Intro to Data Engineering — Part 3 : ETL , ELT and Data Engineering.

In the second part of this series, we have got the basic understanding of Database, Data Warehouse and Data Lake. In this article, we will try to understand what is ETL, ELT and Data Engineering.

Do you often get confused by the terms ETL, ELT, and Data Engineering? These terms are sometimes used interchangeably, but there are some key differences.

What is Data engineering? It is the practice of designing, building, and maintaining the infrastructure that enables organizations to collect, store, process, and analyze large volumes of data. This includes creating and managing data pipelines, designing and implementing data warehouses and data lakes, and ensuring data quality and reliability. As simple as that!

Now to start, let’s differentiate between ELT and ETL.

ETL stands for Extract, Transform, Load, while ELT is Extract, Load, Transform. Both processes involve extracting data from multiple sources, but the main difference lies in what happens after extraction. With ELT, the data is loaded into a data lake and then transformed, while with ETL the data is transformed into a structured schema before being loaded into a data warehouse.

ELT is more favored by analysts and data scientists as they have the flexibility to transform the data directly from the data sources. The below diagram illustrates the difference between ETL and ELT.

Credit : Saras Analytics

Now, how is Data Engineering different from ETL and ELT? Views on this question may vary. Some developers argue that there is no difference, while others believe there is.

In our opinion, the difference between ETL/ELT and Data Engineering depends on the approach taken by the developer. ETL/ELT developers often use GUI-based tools like Informatica, while Data Engineers work with programming languages like Scala, Java, or Python, using cloud or open-source frameworks such as Glue or Spark. This gives Data Engineers more flexibility and a deeper understanding of what is happening under the hood.

In contrast, ETL/ELT developers are limited by the options of their GUI-based third-party tools. To summarize, a Data Engineer is an ELT/ETL developer, but not vice versa.

Here comes the main question. Which one is better? ELT or ETL?

To answers this question, we have to look at different factors of both approach. Both has there advantages and disadvantage. We will explore few of them below.

ETL

Advantages:

1. Can handle a wide range of data sources and formats.
2. Enables data cleansing and normalization.
3. Processing occurs before data is loaded into the warehouse.
4. Provides a structured approach to data integration.

Disadvantages:

1. Requires more upfront data preparation and cleansing.
2. Processing can be slower due to data transformation.
3. Can be more expensive due to the need for ETL tools.

ELT

Advantages:

1. Can handle large and complex data sets.
2. Allows for more flexible data analysis.
3. Processing occurs on the data warehouse.
4. Can be more cost-effective than ETL.

Disadvantages:

1. Requires a powerful data warehouse platform.
2. Data quality issues can be harder to detect.
3. Data governance and security can be challenging.

Overall, the choice between ELT and ETL depends on the specific needs and constraints of a given organization. ELT is often more suitable for large and complex data sets, and can be more cost-effective, while ETL is typically more appropriate for traditional data warehousing environments and provides a structured approach to data integration. Both approaches have their advantages and disadvantages, and organizations should carefully consider their requirements before making a decision.

That’s all for today. Next article in this series, we will explore some open source as well as cloud based tools and solutions that can be used for Data Engineering.