Old School or Still Cool? Top Reasons To Choose ETL Over ELT

Cases Where ETL Still Bosses Over ELT

In the past few years, ELT has become very popular, mostly because more people are using big data and cloud-based solutions. ELT does have some benefits, but it may not be the best choice for all uses.

In some situations, ETL may be a better and more efficient choice. In this article, we’ll look at the situations where ETL is still better than ELT. We will talk about situations where ETL is the best choice because of the complexity of the data transformations, the size of the data, and the need for data quality and consistency. By the end of this article, you’ll have a better idea of when ETL is better than ELT for integrating data.

When Should You Use ETL?

The following are some common scenarios that warrant the use of ETL vs. ELT.

When Data Requires Complex Transformation

ETL is the way to go when the data needs to be changed in a complicated way. ETL is good for these kinds of situations because it is built to handle complex data transformations.

Data Aggregation

Putting together information from different sources and summarizing it based on different criteria.

Data Normalization

Making sure data from different sources is the same and getting rid of duplicates. Standardizing names, addresses, and other data elements is one example.

Data Cleansing

Find mistakes in the data, like missing or duplicate records, and fix them to make sure the data is accurate.

Data Mapping

Changing the format of data, like putting data from different sources into a standard format.

Data Enrichment

Adding more data to a set of data that already exists, like demographic or geographic data, to give more insight and context.

Data Transformation

Data manipulation, such as calculating metrics, making new data elements, and analyzing trends, is used to get insights from data.

When You Need To Combine Data From Multiple Sources

ETL is also a great choice when your project needs data from different sources to be put together. Using ETL, data from different places, like databases, flat files, and even cloud services, can be pulled out and put into a single repository.

This central data repository has a single version of the data that can be used by many teams and systems. It is also easier to access and manage. ETL tools also standardize and clean the data before it is put into the system where it will be used. This keeps the data’s quality and accuracy at the same level.

Here are some examples of situations like that:

Combining Sales Data

A company’s sales data may be kept in different databases, like customer data in one database and transactional data in another. ETL can easily combine all of this data into a single format for analysis, making sure none of it is duplicated or inconsistent.

Putting Together Customer Data

Data about customers is often kept in more than one system, like CRM, billing, and support systems. ETL can help integrate this data and create a single, complete view of the customer. This gives information about how and what customers like to do.

Consolidating Financial Data

Financial data, such as invoices, payments, and expenses may be stored across multiple systems or in different formats. ETL can help bring all of this data together and standardize it for reporting and analysis. This makes financial reporting more accurate and consistent.

Incorporating Marketing Data

Organizations may have marketing data stored across different platforms, such as email marketing, social media, and digital advertising. ETL can help bring all of this data together and give a full picture of how marketing is doing so organizations can make decisions based on the data.

In these examples, ETL is a better choice over ELT as it allows organizations to efficiently transform and combine data from multiple sources before loading it into a target database. ETL enables efficient data processing, aggregation, transformation, and cleansing before loading, ensuring high-quality data and faster analytics processing times. On the other hand, ELT may lead to slower processing times and more complex data integration workflows, especially when dealing with multiple sources and complex data transformations.

When Data Needs To Be Cleansed

ETL tools have built-in functions for cleaning and validating data, which can be used to get rid of duplicate, inconsistent, or missing data at the source. This helps confirm the quality and accuracy of the data, ensuring the data is accurate and reliable. ETL tools can also standardize the data, which makes it easier to work with and makes it more useful.

Data cleansing is an important part of integrating data, and organizations need to make sure their data from different sources are correct and consistent.

Here are some examples where data needs to be cleansed:

Removing Duplicates

Organizations may have duplicate data in multiple systems, such as customer or product data. ETL can help identify and remove these duplicates before loading the data into a target system, ensuring data accuracy.

Standardizing Data

Data may be stored in different ways on different systems, which can cause problems. ETL can help standardize the data, such as standardizing names, addresses, and other data elements, before loading it into a target system.

Correcting Errors

Data may have mistakes, like missing or wrong values, that need to be fixed. ETL can help identify and correct these errors before loading the data into a target system, ensuring data accuracy.

Handling Missing Values

Data may have missing values, which need to be handled appropriately. ETL can help fill in these gaps with the right values or flags, making sure the data is correct.

In these situations, ETL is better than ELT because it makes it easier for organizations to clean up data from multiple sources before loading it into a target database. ETL provides extensive data transformation capabilities, including data cleansing, data validation, and data profiling, ensuring data accuracy and completeness. On the other hand, ELT might not have the right tools to change data, which could make the data in the target system less accurate and more inconsistent.

When Data Requires Historical Tracking

ETL tools can store historical data and give a full picture of how things have changed over time. This can be useful for tracking trends and making informed decisions, providing valuable insights into the data.

For organizations to understand trends, spot patterns, and make decisions based on data, they need to keep track of data over time.

Here are some situations where data needs to be tracked back in time:

Tracking Changes in Data

Organizations may need to track changes made to data over time, such as changes to customer information or product pricing. ETL can help keep track of how these changes were made in the past, so organizations can audit changes to data and see where they came from.

Financial Reporting

Organizations may need to keep track of their income, expenses, and profit margins over time. ETL can help make historical records of this data, which allows businesses to look at trends and make smart financial decisions.

Reporting on Compliance

Organizations may need to keep track of data for regulatory reasons, like keeping track of changes to data. ETL can help make historical records of this data, which allows companies to show compliance and keep audit trails.

Customer Behavior

Organizations may need to keep track of how customers act over time, such as how often they visit a website, what they buy, and how they talk to customer service. ETL can help create historical records of this data, which allows businesses to study how customers act and improve their services.

In these situations, ETL is better than ELT because it makes it easier for organizations to collect and store historical data before loading it into a target database. ETL has many ways to change data, such as tracking data history, data versioning, and data lineage. This ensures the data used for analysis is accurate and reliable. On the other hand, ELT may not have the necessary data transformation capabilities, leading to incomplete or inconsistent historical data in the target system.

Conclusion

ETL and ELT have their pros and cons, so businesses need to think carefully about their data processing needs before deciding which one to use. But ETL is better than ELT for complex data transformations, cleaning data from multiple sources, keeping track of the past, and automating tasks. With the right ETL tools and expertise, businesses can improve their data processing capabilities, automate data processing and integration, and accelerate time-to-insight, enabling them to stay ahead of the competition and meet customer needs.