You are now in the process of constructing a new data solution for your startup, and you require an ETL tool in order to make the process of flinging data more manageable. You are currently faced with the challenge of selecting one path out of fifteen unique options that are available to you.
How is it feasible for you to limit the number of tools you use from fifteen to just one, especially considering that making this selection is only a small fraction of your job and you can’t afford to spend a lot of time playing around with it?
Things to take into account prior to deciding on an ETL tool –
The question that needs to be answered is, in spite of the fact that different instruments have different names, how do you choose amongst them?
The manufacturers of ETL tools take great pleasure in presenting you with an absurdly lengthy list of all of the reasons why their products are superior to those of their competitors. But if we’re being completely straightforward, there are really only six things you need to be concerned about:
1. A simple and natural procedure –
ETL tools are normally highly strong; yet, many ETL applications give the impression that they were developed by extremely nerdy data engineers just for the use of extremely nerdy data engineers. This is not the case.
When analysing the ease of use of an ETL tool, you should make sure that you have planned out a number of different user scenarios that cover the key issues that are generated by your data sources. The drag-and-drop method is quite useful, but it has the potential to rapidly become extremely disorganised if it was not designed to support activities such as putting together the components of a complex data model. Although the drag-and-drop method is quite useful, it can quickly become extremely disorganised.
2. Maintenance –
When you are in a hurry to get a new data solution up and running, it is easy to forget how essential maintenance will be to the achievement of your goals for the solution’s success. In light of this, make it a point to ask questions such as the following:
What amount of experience will you or your team need to possess to guarantee that the ETL system will continue to function normally?
If the ETL tool that you use does not have a built-in integration with the data source that you use, how difficult would it be to keep up with the changes that occur over time in the data model of a data source?
3. Support –
No matter how straightforward the operation of a tool may be, there will inevitably come a time when you will need some kind of assistance. And the fact that the tool you chose had a slightly better feature set isn’t going to matter to your users if they are unable to complete critical work. This is because the tool you chose had a slightly better feature set. In this particular scenario, the marginally improved feature set won’t make a bit of a difference at all.
Using an ETL tool to establish an integration with a data source is significantly less labour demanding than writing a huge amount of code to do the same operation. This is due to the fact that writing code takes significantly more time.
In today’s world, certain ETL technologies come equipped with bespoke integrations for the most popular data sources. When using an ETL tool that has specific integrations for the majority of the data sources that you employ, the amount of time that it takes to get your data warehouse up and running can be cut down by a significant amount. If you have the correct integrations, it may only take a few hours or even minutes to get a significant portion of your data ready to use.
In many instances, a single ETL solution will not have native integrations to all of your data sources.
Integrate with many other ETL solutions if they already own the connector you require, or
Connect to an S3 bucket that can serve as a go-between between the primary source of data and your pinterest etl tool, or connect directly to the S3 bucket itself.
Ingest data from a pipeline that was manually coded (although they wouldn’t recommend constructing all of your ETL pipelines yourself, if you’ve just got one to handle, it can be a reasonable choice).
4. Cost –
When making a selection among a number of different kinds of apparatus, the price is typically the factor that matters the most. Some questions you’ll need to consider:
- What exactly are the yearly dues that are expected of you?
Are you subject to additional costs if the amount of data included inside your data sources increases respectively, as well as the number of data sources that you have? If so, by exactly how much would that be?
- Is the pricing model characterized by a recurring trend of some kind?
When comparing the costs of the various ETL solutions, you may find that it is impossible to be objective and make comparisons based on the same criteria throughout the process. That’s okay. When you are making your initial pick of potential applicants, all you need is a very basic estimate of how much money it will cost you to hire each one of them.
And when you are going to make your ultimate decision? Whether you aren’t certain about the price difference, you might want to talk to some of the people you know to find out if anyone else uses it. You should also consider running a small proof of concept to obtain a better understanding of how the pricing model functions in the context of your specific situation.
Conclusion –
Do you want to believe that? Try it out for yourself—it won’t cost you a thing, and you won’t even need to enter your credit card information. You can start syncing data within minutes of setting up your ETL pipeline, and you won’t have to write a single line of code to do so.
