modern cloud platforms and evolving business needs. By loading raw data immediately and transforming it within the target system, this approach isn't just rearranging technical steps—it's reimagining how organizations derive value from data.

In this article, we'll explore how ELT transforms data integration in the cloud era, its key benefits, and practical guidance for implementation across various industries.

What is ELT (Extract, Load, Transform)?

ELT (Extract, Load, Transform) is a data processing methodology that reverses the traditional sequence of data integration steps. In this approach, data is first extracted from source systems, then loaded directly into a target system (such as a data warehouse or data lake), and finally transformed within that target system.

This method leverages the computational power of modern cloud data platforms to perform transformations after data has been loaded, rather than before.

The evolution from ETL to ELT

The shift from ETL to ELT didn't happen overnight. Traditional ETL made sense when on-premise data warehouses had limited storage and processing power, so transforming and filtering data before loading was necessary.

What changed? Data exploded in volume and variety, making pre-load transformations increasingly cumbersome. In a webinar on legacy ETL modernization, Soham Bhatt, Lead Solutions Architect at Databricks,  explains how "Data is now everywhere. Images, video, audio, semi-structured text... This is now what the enterprise space is always thinking about, making ELT's flexibility increasingly important."

Organizations needed faster access to raw data and more adaptable transformation options. This need coincided with the rise of modern cloud data platforms like Databricks, which changed everything with virtually unlimited storage and powerful computational capabilities, making it practical to load first and transform later.

Businesses demanding real-time analytics pushed ELT adoption further, as they couldn't wait for lengthy transformation processes to finish before accessing insights.

How ELT works

The ELT process breaks down into three distinct stages:

1. Extraction pulls data from various source systems—databases, applications, APIs, and files. Modern methods include real-time streaming, batch processing, and change data capture (CDC), optimizing data ingestion to let you collect both structured and unstructured data efficiently and overcome data silos.
2. Loading moves the raw data directly into your target data platform with minimal modification. This stage uses high-speed ingestion tools and cloud storage to quickly make data available for analysis, enhancing cloud efficiency.
3. Transformation happens within the target system, using its processing power to clean, enrich, aggregate, and model the data. You can perform transformations on-demand with SQL, Python, or specialized tools that work directly within your data warehouse or lake.

Unlike traditional ETL (Extract, Transform, Load), where data transforms in a separate staging area before loading, ELT brings raw data into the target system right away. This gives you much more flexibility in how and when transformations happen—a fundamental shift that creates numerous advantages for data teams handling large, diverse datasets, leading to greater cloud efficiency.

This sequence lets you work with complete datasets, transforming data only when needed and refining your approach without reloading from the source.

The business benefits of ELT in the cloud era

ELT offers several compelling advantages in today's cloud-centric data environments:

• Immediate data availability: Raw data becomes accessible as soon as it's loaded, giving you faster insights and quicker decisions compared to traditional ETL.
• Enhanced flexibility: You can modify transformations without reloading data from sources, helping teams adapt to changing business requirements faster.
• Superior scalability: Cloud data platforms handle massive data volumes and parallel processing, making ELT significantly more scalable than traditional approaches.
• Cost optimization: By using the computational power of modern data warehouses, you can eliminate separate transformation servers and optimize resource usage, improving cloud efficiency.
• Preservation of raw data: Original data stays available in the target system, enabling different transformations for various use cases and maintaining a complete historical record.
• Simplified maintenance: Fewer components in the data pipeline mean less maintenance and fewer points of failure than complex ETL systems.
• Support for agile analytics: Data scientists and analysts can work directly with raw data, creating transformations as needed without waiting for IT to modify processes.

When to use ELT vs. ETL

ELT isn't always the right choice. Consider ELT when:

• You're using a cloud data platform with robust processing capabilities. Modern warehouses are built to handle ELT workloads efficiently, unlocking ELT for cloud efficiency.
• Your data volumes are large and growing rapidly. ELT shines when processing massive datasets that would be awkward to transform before loading.
• You need flexibility to transform data differently for various use cases. ELT keeps raw data intact, allowing multiple transformation paths without re-extraction.
• Your team needs real-time or near-real-time analytics. Accessing data immediately after loading speeds up insights.
• You're working with diverse data types, including unstructured or semi-structured data that might need different transformation approaches.

Traditional ETL might still work better when using legacy on-premise systems with limited storage or when strict privacy regulations require certain transformations (like anonymization) before data enters the main repository.

For businesses handling extremely sensitive information in compliance-heavy industries, ETL's ability to cleanse data before warehouse entry offers additional security benefits.

The choice depends on your specific infrastructure, data characteristics, and business requirements. Many organizations actually maintain both ETL and ELT processes for different data pipelines.

Modern ELT tools and technologies

The technology ecosystem supporting modern ELT consists of integrated components that create efficient, scalable data pipelines. From storage to transformation to orchestration, each element plays a crucial role in delivering analytics-ready data while maximizing cloud efficiency through cloud-native architecture benefits.

