If you are familiar with data warehousing and related to computing, then you must have heard about ETL and cloud automation. Well, ETL gained popularity during 1970 when organizations started using multiple data repositories and now there are many ETL tools available in the market. But even after being a part of data warehousing, there are many businesses that unintentionally make ETL implementation mistake and that’s what we are going to address in this blog post.
What is ETL?
ETL basically stands for Extract, Transform and load. These three steps are a part of the data integration process that is used to blend data from different sources. During the ETL process, first, the data is taken from a source (extraction), then converted into a format that can be analyzed (transformed) and finally stored into the data warehouse (loading). You should know that ETL is a recurring activity of data warehousing and that’s why it needs to be well-documented, agile and accurate.
Advantages of using ETL tools
One of the biggest advantages of using ETL tools is improved performance and cloud automation. With the help of ETL tools, you are able to work on data and extract information, do multiple calculations and use it in the best possible way without putting much time and effort. In addition to this, you are also able to apply universal formatting standards to all the data sets as you integrate them. This paves the way for clean and seamless data flow. With universal formatting, you are also able to make your data consistent semantically.
By using ETL tools, you are able to work on an easy to use data warehousing system and it proves to be very beneficial for each and every type of platform. You will be surprised to know that even the ROI in the case of an ETL tool is very high and that’s why so many organizations are now adopting it without a second thought.
Common ETL implementation mistakes to avoid
Here are some of the most common ETL implementation mistakes that you should avoid.
Choosing the wrong hardware or software
Although there are various ETL implementation mistakes choosing the wrong hardware and software is the most common one. Usually, companies buy new ETL tools and start writing codes before even understanding the basic business requirement and if you will do this then things can quickly go haywire.
You should first survey all the major stakeholders, go through everyone’s motive, and only then you can start working on building your solution. It is paramount to choose the ideal tool for every stack so that you can get what you want from ETL implementation. In addition to this, you should also allow yourself to make changes in the ETL process as per the changing business demands without the need for building everything from scratch.
Not planning properly for the volume
This is another big mistake that many companies make when they become totally dependent upon the ETL pipeline and don’t’ plan properly for the volume of data they will have to deal with. One of the most important things to keep in mind during ETL implementation is that; the volume of data goes up, not down. It is true that it is almost near to impossible to accurately predict the exact volume of data you have to deal with but still even a rough estimate can save you from a blunder.
There are always some applications in a business that is prone to a sudden increase in the volume of data. This is why it becomes important for you to always leave some spare bandwidth while choosing a tool so that you can scale up when the volume increases suddenly. If you are processing gigantic amounts of data then there is no chance that the volume will ever go down.
Missing parallelism in data import
Many businesses waste both times and resource in ETL by not understanding how SSIS import works. And because of the lack of knowledge or misunderstanding regarding the SSIS import, people use only one of the server’s many CPUs for the job. It is very necessary to know that DTExec. Is a single-threaded mechanism which means that if you have only one big flat file to import, you will use a single SSIS packaging and it will waste a lot of time, resource and efforts.
In order to avoid this mistake, you should agree with the data vendor to break the data file into n number of equal parts. Then create a piece of code that monitors the folder where the flat files are delivered and run in parallel.
You must be aware of the importance of team effort in an ETL process but still, many people forget the customers. This is why you should ask yourself some basic questions before ETL implementation, for whom you are ETLing the data? What type of information do they need from the ETL process? Whether your ETL implementation will be able to fulfill their demand?
In addition to this, you should also conduct interviews with everyone who is partnering in your company, not just managers. Then you will need to collect all the data in order to understand how to deal with the business data through the ETL process.