Data Engineering: Principles of ETL vs. ELT

Introduction

There is a long journey within data engineering, especially in the ETL process. ETL is an acronym that represents three essential stages in data engineering:

1. Extraction

2. Transformation

3. Load

What is explained for ETL also applies to ELT, where the order of the stages changes. These stages are fundamental for preparing raw data, making it suitable for analysis, storage, use in information systems, or integration between heterogeneous systems.

There is a growing movement in the market to refer to all of this as data integration. Some tools that previously presented themselves as ETL are now termed data integration tools, a broader approach.

ETL and ELT: Concepts and Differences

ETL is not a tool but a process. The implementation of ETL can vary, being done with programming languages, frameworks like Apache Spark, or tools like Airbyte and DBT. The choice depends on the company's data structure, available infrastructure, business objectives, professionals involved, and the urgency of data availability.

1. Extraction

The difference between ETL and ELT is primarily the order of transformation and loading. In ETL, the transformation occurs before the load, while in ELT, the load comes first.

During the extraction phase, data is collected from various sources such as databases, file systems, APIs, spreadsheets, and event logs. The complexity of extraction depends on the data source and available tools.

Extraction Examples

- Social Media: Extracting data for sentiment analysis.

- PDFs: Extracting data from invoices for financial analysis.

Extraction can be scheduled or manual, depending on the source and project needs.

2. Transformation

In transformation, extracted data is cleaned, reformatted, and restructured to make it useful and consistent. This may include removing duplicates or invalid data, converting data types, aggregating information, and applying specific business rules.

Transformation Examples

- Reformatting States: Converting state abbreviations to full names.

- Sentiment Analysis: Preparing social media data for analysis.

Transformation is essential to ensure that data is in a format suitable for effective analysis and insight creation.

3. Load

In the loading stage, data is loaded into a destination repository such as a database, Data Warehouse, or Data Lake. It is important to ensure that the loaded data is consistent with the destination schema and meets integrity and quality requirements.

The loading process can be continuous or scheduled, depending on project needs.

Characteristics of ETL and ELT

1. Transformation Location

- ETL: Transformation occurs before loading, usually in an intermediate staging area.

- ELT: Transformation occurs after loading, possibly on the destination server.

2. Performance and Scalability

- ETL: May require more hardware resources for data transformation before loading.

- ELT: Can be faster for large volumes of data, allowing transformations at less demanding times.

3. Flexibility

- ETL: Less flexible, as data must be transformed before loading.

- ELT: More flexible, allowing different transformations as needed.

4. Complexity

- ETL: Can be more complex to implement, requiring robust transformation logic.

- ELT: Tends to simplify the data integration process, especially with varied and unstructured sources.

The choice between ETL and ELT depends on the company's specific needs, data types and volumes, available infrastructure, requirements, and data storage and analysis strategy.

Tools for ETL and ELT

- ETL Tools

1. Apache NiFi (opensource)

2. Talend

3. Informatica PowerCenter

4. Pentaho Data Integration (PDI)

5. IBM DataStage

6. Microsoft SQL Server Integration Services (SSIS)

7. Oracle Data Integrator (ODI)

8. AWS Glue

9. Google Cloud Dataflow (GCP)

10. Azure Data Factory (Microsoft Azure)

11. Apache Airflow (ETL Process Orchestration)

- ELT Tools

1. Airbyte

2. DBT (Data Build Tool)

3. Fivetran

4. Stitch Data

5. AWS Glue

6. Google Cloud Dataflow and BigQuery

7. Azure Data Factory

8. Apache Airflow (ELT Process Orchestration)

Conclusion

The ETL process is fundamental in data engineering, ensuring the quality and utility of data that feeds analytical, reporting, and decision-making systems. However, data engineering also involves modeling and building data repositories, creating integration APIs, security, governance, metadata management, and, of course, ETL.

Thank you.