Did you know that 68% of enterprise data goes unused while data engineers spend 44% of their time maintaining pipelines? The shift from ETL (Extract, Transform, Load) to ELT (Extract, Load, Transform) is changing how businesses manage data pipelines, offering faster processing and better scalability by leveraging cloud platforms like Snowflake, BigQuery, and Redshift.
Key Differences Between ETL and ELT:
- ETL: Transforms data before loading it into a database. Best for structured data and industries with strict compliance (e.g., healthcare, banking).
- ELT: Loads raw data into a cloud warehouse, then transforms it. Ideal for handling large-scale, unstructured, and real-time data (e.g., e-commerce, logistics).
Quick Comparison:
| Feature | ETL | ELT |
|---|---|---|
| Processing Location | Dedicated server | Within the data warehouse |
| Data Types | Structured | Structured, semi-structured, unstructured |
| Speed | Slower | Faster with parallel computing |
| Cost | Higher upfront costs | Pay-as-you-go cloud pricing |
| Security | Custom security tools required | Built-in cloud security |
| Use Case | Regulated industries (e.g., healthcare) | Real-time analytics (e.g., clickstream data) |
ELT takes advantage of cloud scalability, reducing costs, speeding up analysis, and simplifying workflows. If you're considering a switch, assess your current setup, choose the right data warehouse, and ensure your team is trained for ELT workflows. This shift can save time, cut costs, and unlock faster insights for your business.
ETL vs ELT: Core Differences
How ETL Works
ETL relies on a dedicated server to transform data before it's stored. This method uses a Schema-on-Write process, meaning all transformations happen prior to storage [2].
For example, in healthcare, ETL ensures patient data is validated and structured before entering the system. This guarantees compliance with industry standards and maintains data accuracy for medical reporting [5].
ETL is well-suited for handling complex transformations in smaller, regulated datasets. It's commonly used in industries with strict compliance requirements or when extensive pre-storage data cleaning is necessary.
How ELT Works
ELT, on the other hand, loads raw data directly into a data warehouse. Transformations are then handled using cloud processing [3].
A real-world example: Inspyrus used Striim to set up an ELT pipeline for real-time data replication to Snowflake. They paired this with dbt for transformations, cutting processing time from weeks to just moments [4].
These two methods differ significantly, setting the stage for a detailed comparison of how they operate.
ETL and ELT Comparison
Here's a side-by-side look at how ETL and ELT stack up in key areas:
| Feature | ETL | ELT |
|---|---|---|
| Data Compatibility | Mostly structured | Structured, unstructured, semi-structured |
| Cost Structure | Higher upfront costs for tools and servers | Pay-as-you-go cloud pricing |
| Security Controls | Requires custom security applications | Built-in warehouse security features |
| Ideal Use Case | Banking transactions, healthcare records | E-commerce analytics, clickstream data |
Choosing between ETL and ELT depends entirely on your business needs. For instance, manufacturing companies often use ETL to process moderate volumes of operational data that require a high level of cleanliness. Meanwhile, logistics companies lean toward ELT for managing massive amounts of real-time tracking data [5].
Industry experts caution that more than 80% of digital organizations risk failure if they don’t adopt modern data governance practices [2].
Benefits of Switching to ELT
Modern data teams are increasingly moving toward the ELT model. Here's a closer look at why this shift makes sense.
Cloud Platform Advantages
Cloud platforms like Snowflake, BigQuery, and Databricks have changed how data is processed. These platforms allow data transformations to happen directly within the data warehouse, thanks to their powerful cloud computing capabilities. For instance, Snowflake supports parallel processing, enabling transformations to run across multiple compute clusters at the same time. This setup not only speeds up processing but also simplifies system architecture by removing the need for separate transformation servers [6]. The result? Quicker insights and a streamlined infrastructure.