Cloud data platforms for ELT

Cloud data platforms form the foundation of modern ELT processes, providing the computational muscle and storage needed for large-scale transformations. These platforms use massively parallel processing (MPP) architectures that distribute workloads across multiple nodes, transforming vast datasets that would crush traditional systems.

Columnar storage makes these platforms ideal for ELT workloads. Rather than storing data row by row, columnar storage organizes information by columns, dramatically improving analytical query performance by reading only the required columns during transformations.

Platforms like Databricks combine SQL, Python, and Spark processing with optimized query engines for efficient in-platform transformations. This integrated approach eliminates data movement between systems, reducing latency and maximizing throughput for complex data pipelines at virtually any scale.

Data integration and transformation tools

Modern ELT workflows use various approaches for data integration and transformation. When selecting data integration tools, organizations should prioritize efficiency and development speed—a critical consideration as 47% of data leaders identify excessive time building new pipelines as their top data processing challenge.

Many consider code-based methods using SQL, Python, and Spark, which give developers precise control over transformation logic, supporting complex business rules and data cleaning operations that require customization.

Low-code data engineering tools have also democratized ELT processes, letting analytics teams build transformation pipelines without extensive programming knowledge. For instance, they can enhance productivity on Spark by simplifying complex transformations. Tools like dbt (data build tool) allow analysts to define transformations in SQL while handling version control, testing, and documentation automatically.

These modern tools are designed to tap into cloud platform capabilities, with built-in connectors for cloud data sources, parallelized operations, and metadata-driven approaches. This means organizations can implement sophisticated ELT processes that scale elastically while remaining accessible to more users beyond specialized data engineers, enhancing cloud efficiency.

Orchestration and monitoring solutions

Orchestration tools serve as the central nervous system of ELT workflows, scheduling jobs, managing dependencies, and ensuring transformations execute in the proper sequence by following pipeline best practices. Platforms like Apache Airflow let teams define monitoring workflows as code, enabling version control and collaboration around pipeline definitions.

Reliable, repeatable execution is essential for production ELT pipelines, which orchestration solutions address through features like automatic retries, error handling, and notifications. These capabilities keep data pipelines operational even when facing intermittent failures or unexpected conditions.

Monitoring capabilities integrated with orchestration tools provide visibility into pipeline health, performance bottlenecks, and data quality issues. Advanced monitoring solutions track data lineage, helping teams understand how information flows through transformations and identify root causes of problems. This observability is crucial for maintaining high-quality data products while optimizing resources across complex ELT environments, contributing to cloud efficiency.

ELT vs. other data integration methods

ELT represents just one methodology in the diverse landscape of data integration. Understanding how it compares to other approaches helps organizations choose the right method for their specific requirements.

Different integration strategies offer unique advantages depending on data volumes, latency requirements, and analytical needs. Many organizations implement complementary approaches within their broader data strategy rather than picking just one.

ELT vs. data virtualization

Data virtualization creates a virtual abstraction layer on top of source systems without physically moving or copying data. Instead of loading data into a central warehouse, virtualization provides real-time access to data where it lives.

The performance differences are significant. ELT excels at handling high-volume analytical workloads by using the computational power of cloud data warehouses. Data virtualization shines in scenarios requiring real-time insights where data freshness matters most.

These analytical capabilities reflect fundamental differences. ELT provides robust historical analysis and supports complex transformations on large datasets, while data virtualization offers immediate access to the latest data across disparate sources.

Organizations often use both—ELT for data-intensive analytics and virtualization for real-time operational intelligence that doesn't require moving large data volumes.

ELT vs. API-based integration

API-based integration focuses on real-time data exchange between applications rather than batch data processing typical of ELT. APIs act as bridges enabling different systems to communicate and share information on demand through standardized interfaces.

Storage requirements mark a key distinction. ELT pipelines typically store complete datasets in a warehouse or lake, while API integration often involves minimal temporary storage, exchanging only necessary data between systems as needed.

These methods serve complementary purposes. ELT works best for comprehensive analytics and reporting on historical data, while API integration excels in operational scenarios requiring immediate data access.

ELT vs. event-driven integration

Event-driven integration processes data in response to specific events or triggers, rather than on a schedule like typical ELT. This architecture responds to business events as they occur, such as a customer placing an order or a sensor reading exceeding a threshold.

The timing difference is substantial: ELT typically runs on predetermined schedules, processing data in batches, while event-driven systems process data immediately as events happen. This creates different storage patterns—ELT tends to store complete datasets optimized for analytical queries, while event-driven systems often use message queues and event streams.

Organizations frequently implement hybrid approaches. ELT provides comprehensive historical analytics capabilities, while event-driven integration enables real-time operational responsiveness.

