An overview of ETL and why it is important for decision-making purposes

Let us take a commercial scenario into account. We are working for a commercial software company and the customer care department has gone on to receive feedback from customers A and customer B. Both of them would like to incorporate a specific feature into the platform. With limited resources at our peril, the key is to choose which one of them first.

Obviously to figure out your priorities you would require more data at your end. Check out the point of the sales system and it would be better if you go on to compile data from relevant points in an organization. The answer to all these questions will be determined by the priority of the organization, but are you aware that a data-centric approach could frame the decisions to follow a strategic approach?

If there is no centralized source to correlate all these factors then the decision-making goes on to make it really difficult. For this reason, organizations have turned over to Bing Ads ETL which provides the much-needed context to understand the data in a better way. It is supported by the statistics that they go on to value the most.

The definition of ETL

ETL stands for extract, transform and load and it works out to be a standard model for organizations who are looking to integrate data from multiple sources into a central data respiratory system. Some of the main benefits of ETL are

  • Quality- it is known to improve data quality where it goes on to transform data from numerous sources. Hence it is able to comply with the internal along with external requirements of the business. Such a form of consolidation provides a historical context, as the relevant data is stored for recovery. Therefore, during the process of decision-making, it goes on to eradicate the blind spots.
  • Consistency- ETL is known to simplify the analysis process, where the data is being transformed into a universal standard. Hence it is known to improve the accuracy of the calculations, along with predictions where all the data is archived and searchable.
  • Speed- with ETL the speed of decision-making is enhanced, where no longer there is a need to query multiple data sources. The response time may vary to arrive at a precise result on all counts.

The process of ETL

Pretty much as the name sounds the process of ETL is split into three significant stages as follows


It is obvious for a business to rely on multiple sources of data from numerous sources. Let us cast our minds to customer A and customer B along with the data points that are being pulled on each customer. Before you go on to analyze the data it is better that you locate them first and move them to a central warehouse. This turns out to be the extract process of ETL.

A point worth mentioning is that data may emerge from numerous sources and it is not like a traditional data module. It is possible to extract raw data from unstructured sources, like emails, documents or storage. The process of extraction is all about where the disparate data is located and copied. The formats so obtained can be evaluated based on the specific needs of the business.


By now the process of data collection is over and the processing needs to take place. You need to be aware that the information is coming from different systems and the sources are also different there is a need to maintain data integrity so that it is curable. There is a need to follow pre-determined rules as the process of transformation is expected to sort out the data. Even the loading of data is ready so that it reaches out to the next phase.


In this process, the transformed data is loaded into the database. A couple of methods of loading the data are available, which is the incremental loading and full loading. When it is full loading, you go on to collect every data point during the extract along with the transformation process as it churns into unique points in the database. A user-friendly approach is an incremental model where you compare the incoming data with your existing data and it requires smaller data warehouses where less maintenance is necessary.

The process of loading may occur at different time intervals. Both processes could take place at scheduled time intervals. Some of the organizations have gone on to synchronize the process so that the new data is subject to ETL so that it is recorded at origin and goes on to provide real-time visibility. But among the various data sources, it may require a series of data integrations and the data store may not be subject to be used in all cases.

The traditional ETL model and the cloud ETL model

When you compare the traditional ETL model the data is extracted from numerous sources. It is validated into the various sources and then loaded into a data warehouse. What it means is that the data needs to be structured in such a way that it aligns with all the requirements of the business. An issue with this form of method is that data processing does go on to occur before it reaches out to the final customer. If the data is large or complex it can go on to ensure that the delivery takes further time.

Coming to the cloud-centric ETL model the entire process of data validation occurs in such a manner that it takes into consideration the needs of a modern-day enterprise. There is a wide spread acceptance of this model and a lot of companies are resorting to the use of this model at an extensive level. It is going to lead to a faster delivery time and the response time would be at a smaller scale. Most of the organizations have gone on to cash in on the benefit of such an approach.

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