Faster Data Analysis
Did you know data engineers spend nearly half their time - 44% - on managing pipelines? This costs companies around $500,000 annually [1]. ELT helps reduce this time by providing immediate access to raw data and allowing transformations to happen on-demand. Unlike traditional ETL servers, modern data warehouses handle transformations faster, which is particularly helpful for real-time analytics and processing large datasets [8].
Lower Costs and Better Scaling
ELT offers a more cost-effective and scalable solution compared to traditional ETL. Here's a breakdown:
| Cost Factor | Traditional ETL | Modern ELT |
|---|---|---|
| Infrastructure | Dedicated servers, high upkeep | Pay-as-you-go cloud pricing |
| Processing Power | Limited by hardware | Auto-scalable cloud resources |
| Storage Expenses | Fixed costs for capacity | Flexible, usage-based pricing |
| Maintenance | Significant IT overhead | Minimal maintenance required |
With ELT, companies can start small and scale as needed. For example, Matillion's ELT solutions range from $1.37 per hour to $5.48 per hour for larger workloads [7]. This flexible pricing eliminates the need for hefty upfront hardware investments.
A survey from June 2020 by Dimensional Research revealed that 86% of data analysts dealt with outdated data, and 90% faced unreliable data sources within the previous year [1]. ELT helps tackle these issues by enabling faster data ingestion and leveraging cloud-native processing for more reliable results. These improvements allow data teams to adapt quickly to changing business needs.
sbb-itb-695bf36
How to Set Up ELT Systems
Check Your Current Setup
Before moving to ELT, take a close look at your current data setup. Studies show that ETL workloads make up over 50% of data processing costs [10]. Start by mapping out your data sources, transformation processes, and any downstream dependencies.
Here are some key areas to focus on:
-
Data Source Assessment
Check if your data sources are compatible with cloud platforms. Note the volume, speed, and variety of your data to plan for scaling. -
Infrastructure Evaluation
Identify your existing ETL processes and pinpoint which transformations can shift to the data warehouse layer. Make sure your tools can integrate with ELT workflows. -
Team Capabilities
Modern data workflows often divide responsibilities: Data Engineers manage Extract-Load tasks, while Analytics Engineers handle transformations [9].
Once you’ve assessed your setup, the next step is selecting a data warehouse that matches your needs and takes advantage of cloud-based scalability.
Pick the Right Data Warehouse
Choosing the right data warehouse is a key step in making ELT work. Here’s a quick comparison of popular platforms based on different use cases:
| Feature | Snowflake | Databricks | BigQuery |
|---|---|---|---|
| Best For | SQL and BI workloads | ML and advanced analytics | Large-scale data processing |
| Scaling | Automatic | Auto-scaling clusters | Serverless scaling |
| Data Types | Structured, semi-structured | Structured, semi-structured, unstructured | Structured, semi-structured |
| Learning Curve | Low | Moderate to High | Moderate |
| Cost Model | Usage-based | Consumption-based | Pay-as-you-go |
Your choice should align with your specific needs. For example, if your team relies on Google’s ecosystem, BigQuery could be a natural fit. On the other hand, if machine learning is a priority, Databricks provides strong ML and AI tools [11].
Data Security and Control
Strong security is non-negotiable in ELT systems. Security expert Bruce Schneier puts it bluntly:
"Data is a toxic asset, we need to start thinking about it as such, and treat it as we would any other source of toxicity" [14].
Here are some essential measures to put in place:
-
Access Control
Use role-based access control (RBAC) and enable two-factor authentication [13]. -
Data Protection
Encrypt your data both at rest and in transit. For sensitive data, use masking or tokenization [14]. -
Compliance Monitoring
Set up audit trails and logging to track who accesses and modifies data. This helps ensure you meet regulations like GDPR, HIPAA, and CCPA [12].
Common ELT Problems and Solutions
Implementing ELT can come with its fair share of challenges, even though the approach offers many advantages. Below are some common problems teams face and actionable solutions to address them.
