The numbers are staggering: we produce 2.5 quintillion bytes of data across the globe daily. And as our society leans closer and closer to operating off a digital landscape, why wouldn't you want to get in on this action?
But with loads of data comes the need to have the best way to model data, right? So how do you know which model is right for you?
Well, you're in luck! We're here to give you a definitive guide on ELT and ETL tools and their pros and cons for giving you the best data modeling! Are you ready?
Then let's get into it.
What Are ETL and ELT?
ETL stands for extract, transform, load. It has been the old standby for data building for a long time, since the 1970s. The "extract" part of the acronym refers to the data withdrawing out of one or many central sources.
From there, the data transforms into a giant model that puts all the data in the same format (to allow ease of "reading"). After that, the data loads into a single database. These databases can range from a data warehouse to a data lake: the only qualifier is that it has to be somewhere that all the data can gather.
ELT is no more than a simple flip on the formula, ordering the steps extract, load, transform. As a result, data goes inside a database after coming out of a central source and converts inside that database into a data model. More memory demand will go on the database, but the process becomes much more streamlined as a result.
Why ELT for Businesses?
One of the reasons ELT is the top choice for a lot of businesses is their automation. ELT makes the arduous task of coding large amounts of data by hand unnecessary, reducing both the time spent on it and the need to pay someone to do it.
ELT also helps make the data easier to understand. By compiling it into a central model, ELT makes large chunks of data more digestible and presents them in a manner that is easier to "read" by humans.
Finally, ELT keeps the memory demand on the servers of businesses light. This not only gives them more room to store other essential programs or files but lowers the need to buy large, expensive data storage devices.
The Traditional ETL Process
If you look for the most common way companies use ETL, it will likely be a system that moves data in scheduled batches from point to point. The schedule helped to work around chunks of time where the computers were operating at a lower capacity.
The primary advantage of this method is that because of its time-tested manner, it's reliable: we've figured out how to get this method as tight as possible. Some also see the localized manner of this method as a security advantage (since it's harder to hack it remotely).
But if you're trying to send a mass storm of different forms of data, ETL this way might not be right for you. So what can you do?
The Modern ETL Process: Modern vs Traditional
Enter the modern ETL process. This bad boy changes the database from local storage to the cloud and monitors the process in real-time while also making changes where needed.
Modern-day ETL takes some of the best parts of ELT and mixes it in. You can transfer your data right to the database and choose which edits you want to make, tailoring the data to your desire. Modern ETL tools and ELT tools are also built to handle a wider variety of data types, which is a bonus in the age of constantly evolving tech.
But as in all things, there are drawbacks to traditional and modern ETL: Because of traditional ETL's manual nature, it's less flexible to on-the-fly changes and debugging poses a challenge. Traditional ETL's speed is also limited by the amount of data you're transporting: the bigger the load, the slower it goes.
Traditional ETL also tends to prefer data that has some relation to each other.
Modern ETL does not have the speed limitations of its traditional brother and will keep pace regardless of the size of the data load it has to work with. It hits a different hamstring though: the number of hoops it jumps through. Because modern ETL has to revisit data multiple times, this can add to slower overall processing time.
Modern's drawbacks stem from its relative youth. It's still an imperfect science. Also, security is moved to the responsibility of whoever's providing you the cloud service, so choose your cloud provider wisely. ELT proves to be more flexible than modern and traditional ETL, making debugging easier and more time-efficient.
ELT and ETL Tools
So now that you know what ETL and ELT are, how do you know which type of tools to use for the goal your business is trying to achieve?
Types and Benefits
There are two primary types of tools for these processes. They are incumbent batch and real-time tools. There are various process that can roll up to these two primary tool types.
● Incumbent batch tools work best with the traditional style of ETL, transferring data in batches during off-hours. Within incumbent batch tools, you may find Cloud-native (cloud-based) tools built to work with modern-style ETL. You may also find Open-source tools where you can see the code upfront (and in some cases, you can even make edits).
● Real-time or Streaming tools provide the ability to model and make transformations to the data in a way that will allow real-time results, keeping the accessible data fresh.
Each type of tool works to handle a specific system and each system has it's own variables that should be considered on a use-case basis. For example, if your database has finite resources and those resources are needed for BI analysis during the day, you may want to consider an incumbent batch system that only uses database resources during off hours. Do your research and stick to the tool that best serves your exact business intelligence needs.
Now It's Your Turn
You now know all about ELT and ETL tools and their various pros and cons! And, you know why ELT is the preferred method of our data engineers and strategists at Zuar. Its modern features make it highly adaptable in an always-changing world of technology.
If you have more questions about ETL and the different ways you can transfer and organize your data, check out more of the posts on our blog!
So until next time, remember: stay ahead of the data game. You'll thank yourself in the long run; trust us.
Take a look at more of Zuar’s capabilities here.