ETL and ELT - Definition

What is ETL?

When you get into data engineering, you encounter two specific terms multiple times: ETL and ELT. One is a more dated practice while the other is more relevant for today’s cloud-based world. ETL is a data integration pattern that emerged in the 1970s where you extract data from multiple applications and other sources, transform it, and then load it into different targets. It is a combination of those three data engineering processes and is mostly used for moving data from one database to another or into a repository. 

ETL is best for relational, structured data and is managed and governed by a company’s IT department. It can be resource-intensive and slow. In the context of data warehousing, ETL processes run outside the data warehouse so it can require the use of additional tools and languages to conduct and express transformations. 

A typical ETL pipeline will pull data from multiple sources and then conduct a series of transformations. As there are so many different sources, it is relatively easier to transform the data before upload as managing multiple pipelines requires orchestration which will allow the data to align with the larger goal of the pipeline.

What is ELT?

To optimize ETL, ELT was born. It’s the same processes in a different order: extract, load, then transform. The reason behind this change is because organizations can now load raw data and solidate everything without needing to worry about maintaining transformations or losing uniformity. Data transformation is a cumbersome process that involves converting data from its source format into a format that is amenable to analysis. So, in an ELT approach, all transformations occur after data has been loaded into a data warehouse.

The data pipeline in ELT is split into two: first is data extraction and loading into a data warehouse, and second is performing SQL scripting on the data so that it can be transformed. While this consumes warehouse resources, it only requires the use of SQL and not any other tools or languages unlike ETL. 

In a cloud-centric world, ELT allows for a lot more flexibility and speed. It’s the smart choice for business intelligence and big data analytics as it can better handle unstructured data because data is copied exactly onto the warehouse. ETL is a better approach when data needs to first be cleaned and organized before loading – so the transformations are more complex and heavy. It is amendable and easier to edit which makes it a better choice for data lakes as well; ETL is too strict to allow that kind of flexibility with data but it can be a good choice in cases when transformations are extremely complex. ELT and data lakes are the ideal choice for the future of cloud data. 

ELT Advantages 

Modern businesses are so data centric that there is no other practical alternative to ELT. The ELT process provides much more benefits to the data-driven industry. 

• Flexibility: In today’s world, due to advancements in technologies like IoT, smart gadgets, & 5G, the amount of data is not an issue when it comes to BI (business intelligent) applications. With ELT, you can import all the data to your data warehouse and have all the raw information at your disposal. This flexibility is a great benefit for analytics can take place within the warehouse without any need for reaching out to source systems constantly.
• Load Times: ELT models reduce load times compared to ETL because they can take advantage of the processing capabilities built into the warehouse architecture, reducing load times and cost. It combines powerful processing with the ease of cloud computing which is important in today’s age.
• Automations: With modern ELT tools, it is easy to monitor and automate data transformations. Moreover, due to the functionality of the tools and improved standards of catching operational errors, IT teams can be alerted of the error immediately, which helps in improving data quality.
• Support for Big Data Systems: With the explosion in internet usage, companies are receiving data in all kinds of formats, structured, semi-structured, and unstructured. These disparate sources can create a huge problem while injecting data into databases. But, with one mapping in ELT, we can combine all formats and convert them into a suitable format to store them in a data warehouse or even a data lake.
• Improves Data Governance: One of the crucial issues faced by the data industry is data accessibility and usability, as any misuse can create a security complication. ELT supports data governance by ensuring quality and structure of data, which improves accessibility and security of data.
• Cuts Down Operational Cost: All the manual work of extracting and loading data is automated in ELT pipelines, it saves a great deal of time and reduces costs.
• Easier Debugging: In ELT, because data is transformed last and therefore most recently, it is easy to debug errors if they ever come up. ETL is too rigid for this flexibility in debugging as the transformations take place before loading as so it makes debugging much more difficult and tedious.

Limitations of ELT

Although ELT provides so many benefits to the business, like all systems, it faces some limitations too. Ignoring these limitations can add to inefficiencies and downtime.

• Extraction Latency: The volume of data being extracted impacts the speed at which data is extracted. The extraction process can be tweaked to improve the frequency of extraction, but due to the trade off between computational resources and extraction frequency, it can hamper system performance. So, data engineers need to design the system keeping in mind the end-to-end application usage.
• Compromises Data Quality: Sometimes, due to extraction and transformation, data is formatted in a way that can create data validation problems. Thus, one needs to take care of data quality by monitoring data at every step.
• Bottleneck of Computational Resources: If we consider high volume, high-velocity operations such as big data systems, then such transformations can be very heavy in terms of I/O and CPU processing. And if the data is broken into batches, then it adds delay to time-to-insight.
• Administration Issues: ELT processes, especially if implemented in an already functioning team using ETL, can require more tools to be adopted for a seamless transition and functioning. This would need more administrative tools and permissions, possibly adding more steps in the process.
• Security: ETL allows for greater security measures as the data transformations take place before loading and so can be encrypted; it’s much harder to encrypt raw data while loading as it will make the transformation process in the warehouse much more difficult. This is also a potential security concern if a hacker penetrates the loading pipeline.

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