Extract, Load, Transform, or ELT, is a process that extracts data from the source, loads it directly into a data warehouse or data lake, and then transforms it to make it available for business intelligence tools. It supports all data types, from raw to structured. ELT is a popular way to ingest large volumes of raw data quickly, but it brings many security concerns with it.

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Table of Contents

  1. Sensitive Data Loads Without Transformation
  2. Compliance and Data Governance Is More Complex
  3. ELT Is a Less Mature Technology Than ETL
  4. Corrupted or Compromised Data May Reach Your Data Stores
  5. Alternatives to ELT
  6. Using a Streamlined ETL Tool Like Xplenty

Sensitive Data Loads Without Transformation

One of the biggest ELT security issues occurs when the process loads sensitive data into a data lake with no data transformation taking place first. The information sits in the data store unmasked until it’s transformed, which makes it vulnerable to unauthorized access and usage. Data scientists, analysts, and other business users may pull this data as-is into business intelligence tools or reports, and run SQL queries on it. This data may also show up in system logs, exposing it to system administrators.

A lack of protection for your organization’s sensitive data can lead to many issues, including a loss of customer trust; data breaches and loss; regulatory fines and penalties; and other long-lasting problems.

Compliance and Data Governance Is More Complex

Data that falls under regulatory measures such as GDPR, HIPAA, and CCPA have specific handling requirements that ELT may violate. Since this data moves from the source to the data store in its original form, it becomes much more difficult to keep it secured from unauthorized parties.

Since ELT programs use cloud-based data lakes and cloud data warehouses, it’s also important to know the physical location of these servers. If you’re working with sensitive data of an EU citizen, for example, you may have a GDPR issue if you’re moving data between countries. Financial institutions may likewise face restrictions on where they can store or process data. 

Data governance also becomes more complex, which can lead to potential ELT security vulnerabilities. If it’s difficult to maintain proper access controls on each data set as it moves from the data source to the data lake and eventually analysis tools, then data exposure, loss, and breaches may be harder to stop.

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ELT Is a Less Mature Technology Than ETL

ELT is a relatively recent data integration development, especially when compared to Extract, Transform, Load, or ETL, technology. ELT being less mature than ETL affects its security in the following ways:

Fewer established implementation tools: ETL solutions have been around for decades, so there is a wider range of options for different use cases. ELT tools lack that long history, and you may end up picking a solution that doesn’t meet your security needs because of the limited choices.

Harder to source ELT talent: Finding ELT specialists is also challenging, whether you’re looking for in-house hires or managed services providers. If you lack ELT technical talent, you may not be able to optimize your data pipelines or discover potential ELT security vulnerabilities. The cost of recruitment may also be significant. 

ELT security best practices are still evolving: Developing comprehensive IT security best practices for ELT takes time, as these depend on real-world experience, the evolution of the technology, and trusted security measures. If organizations are slow to implement cutting-edge best practices, then new security threats and vulnerabilities could cause issues.

Corrupted or Compromised Data May Reach Your Data Stores

Another ELT security problem occurs due to the lack of transformation before the data reaches your data lake. If the source has corrupted or compromised data, you risk loading it into your systems. Sophisticated cyber attackers may use this vulnerability to gain access to the data you’re loading.

Problematic data, even if it’s not compromised, could have other ELT security impacts on your organization. If you have a security analysis tool relying on data from the data lake or cloud data warehouse, it may run into inaccurate or poor quality data that leads to bad decision-making, poor budget allocations, and other negative outcomes.

Alternatives to ELT

ETL solutions switch the way ELT works by changing the order of operations. The transformation layer occurs before the data loading. While this might seem like a minor change, it has huge implications for your data security.

When you transform your data immediately after extraction, you can:

Mask or remove sensitive data: Eliminate many security issues by preventing sensitive data from being loaded into your data stores. When it’s transformed into a masked form or removed from the data set, you don’t risk this information being used in reports or accessed by unauthorized parties.

Easily maintain compliance: Find ETL SaaS providers that comply with relevant regulations on their cloud platforms to take the guesswork out of the equation. Since you have tighter control over the data pipeline, you’re also able to implement the required security recommendations.

Filter poor quality data and metrics out of the set: Keep problematic data out of your data lake or warehouse entirely by cleansing it during the transformation step.

Using a Streamlined ETL Tool Like Xplenty

Integrate Your Data Today!

Try Xplenty free for 14 days. No credit card required.

Ready to make the switch from ELT to ETL processes to prevent an ELT security tragedy? Xplenty offers a cloud-based, user-friendly, no- and low-code solution for your ETL data pipelines. We follow strict security standards to keep your data safe as it moves from your sources to your data stores. Explore our platform with a fourteen-day trial and learn more about ETL vs ELT.