It may seem that the ETL may not be enough as we need to consider their application in
detail. You need to be aware of their differences along with the business application. Based
on the numbers available nearly 75 % of the databases will be deployed or migrated onto
the cloud platform. What it indicates is that organizations would be confronting a pressing
need for cloud services and the development of a reliant support stack is vital. With all these
things in mind, there is a need to alter the manner by which companies go on to process
data and there is a need to be new approaches to be adopted.
More about ETL
All of us are aware that ETL stands for extract transform and load. It happens to be the
traditional process of data manipulation that is existent in an organization. This was a
popular tool in the period of the 1970s. and still has an important role to play in data-driven
decisions for enterprises and organizations. Coming to ETL is the process where the
movement along with the transformation of data is collected from multiple data sources.
This would be in accordance with business objectives. This date can turn out to be data
The data that enters the data warehouse is already integrated and cleaned. So, at the end
what is at your end is ready-to-use data that would aid in the process of decision–making. In
some cases, smartrr ETL may turn out to be a lengthy process. The process of
transformation could take a considerable amount of time or even weeks to be finalized. This
happens to be a pain point that needs further scope of clarity.
One thing is that since the period of the 1970s a lot of things have gone on to change. The
sources of data have diversified. The business needs to grow and be more articulate when it
comes to decisions. It is fair to say that the technical capabilities have enhanced at a major
level. So ETL is able to cope with data that emerges from numerous sources like log data,
IOT and a lot more things. Now the question is what is going to be big data?
The points of ETL that needs attention in a modern business environment
ETL may not restrict the current needs of your business in several cases
● It has been already mentioned that due to the results of ETL execution, you will
obtain structure and clean data. But you could end up missing loads of unstructured
raw data which may be the type of data that is needed for your data science and
● The moment you execute the ETL, you are already aware of how long you will be
using this data. This is something that is not possible to predict accurately. It is going
to take away the element of research along with data exploration.
● You are going to transform the whole data which is something which you may need
in the future. In fact, you end up spending lots of time and resources on something
that is going to be of help in the coming days. ELT tends to happen at the premises
● The sooner the data grows and tends to be diversified, the heavier it is going to be
on the ETL for them to handle it. Trust me the speed would gradually slow down, and
the data quality would be a cause of concern where wrong results could emerge.
There could be lost opportunities on the revenue part too.
What is the definition of ETL?
ETL stands for extract, transform and load. It is the same set of actions but you end up
following a different order to the traditional form of an ETL. In comparison to an ETL, it is
possible for an ETL to import or export unstructured form of data that is available from
multiple sources. An example in this regard is IOT sensors. They end up taking all the
benefits from the cloud including flexibility and scalability. The data lake that works out to
be a perfect choice when it comes to high volume diverse data sources that accomplish the
● Is able to accomplish big data analytics
● The structured along with the unstructured form of data is being stored in a raw
● Unify the data sources
● To be able to train AI and machine learning
With a cloud data lake, it is going to serve your data teams along with the business since
your organization will be able to understand in detail about structured and unstructured
data. Coming to the main difference that exists between ETL and ELT is that the extraction
of data tends to take place from the sources. It is being loaded in a raw state into a data lake
or a data warehouse. It is possible for you to decide on what you are looking to transform
but you need to be aware that the process of transformation does go on to take place a
tinge faster. Even the data loading needs to take place at a faster pace since there is no
need to wait for the data to load. What it also indicates is that interpretation is going to take
place in a faster manner in ELT than in ETL which is a factor that you need to be aware.
To conclude when it comes to the choice among the platforms, it all boils down to your
requirements. Be aware that both of them are not universal approaches when it comes to
coping up with your challenges, but if you apply them properly then you will be able to
make proper sense of your data. There are other pointers of consideration where one of
them is apt in dealing with smaller data sets whereas the other one is suited for larger data
sets. Take note of the following pointers before you decide to opt for any one of them.