ETL vs. ELT: Which Data Integration Approach Reigns Supreme

A Deep Dive into Performance, Scalability, and Real-Time Processing

Whether you're a seasoned data engineer or just starting, mastering ETL and processes is essential for effective data management.

In part one of this two-part series on ETL and ELT, you learned the basics of these two concepts. I shared some real-world stories and tips.

Now, we are diving a lot deeper. You will learn how ETL and ELT compare in terms of costs, scalability, security and much more.

But that’s not all. Based on your use case, I created a questionnaire to help you choose between ETL and ELT. Read the article and answer the questions at the end.

Enjoy!

Reading time: 9 minutes

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💎 Data Quality and Governance: Keeping It Clean and Compliant

ETL: Early Error Detection and Standardisation 🚨

When it comes to data quality, ETL has some advantages. Transformation happens before the data is loaded into your data warehouse. This means you have a dedicated stage for cleaning, validating, and standardising your data.

ETL allows you to:

• Catch and correct errors early: Apache NiFi's validation processors are an excellent example. They allow you to ensure data integrity before loading into your data warehouse.

• Ensure consistency across different data sources: Tools like Talend can help standardise data formats and units of measurement, ensuring uniformity.

• Apply business rules and data standards before the data hits your system: For instance, you can use Pentaho Data Integration to apply complex business logic and transformations.

In terms of governance and compliance, ETL gives you a clear checkpoint for implementing controls.

Need to mask sensitive data? Remove personally identifiable information (PII)? ETL provides a natural place to do this before the data enters your secure environment. For example, Informatica PowerCenter offers robust data masking capabilities to ensure compliance with regulations like GDPR and HIPAA.

ELT: Preserving Raw Data for Auditability 🕵️

At first glance, it might seem like ELT is at a disadvantage when it comes to data quality and governance. After all, you're loading raw data directly into your data warehouse.

But don't count ELT out just yet! With ELT, you preserve the raw data in its original form, which can be a big plus for data lineage and auditability.

If there's a question about where a piece of data came from or how you transformed it, you can always go back to the source. Moreover, modern data warehouses have features for data quality management and governance.

For example, Snowflake provides automated data quality checks, row-level security, and dynamic data masking.

Best Practices 🏆

ETL:

• Implement thorough data validation and cleansing in the transformation stage.

• Document all transformation rules for transparency.

• Use ETL tools that provide data lineage tracking, such as Apache Atlas.

ELT:

• Implement data quality checks in your data warehouse using tools like dbt.

• Use data cataloguing tools like Alation to keep track of your raw and transformed data.

• Leverage the security features of your data warehouse for access control and data protection.

Whether you choose ETL or ELT, data quality and governance should be ongoing concerns. According to Gartner, poor data quality costs organisations an average of $12.9 million every year.

Continuously monitor and improve these processes to build a foundation of trust that data can drive better decision-making across your organisation.

🤸 Flexibility and Future-Proofing: Tools and Skills

ETL: Mature Ecosystem and Specialised Skills 💪

ETL has existed for decades. It offers a rich ecosystem of mature, feature-rich tools with robust support.

In terms of skills, ETL requires a solid understanding of data modelling and programming languages like Python or Java. You'll also need to be familiar with the specific ETL tools you're using.

For example, Apache Spark provides a powerful engine for large-scale data processing. This engine allows you to write complex transformations in Scala 🤮 or Python.

On the other hand, Pentaho Data Integration offers a graphical interface for designing data pipelines. This makes it easier to visualise and manage your ETL processes.

ELT: Modern, Cloud-Native Tools ☁️

With the rise of cloud computing, ELT has gained popularity. Tools like Upsolver, dbt, and Mage are modern, cloud-native, and designed to work with today's data warehouses and lakes.

You'll need strong SQL skills for ELT, as much of the transformation happens in the data warehouse. Understanding cloud platforms and modern data warehouse architectures is also beneficial.

For instance, dbt allows you to define transformations in SQL and manage them as code. This promotes best practices like version control and testing.

Upsolver simplifies the process of ingesting and transforming streaming data. And that means it is easier to build real-time analytics pipelines.

Best Practices 🙌

ETL:

• Choose tools that align with your existing infrastructure and skill set.

• Invest in training for specialised ETL tools.

ELT:

• Focus on building strong SQL skills.

• Stay updated on the latest cloud technologies and data warehouse features.

🧶 Complexity: Implementation and Maintenance

ETL: Upfront Complexity 🤯

Implementation of ETL can be pretty complex. You're setting up a separate processing layer between your data sources and warehouse.

This means:

• Designing and building out your transformation logic upfront.

• Possibly needing separating servers or services to handle the transformation workload.

• Working with specialised ETL tools, each with its own learning curve.

For example, setting up an ETL pipeline with Apache NiFi involves configuring processors, connections, and controllers to handle data flow and transformation. This requires a deep understanding of the tool and its capabilities.

Maintenance can also be challenging. You'll need to update your transformation logic when your data sources change.

If your business requirements evolve, you might need to reprocess historical data. Tools like Talend provide features for monitoring and managing ETL jobs but still require ongoing attention.

ELT: Simpler Initial Setup 🎯

Initial implementation of ELT is often simpler:

• Extracting and loading data in its raw form.