Data Quality Control
Poor data quality costs organizations an average of $12.9 million annually [15]. Since ELT systems load raw data directly into warehouses, maintaining data quality becomes essential.
Strong quality controls can turn lengthy workflows into near-instant processes while ensuring data remains accurate and reliable. Here's a breakdown of how to maintain quality at different stages:
| Quality Control Layer | Purpose | Key Actions |
|---|---|---|
| Ingestion | Prevent bad data entry | Validate data types, check formats |
| Storage | Maintain data integrity | Deduplicate, enforce referential integrity |
| Processing | Ensure accurate results | Run automated checks, perform reconciliation |
"One of the most important things is to have the ability to detect data quality issues, so I advise not only having a strong data model in your data warehouse or operational data store, with thorough and enforced referential integrity, but also to implement automated data quality checks involving aggregated measures like counts, averages, min and max between source and target" [16].
Once quality is under control, the next hurdle is dealing with large volumes of data.
Processing Big Data Sets
Handling massive data sets efficiently is critical for ELT workflows. For example, Enigma Engineering scaled their Pandas ETL job to process 600GB of data in May 2019. They achieved this by using smart partitioning and optimizing data types, reducing memory usage by 50%, and leveraging bulk data insertion with psycopg2.
Here are some strategies for processing large data sets:
- Smart Partitioning: Break data into logical chunks for parallel processing and faster queries.
- Z-Ordering: Optimize storage layouts for quicker retrieval, especially for time-series data.
- Aggregation Tables: Create summarized views to improve query performance.
A real-world example: Kyle Hale used PowerBI and Azure Databricks to query one trillion rows. By partitioning the data by device ID and day, and applying z-ordering by hourminute, they achieved an average query response time of just 2.12 seconds for line chart visualizations [17].
Staff Training for ELT
Switching to ELT requires teams to gain new skills. Proper training and change management are crucial to help staff adapt to updated tools and workflows [3].
Focus training efforts on these areas:
| Training Focus | Description | Expected Outcome |
|---|---|---|
| Technical Skills | Cloud operations, SQL optimization | Better pipeline efficiency |
| Process Knowledge | ELT workflows and best practices | Improved decision-making |
| Tool Proficiency | Hands-on experience with ELT platforms | Higher productivity, fewer errors |
Equipping your team with the right skills ensures they can handle ELT's demands and maximize its potential.
Conclusion
The shift from ETL to ELT has reshaped how organizations handle data pipelines, leveraging the speed and scalability of cloud technologies. Take Gill Capital as an example: by using ELT with Fivetran and Google BigQuery, they boosted sales by 25% and gained near real-time insights into their retail operations [18]. These real-world results highlight how ELT can drive meaningful business improvements.
From a financial perspective, ELT offers clear benefits. For instance, Trinny London saved around $340,000 annually by cutting out manual pipeline management and reducing reliance on complex local infrastructure [18]. Similarly, Cemex uses ELT across more than 1,800 facilities, showcasing its ability to scale efficiently [18].
"With Fivetran, I replicated all of our data in 10 business days - every table and every field - from both our on-prem and cloud ERPs, which saved us about $360,000 in initial setup and maintenance of our SQL Server and NetSuite connectors." – Nick Heigerick, IT Manager of BI at Oldcastle [1]
Beyond cost savings, ELT helps businesses stay agile in the face of changing demands while ensuring data remains accessible and accurate.
To make the most of ELT, organizations should focus on three key areas:
| Success Factor | Key Consideration | Business Impact |
|---|---|---|
| Infrastructure Assessment | Review current data systems and warehouse capabilities | Better resource allocation |
| Implementation Strategy | Begin with simple, scalable solutions | Faster results and ROI |
| Team Enablement | Provide training and support for teams | Higher adoption and efficiency |
ELT's cloud-first model offers the adaptability and performance today’s businesses need. By embracing this approach, organizations can transform their data into actionable insights, reduce expenses, and streamline operations.
