ETL vs ELT: Choose the Right Data Strategy for Speed, Scalability, and Compliances

“There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days.” ~ Eric Schmidt, Executive Chairman at Google.

It is rightly said that data is the new oil or the strong pillar of businesses and is a significant factor in the decision-making process. Name any company, and you will notice how successful they are. The entire credit goes to their decisions after processing the collected data.

Amazon uses data to understand customer behavior and create new products, driving 35% of sales.

Netflix relies entirely on data (users binge-watching shows and providing them with similar shows), which resulted in a 93% user retention ratio.

Starbucks offers rewards programs for receiving customers’ buying habits and then providing them with better products and constant updates. (source: renascence.io).

The list includes Uber, Google, Walmart, and others, but the use of ETL or the ELT process remains common and constant among all these companies. It is an indispensable approach that has made companies billions of dollars globally.

ETL (Extract, Transform, Load) is the dominant data pipeline architecture since it ensures data cleaning and formatting before storage. On the other hand, ELT has gained momentum due to the rise in cloud computing and big data. It involves loading raw data and then transforming it. Considering the ever-evolving industry requirements and rapid data generation, choosing between ETL vs ELT becomes the top priority. This guide will explore these two approaches, helping decision-makers choose the best fit for their data strategy.

How Do ETL and ELT Work?

Data generation and data integration tools are abundant. However, among the crowd, ETL and ELT outshine the most commonly used approaches to processing structured and& unstructured data. After reading about a lot of confusion about the two online, we decided to meticulously compare their data pipeline architectures to help you make decisions. So, let’s begin.

ETL (Extract, Transform, Load): How It Works – Transform Before Loading

ETL is an age-old data integration method in which data extraction from multiple sources (APIs, web pages, files, ETL databases) happens initially.

After extracting, the data undergoes cleansing, aggregating, and/or filtering, removing duplicates, creating a tabular format (if required), summarizing and groups, or merging.

Once the structured data is obtained from the raw and unstructured data sets, it is loaded into data warehouses or data marts like Snowflake, Amazon RedShift, Google BigQuery, Oracle Exadata, or Cloudera Data Platform.

ELT (Extract, Load, Transform): How It Works – Load First, Then Transform

ELT is a cloud-native data integration method slightly different from its peers ETL. This one loads after data collection and then transforms. Here’s the breakdown:

The first step in ETL and ELT methodologies is the same: collecting raw data from varied sources.

The data is then loaded into cloud-based data lakes or warehouses, such as Azure Data Lake Storage, Databrick Lakehouse, Apache Hadoop HDFS, Amazon S3, and Microsoft Azure Synapse Analytics.

The final step is processing the data inside the warehouse using SQL-based queries, Apache Spark Databricks, or dbt real-time analytics.

ELT and ETL: Key Differences That Impact Your Data Strategy

Choosing between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) is crucial for your data strategy, performance, and scalability. ETL transforms data before loading, ensuring structure and compliance, while ELT loads raw data first, leveraging cloud computing for flexibility and speed. Understanding these differences helps businesses optimize big data, real-time analytics, and compliance workflows.

Let’s compare them in detail.

Businesses wanting to structure and secure data should opt for ETL development services, while modern businesses looking for a time-saving approach can opt for the ELT data integration method.

Breaking Down the Cost to Maximize ROI in ETL and ELT

The cost is one factor to consider when choosing between ETL and ELT. It includes infrastructure, storage, computing power, licensing, and other operational expenses.

While ETL follows a structured and pre-determined method, ELT relies on cloud scalability and cost efficiency. Though ELT is affordable, it still has challenges in governance and security. Thus, it is advisable to examine every input and conclude.

Infrastructure & Storage Cost

• Extract, Transform, Load

This traditional data integration tool requires dedicated servers, data staging areas, and on-premise infrastructure to enable transformation before final data loading. These require heavy capital expenditures. In addition, this tool requires regular maintenance, which increases its cost over time.

• Extract, Load, Transform

This modernized approach is quite budget-friendly compared to ETL. It requires no staging areas since raw data is directly uploaded to a cloud-based data warehouse. Besides, there is no need to incur infrastructure costs, which results in cost cuts compared to ETL. However, one challenge is that cloud storage costs may rise if data governance is well-optimized.

Compute & Processing Cost

• Extract, Transform, Load

ETL data processing requires dedicated processing power on-premise or through a managed service. Due to the ongoing and constant requirements for data processing, computing costs are growing.

• Extract, Load, Transform

In this scenario, ETL leverages built-in compute capabilities to process data within a cloud-based data warehouse. This allows businesses to use pay-as-you-go compute pricing, reducing the fixed cost burden. However, the challenge is that costs may increase if the workloads are not correctly optimized.

Licensing & Operational Cost

• Extract, Transform, Load

Traditional ETL tools like Talend, Informatica, and SSIS require licensing fees, maintenance costs, and dedicated resources. Manual tracking updating and timely performance tuning are other segments that may require significant investment.