ELT use cases

ELT processes have transformed how organizations manage data operations across diverse industries. The shift from traditional ETL to modern ELT has facilitated more agile analytics, faster decision-making, and greater operational efficiency.

Let's see how different industries are using ELT to address specific challenges and deliver tangible business value.

ELT in financial services

Financial services organizations face immense challenges managing transaction data while meeting strict regulatory requirements. ELT addresses these challenges by letting firms load raw data directly into cloud data warehouses before applying transformations, optimizing data for cloud efficiency.

Amgen, a global biopharmaceutical company with complex financial operations, struggled with traditional ETL processes that couldn't scale to meet growing data demands. Their financial data integration was bottlenecked by preprocessing requirements and limited processing windows.

After implementing a cloud-based ELT architecture, Amgen could ingest raw financial data immediately and apply transformations within their data warehouse. This eliminated preprocessing bottlenecks and allowed financial analysts to access data much faster.

The implementation significantly reduced their data processing time by using the cloud's parallel processing capabilities. What previously took hours now completes in minutes, enabling near real-time financial reporting and analytics that support more agile business decisions.

ELT in healthcare insurance

Healthcare insurance organizations manage diverse data types, including patient records, provider information, and claims data. The ability to integrate and analyze this data quickly is crucial for operational efficiency and customer service.

CZ, a leading healthcare insurer, needed to modernize their data platform to better support analytics across their complex data ecosystem. Their traditional ETL processes couldn't handle the variety of data formats or provide the flexibility needed for evolving analytics requirements.

By implementing an ELT approach, CZ created a unified data platform where raw healthcare data could be loaded directly into their cloud data warehouse. This preserved all data attributes while applying specific transformations based on the analytical needs of different departments.

The results were impressive—CZ reduced their data integration time by 60% while improving data quality and consistency. Their analytics teams gained self-service capabilities, enabling them to explore data and create new insights without waiting for IT to create specialized data views.

ELT in investment management

Investment management firms require rapid integration of market data, portfolio information, and client records to drive investment decisions and client reporting. Time-to-insight is critical in this fast-moving industry.

Waterfall assets, a growing investment management firm, struggled with fragmented data systems that made it difficult to create comprehensive views of their investment portfolios. Their legacy ETL processes couldn't keep pace with market data volumes or provide the flexibility needed for diverse analytical requirements.

After transitioning to an ELT architecture on a cloud data platform, Waterfall assets created a centralized repository where market data, portfolio information, and client data could be loaded in raw form. Transformations are now applied based on specific analytical needs, rather than being predetermined during the extraction phase.

This approach dramatically improved their operational agility. Portfolio managers now access integrated data within minutes of market events, rather than waiting for overnight batch processes. Risk assessments that previously took days can now be completed within hours, giving the firm a competitive edge in responding to market conditions.

The next step in ELT

Organizations face a growing tension between democratizing data access and maintaining control. ELT processes offer a unique opportunity to resolve this tension by empowering business users while preserving governance standards. When implemented correctly, these approaches create the perfect balance of flexibility and control, enhancing cloud efficiency through effective collaboration in data workflows.

The most successful self-service ELT implementations use standardized components that business users can assemble without IT intervention. Companies can deploy modular transformation frameworks where users connect pre-validated data blocks while automated validation ensures transformations meet quality thresholds before reaching production.

Permission inheritance models represent another powerful strategy, where access rights flow naturally from source data to downstream transformations. This maintains security boundaries while eliminating the bottleneck of manual permission reviews for each new data product created by business users.

The key to successful self-service ELT lies in shifting governance from gatekeeping to enablement. By embedding controls into the transformation process itself rather than blocking access, organizations can dramatically expand data-driven decision-making while strengthening—not weakening—their overall self-service analytics governance posture.

Enhance your ELT processes with AI self-service tools

Cloud data platforms like Databricks have revolutionized data integration by providing powerful processing capabilities that handle massive datasets with unprecedented speed. These platforms offer robust environments where organizations can execute complex ELT workloads, using distributed computing and optimized storage formats.

Yet a significant gap remains between this technical infrastructure and the business users who need data insights to make critical decisions. This is where AI-powered self-service tools are transforming the ELT landscape.

Here's how AI-powered tools like Prophecy enhance your ELT processes with cloud efficiency:

• Visual, low-code interface that simplifies the creation and management of ELT pipelines without sacrificing power or flexibility.
• AI-assisted transformation suggestions that automatically recommend optimal ways to transform your data based on content and context.
• Self-service data preparation enabling business users to access, clean, and transform data independently.
• Built-in governance and lineage tracking to maintain data quality and compliance while expanding access.
• Seamless integration with Databricks to leverage their computational power and enhance cloud efficiency.

To overcome the security risks and hidden costs of traditional ETL processes that limit your organization's data access, explore The Death of Traditional ETL to discover a modern approach that enables AI use cases and accelerates cloud adoption.