ETL vs. ELT: Choosing the Right Data Integration Approach for Data Professionals

Whether you are a data analyst who prefers structured data to analyze information or a data scientist who likes to process raw data, your choice of organizing data matters.

But wait, why is this choice so critical?

Data integration,like a puzzle piece, is the process of combining multiple data sources and datasets from various locations in a single data repository. Data integration is like bringing all your data pieces to the same table. It's crucial for data professionals like you because it stops data from being locked away and hard to use. This means you get more data to work with, and that leads to better insights. 

With data integration:

1. Your data management gets better.

2. The quality of your data goes up.

3. Your data becomes easily accessible

4. Plus, it saves you a ton of time and effort.

   
   

There are two important ways to organize data and process information: ETL & ELT. 

In today's guide, we will explore these two approaches and help you decide which is more suitable for your data adventures. The choice between ETL and ELT can significantly impact your data journeys, affecting everything from data quality to project flexibility.

Understanding ETL (Extract, Transform, Load) 

In this section, we'll delve into the ETL approach, which stands for Extract, Transform, Load,

Defining ETL - The Three Essential Steps

ETL is a process that extracts, transforms, and loads data from various sources like API, a database, or a file to a single data source that is loaded into a data warehouse. With the help of the ETL process, the extracted data is then transformed to match the requirements of the destination system.

Data Extraction: Data extraction is the first stage of the ETL process. It involves collecting data from various sources such as websites, databases, or files. Raw data is extracted from source locations to a staging area during data extraction. Those sources comprise of flat files, email, web pages etc.

Data Transformation: In the staging layer, the raw datasets undergo processing before being loaded into a data warehouse for analysis and reporting.In this phase, data is refined and transformed. It starts with filtering, cleansing, and de-duplication to ensure data quality.

Given the volume and variety of sources that raw datasets come from, the transformation stage is widely known as the most critical step in the ETL process, as it optimizes data integrity and ensures that the data is fully compliant & available for use when it reaches a new location.

Load: The transformed data is moved from the staging area into a target data warehouse in this last step. Usually, this includes an initial data load, followed by regular updates with incremental changes and occasionally, full data refreshes to replace the warehouse contents. In most organizations employing ETL, the process is automated, clearly defined, operates continuously, and is driven by batch processing. Typically, ETL procedures are scheduled during off-peak hours when both source systems and the data warehouse experience minimal traffic.

When is ETL Great?

ETL can benefit data analysts who must clean, reshape, and aggregate data before performing analysis. It allows you to create structured, cleansed datasets for analysis.

ETL will be your best friend if you always work with organized, structured data as a data analyst. It helps you clean and prepare that data for analysis, just like getting a puzzle ready to solve.

Tools like Talend, Informatica and Excel can be used to perform ETL. These tools streamline each step of the process, making data handling more efficient.

Exploring ELT (Extract, Load, Transform):

In this section, we'll delve into the ELT approach, which stands for Extract, Load, Transform

Defining ELT and its Order of Operations: 

The ELT process starts with Extraction, where data is collected from various sources. This raw data is then Loaded into a central storage system, such as a data lake or data warehouse, without immediate transformation. The Transformation step comes later, allowing for flexible data processing and analysis. 

ELT is comprised of a data pipeline with three different operations being performed on data:

Data Extraction: The first step is to Extract the data. Extracting data is the process of identifying and reading data from one or more source systems, which may be databases, files, archives, ERP, CRM, or any other viable source of valuable data.

Load Data: The second step for ELT is loading the extracted data. Loading is the process of adding the extracted data to the target database.

Data Transformation: The third step is data transformation. Data transformation is the process of converting data from its source format to a desired format required for analysis. Transformation is typically based on rules that define how the data should be converted for usage and analysis in the target data store. Although transforming data can take many forms, it frequently involves converting coded data into usable data using code and lookup tables.

Examples of transformations include:

• Replacing codes with values

• Aggregating numerical sums

• Applying mathematical functions

• Converting data types

• Modifying text strings

• Combining data from different tables and databases

ELT’s popularity in Big Data and the Cloud: ELT has gained a reputation in the era of big data and cloud computing. The ability to store vast amounts of raw data is a game-changer. It aligns perfectly with cloud platforms like AWS, Azure, and Google Cloud, offering scalability and cost-effectiveness.

When is ELT Great?

ETL is helpful when data scientists need to work with raw, unstructured data for exploration and experimentation. It's also ideal for organizations dealing with massive data volumes, as it allows for on-demand scaling.

Tools like AWS Glue, Google BigQuery, and Azure Data Factory are designed for data integration in the cloud. Tools like Apache Spark are meant for dealing with big data for ELT tasks.

Key Considerations for Data Analysts:

In order to be a successful data analyst, during the data ingestion process, remember the following things:

Data Cleaning and Structuring: Data analysts heavily rely on clean and structured data for accurate analysis. ETL is advantageous in this context, allowing analysts to prepare well-organized datasets tailored for analysis.

Importance of Data Quality: Quality data is paramount for data analysts. ETL helps maintain data quality through rigorous cleansing and structuring processes.

Key Considerations for Data Scientists:

To address  data scientists' unique needs, we need to consider the following things:

Flexibility and Access to Raw Data: Data scientists thrive when they have the flexibility to work with raw data. ELT provides direct access to raw data, allowing for modeling and experimentation without constraints.

ELT in Big Data Projects: Data scientists often tackle big data projects, and ELT aligns perfectly with their workflow in such scenarios. It allows them to work with vast datasets efficiently.

Relevance of ETL for Data Scientists: While ELT is a strong choice for data scientists, ETL remains relevant in certain cases, especially when working with structured data or legacy systems.

Choosing the Right Approach:

In this section, we provide a framework for data professionals to make informed decisions:

Factors for Decision-Making: Consider factors like data volume, data structure, project goals, and available tools when deciding between ETL and ELT.

Decision-Making Tips: Offer practical tips for navigating the ETL vs. ELT dilemma. Encourage data professionals to assess their project requirements and choose the best approach to their needs.

Conclusion

In conclusion, data integration is essential for data professionals, whether data analysts or data scientists. The choice between ETL and ELT should be thoughtful, rooted in project requirements and goals. By making informed decisions, data professionals can unlock the full potential of their data and drive valuable insights for their organizations.

Key Takeaways:

Matching Approach to Data Type: ETL is valuable when dealing with structured data, ensuring it's analysis-ready. ELT shines when dealing with vast unstructured data, granting flexibility for exploration.

Scalability Matters: ELT's scalability and cost-effectiveness are game-changers in big data scenarios.

Consider Project Goals: Assess project goals and the specific requirements of your data professionals before choosing an approach.