• Extract, Load, Transform

On the other hand, modern ELT solutions follow a pay-as-you-go model, meaning you pay only for the technologies you use. This reduces significant fixed costs. While fixed costs decrease, there may be hidden costs for managing complex transformations that require high computational power.

Governance & Compliance Cost

• Extract, Transform, Load

Industries like finance, healthcare, and manufacturing that rely on compliance prefer ETL processes. It’s because of pre-load transformation before loading the final output. Preloading ensures data privacy, encryption, and regulatory compliance. For this, you will have to invest in dedicated infrastructure.

• Extract, Load, Transform

One of the biggest challenges of this ELT architecture is storing raw data before processing, which increases the risk of compliance violations and government challenges. Any business that uses the ELT data integration method must implement additional security layers to protect sensitive information from data leaks or theft.

Final Take on Cost Comparison

Businesses that adhere to compliance requirements, require structured processing and have easy-to-predict data should invest in Extract, Transform, and Load. However, such companies should also be well-prepared for higher upfront and maintenance costs.

Conversely, businesses opt for cloud-based architecture that ensures the pay-as-you-go pricing model works well with the Extract, Load, and Transform data integration technique. Such companies must also deal with governance and control costs.

Security, compliance, and cost are crucial in determining your business’s proper data integration approach.

Security & Compliance You Must Consider for ELT/ETL Processes

Security and compliance are other factors that determine the best ETL or ELT. Industries like manufacturing, healthcare, government, and finance require strict adherence to data security policies. With regulations like GDPR, HIPAA, SOC 2, and CCPA, any business must systematically adhere to data extraction, storage, and transformation without any leakages.

Data Security Risk in ETL vs ELT

Both data integration processes handle data extraction and movement across varied systems, which increases data theft or leak risks. There are several ELT and ETL security measures and encryptions taken into consideration:

• Extract, Transform, Load Security Considerations

The ETL process requires masking, encrypting, or anonymizing data before loading. This helps reduce the risk of security vulnerabilities.

Another best practice the ETL process requires is on-premise or private cloud hosting, which gives businesses additional control over data.

ETL tools delete raw and unstructured data after transformation to curb the risk of data breaches or threats.

• Extract, Load, Transform Security Considerations

Advanced ETL solutions protect raw and unstructured data with built-in encryption, role-based access control, and multi-factor authentication.

Since ELT processes depend entirely on cloud-based security features, it is important to select a cloud-based environment. Some cloud warehouses provide AES-256 encryption for data transformation and private endpoints to prevent additional or third-party access.

Compliance Considerations Between ELT or ETL: Which One is Better?

Compliance is the third most important factor to consider after cost and security. Here is a breakdown of different compliances and how ETL and ELT handle them.

GDPR & CCPA Compliance for Data Privacy

• ETL data integration masks, anonymizes, and encrypts data before loading, which protects and ensures compliance.
• Raw, unprocessed, and unstructured data in ELT is at higher risk due to loading before transformation.

HIPAA Compliance for Maintaining Healthcare Data Security

• All the protected health information (PHI) offers better HIPAA compliance since it is transformed and secured before storage.
• On the other hand, ELT requires additional security layers to protect protected information.

SOC 2 & Financial Compliance for BFSI

• ETL is the right choice for SOC 2 compliance. It ensures tighter control over data transformation before loading it into the data warehouse.
• ELT requires security governance to protect raw and unstructured financial data, leading to high upfront costs.

As you know, ELT is always an inferior data integration method in battle: ETL vs ELT. Should you never leverage it? Or aren’t there any practices ELT follows to curb security threats? Well, the answer lies in the next point.

What are ELT solutions for handling security challenges in cloud-based data warehouses?

The following are the ELT security measures:

• Offer end-to-end encryption for the raw data in transit or at rest.
• Cloud-based data warehouses offer role-based access control and data masking that restricts access to confidential data.
• Several platforms like Snowflake, RedShift, and BigQuery have audit logging and tracking tools that monitor data access and alterations.
• With zero-trust architecture, ELT ensures no unauthorized access to data.

Conclusion

Deciding between ETL and ELT requires strategically assessing your data needs, compliance requirements, and infrastructure. ETL is the go-to choice for businesses handling structured data with strict governance and compliance mandates (e.g., finance, healthcare). By transforming data before loading, ETL ensures data quality, security, and regulatory adherence, making it ideal for industries with critical data integrity and control.

Conversely, ELT is better suited for cloud-first organizations with high-volume, unstructured, or real-time data. By leveraging cloud data warehouses like Snowflake, BigQuery, and Databricks, ELT enables faster processing and scalability while reducing infrastructure costs.

Businesses must evaluate data complexity, security needs, and long-term scalability to make the right choice. In some cases, a hybrid approach combining ETL’s governance with ELT’s flexibility may be the most effective solution for a modern data strategy.