The 5 Key Differences Between Traditional and Cloud Native ETL Processes

5 Key Distinctions Between Traditional and Cloud-Native ETL Processes

Here are the top five differences between traditional and cloud-native platforms:

• Difference #1 - Data Types
• Difference #2 - Adaptability
• Difference #3 - Security
• Difference #4 - Cost
• Difference #5 - Time

1. Designed to Handle Different Data Types

Within any enterprise’s EDW, there are likely thousands of pieces of unstructured data among lines of code. This unstructured data is a particular inconvenience when an enterprise is using the traditional ETL process. Traditionally, hired developers must have the expertise to create custom solutions for moving this unstructured data to its desired destination.

Needless to say, this integration is time-consuming, rigorous work, and it prolongs the time it takes to load all your data pipelines into one source. You are essentially doing the work manually, which is a major cost in more ways than one.

A cloud native application handles this unstructured data differently. Cloud-native ETLs handle structured and unstructured data from different sources. Therefore, it is less likely that you will encounter the traditional difficulties that you might associate with unstructured data, saving time and money, and far fewer things can go wrong.

2. Adaptability

Next up, we have adaptability. To put it simply, traditional ETLs are not very flexible.

Remember, with traditional ETL, you are relying on teams of developers to write each line of code themselves, which means you’re limited to the knowledge of your developers, and what's made available through the ETL vendor's platform. As a result, the ETLs can only handle certain types of data sources or changes in code and location. They can only handle what they were manually designed to handle.

Tasking traditional ETL to extract and transform code from a data storage source they’re not familiar with is simply not possible. And the only option you’re left with is hiring a data scientist who has the expertise to write an ETL solution capable of executing this task, which costs more money and more time while also adding more work.

On the other hand, cloud-native ETLs have built-in functionality to integrate data from all sources to a broad range of targets. They can integrate data whether you’re in the cloud or using an on-prem legacy system. Additionally, cloud-native ETLs have simple GUIs so that even non-developers can create ETL pipelines. They have higher functionality and much higher usability. Once again, you save a lot of time which means you also save a lot of money.

3. Security

Security is a big concern in all areas of data, so you want to keep your data secure.

With traditional ETLs, it’s up to you to secure your data. If you’re using an on-prem legacy system, then all of your data is stored in one place. One breach and someone can access all your data! You must create the proper security architecture yourself, which often involves a complicated web of tools,  protocols and staff to build a framework to ensure data is secured at rest, and in transit. 

For cloud-native ETL tools, the vendor has its security network in place that integrates seamlessly with the cloud data storage, so the onus is on them, not you. You won’t need to source out external software, and you won’t be responsible for securing your data, although many enterprises still implement measures on their end. Another great by-product of using a managed service in the cloud!

4. Cost

On-prem legacy systems are expensive to maintain. Regular maintenance is expensive, and that’s before even factoring in any regular upgrades (let alone the licensing costs!). On top of this, you have to hire a team of developers to write the ETL code themselves. This process is not very efficient, nor does it scale, which makes it very costly.

Cloud-native ETL platforms are more cost efficient as they typically can run on your cloud platform's processing environment, saving you money from having to run an on-premise server, which comes with subsequent licensing costs. Further, the ease of use of the cloud-native ETL platforms make it faster to write code, which in turns saves you money on resourcing costs. 

5. Time

As they say, time is money, and traditional ETLs take up a lot of time. As we’ve examined above, with so much work that needs to be done manually by developers, it can take months or years to successfully implement new ETL pipelines. If you hire a team of developers, they take a long time to understand your code before they can develop it, not to mention all the potential confusion that may arise when developers apply their varied backgrounds to a common problem.

Simply put, maintaining, upgrading, and developing ETL pipelines is incredibly time-consuming.

But once again, cloud-native ETL tools offer the solution. Cloud-native ETL tools scale much more easily because they use all the benefits of scalability, elasticity, and speed, including all the performance improvements mentioned above. Here is yet another area where you can capture efficiencies, which will translate into cost savings.

Putting it All Together

This handy chart compares the features of traditional ETL processes vs. cloud-native ETL.

Thinking of migrating your legacy ETLs to the cloud? Explore our ETL solutions page to learn more.