• Defining and running transformations within your data warehouse, often using SQL.

• Many modern ELT tools are designed for ease of use, with intuitive interfaces and quick setup.

Let's take setting up an ELT pipeline with dbt, for example. dbt is all about writing SQL transformations and managing them with version control. This approach leverages the power of your data warehouse. This results in a reduction in the need for separate transformation infrastructure.

Maintenance with ELT can be more straightforward in some ways.

Your raw data is already in your data warehouse. So, adapting to changes in data sources or business requirements often means updating your transformation queries.

However, as your data product grows, you might need to manage a complex web of interdependent transformations. This can be a real pain.

⏱️ Real-Time Processing: Keeping Up with the Data Flow

ETL: Evolving for Real-Time 🗃️

Traditional ETL has been designed for batch processing – collecting data over time and processing it in chunks. This works well for many scenarios but is not ideal when you need up-to-the-minute insights.

However, many modern ETL tools have evolved to handle real-time or near-real-time processing.

Some techniques these tools use are:

• Micro-batching: Processing tiny batches of data frequently. For example, Apache Spark Streaming can process data in micro-batches, providing near-real-time processing capabilities.

• Stream processing: Continuously processing data as it arrives. Tools like Apache Kafka and Apache Flink can handle high-throughput, low-latency stream processing.

Challenges include:

• The transformation step can introduce latency.

• Scaling real-time ETL to handle high volumes of data can be complex.

• Ensuring data consistency across real-time and batch processes can be tricky.

ELT: Naturally Suited for Real-Time 🚀

ELT is often better suited to real-time processing out of the box.

Here's why:

• The initial 'EL' steps (Extract and Load) can happen quickly, getting raw data into your data/lakehouse in near real-time.

• Many modern data warehouses, like ClickHouse, can handle streaming data ingestion.

• Transformations can be applied on-demand or incrementally as new data arrives.

Challenges include:

• Ensuring your transformations can keep up with incoming data.

• Managing the compute resources needed for real-time transformations.

• Balancing real-time needs with batch processing requirements.

For example, using Snowflake's Snowpipe, you can continuously load streaming data into your data warehouse and apply transformations as needed.

🚤 Performance and Scalability: Speed Matters

ETL: Upfront Processing 🤹

ETL does much work upfront, processing data before it reaches the data warehouse. This can lead to slower initial load times, especially with large datasets. However, it can be easier on your database because it does not ask it to do the heavy lifting of transformations.

For instance, using Apache Spark for ETL lets you distribute the transformation workload across a cluster. This helps by reducing the load on your data warehouse.

ELT: Faster Initial Load 📥

ELT offers a faster initial load, getting data into your system quicker. However, it puts more strain on your database because it's asking it to handle transformations. This can impact query performance if not managed properly.

For example, performing transformations in Snowflake can leverage its scalable computing resources. However, you need to manage it carefully to avoid exploding costs.

Scalability 📈

• ETL: Can struggle as data volumes increase. The transformation step can become a bottleneck, slowing down the entire process.

• ELT: Often scales more gracefully with increasing data volumes. Modern data warehouses are designed to handle large-scale transformations efficiently.

For example, Google BigQuery can quickly handle massive datasets and perform complex transformations.

Scalability is a big one for growing businesses. A while back, I shared how this was the main reason to move from ETL to ELT.

Dext switched from ETL to ELT, it’s that easy!

💰 Data Storage and Costs: Balancing Act

ETL: Efficient Storage 📂

ETL can be pretty efficient in terms of storage. You often reduce the volume by transforming the data before you load it. You only keep what's relevant to your business needs, leading to significant storage savings.

ELT: Higher Storage Demands 🗄️

ELT is generally more demanding in terms of storage. You're loading all your raw data into your data warehouse before transforming it.

This approach requires more storage space, which can increase costs. And that is especially true if you're using cloud storage, where you pay for what you use. However, if you need it later, you have all your raw data available.

For instance, storing raw data in Amazon S3 before transforming it in Redshift can lead to higher storage costs, but it provides flexibility for future analysis.

Cost Considerations 💸

• ETL: Can be more cost-effective if you have a clear idea of what data you need and want to minimise storage costs.

• ELT: Could be more economical in the long run if you're using a cloud data warehouse with competitive pricing and want to keep all your raw data.

For example, using Google BigQuery's pay-as-you-go pricing model can be cost-effective for ELT, as you only pay for the storage and compute resources you use.

Want to dig deeper on the costs side? Check this Reddit thread.

🏁 Summary

As always, there’s no winner here (no, it’s not ELT). Choosing between ETL and ELT depends on your specific needs and setup.

ETL offers advantages in data quality, governance, and efficient storage, but it can be complex to implement and maintain. ELT provides flexibility and scalability, but it requires more storage.

Consider your long-term data strategy, costs, and your team's skills when deciding.

Take Action Today:

• Assess Your Current Pipelines: Identify areas where ETL or ELT can enhance performance, scalability, and data quality.

• Experiment with New Tools: Try modern tools like dbt for ELT or Apache NiFi for ETL to see how they fit into your workflow.

Want to make things easier? I created this free questionnaire to help you choose between ETL and ELT.

What did I recommend to you? Share your result in the comments.

Until next time,
Yordan

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