Introduction to ETLs as an Enabler for Data Hubs and Warehouses
Data forms the pillar of the foundation for any business in today’s world. It wouldn’t be incorrect to say that the entire outlook and decision-making of an enterprise takes place with its foot in data.
There was a time when decision making was largely guided by the intuition of key stakeholders in an enterprise. Today, these have drastically transformed, and businesses have moved towards data-backed strategy formation. Eventually, this is leading to data-backed decision making.
Enterprises today are moving towards establishing their presence on multiple fronts at the same time. It is helping them reach out to more customers and provide them with a superior experience. Alternatively, this is also providing customers with an opportunity to interact with businesses at the choice of the platform they like.
ETLs for Enterprises in 2020
While providing an experience to the customer sounds like something every enterprise should do, it often takes more than a few resources and ends up eating a business’ time. For this reason, many small and medium businesses fault o have their presence on multiple platforms despite them being such big resources.
The key shortcoming in the process lies in an intelligent tool that can help businesses get out on multiple platforms and make it effortless. At this point, it is natural to think about how this process can take place within an enterprise. The simplest solution to this problem is an ETL software.
More and more enterprises are moving from on-premise data centres to cloud-based data warehouses. While this is a good thing for enterprises now that the burden of infrastructure has reduced significantly, it is also helping them deploy more proactive tools for business intelligence.
With ETL in the picture, the data pipeline of business gets pretty much sorted out. They are not only able to enjoy unified access to the data but, also the data is present safely on the cloud data warehouse.
For an enterprise data warehouse and an ETL go hand in hand. It wouldn’t be incorrect to say that the ETL is also an enabler for data hubs and warehouses. If you don’t agree with this, we need to break down the process of ETL into its very bits and pieces.
ETL as you know stands for Extract, Transform and Load. It is a typical process of bringing data from different sources or platforms under one shelter for enterprises. In other words, it is the unification of different types of data under one platform. It helps businesses see across their entire processes from a holistic point of view.
Extraction is one of the most fundamental tasks of ETL software. It is important because the reality is that most businesses don’t rely on any type of software or data source.
They have a presence all over the web on different types of platforms. Moreover, these platforms have their kind of data. To make everything work, business intelligence relies heavily on monitoring data from all sources.
The point is before all the can be gathered on one single platform or unified, they have to extract from their source. In the extraction part, the data is extracted from its source, whether it is present in the structured or unstructured form. Some of the sources from where the data can extract are as follows:
- Marketing and sales applications
- Cloud and on-premises infrastructure
- Mobile and web-based applications
- Customer relationship management tools
- Data storage platforms,
- Analytics tools
- Legacy databases
While the extraction of data from all these sources is a cumbersome manual task, it is done in a few easy steps with ETL. In other words, ETL makes the princess of extraction hassle-free and automated.
The next step to be undertaken in the process of enabling data warehouses and data lakes via ETLs is transformation. Once the data extracted, it is then transformed. The reason why transformation takes place is that data from different sources are in different formats. For them to view as a holistic one type of entity, they need to be present in one format.
Therefore, in the transformation step, rules and regulations are applied to the data. In other words, data is converted to a standard format using transformation. The transformation step of the ETL data integration process also ensures that the data, when presented in the final format is fit for reporting.
Transformation in itself is composed of several small detailed processes such as cleansing, standardization, deduplication, verification, sorting along with other tasks. The main aim is to take the extracted data and make it presentable. Any redundancies are removed, and data cleansing takes place. The entire result of ETL relies heavily on the transformation site.
Finally, once the data is extracted and transformed, it is loaded into the data warehouse or data lakes. These can be both on-premise as well as cloud-based. But since the demand for cloud infrastructure is increasing day by day, most ETL software act as an enabler for data warehouses.
The loading of the data happens in two steps- they can either be loaded all at once or gradually. While the former is known as the full loading, the latter is better known as incremental loading. Full data loading can be difficult to maintain at times, which is why there is a need for incremental loading.
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Smart changes in business architecture can allow for an efficient business intelligence outcome. With ETL in the picture, even small businesses get the opportunity to stand out and compete in the cut-throat market competition. Even though investment in a data warehouse might seem an additional expense for an enterprise, it is imperative to remember that you only pay for what you use. Both cloud and ETL make business intelligence into an efficient business process leading to direct benefits and profits.
James Warner is working with NexSoftsys as a software developer. It is working on various programming languages like Java, ASP.NET, Big Data, and Python. It has a team of highly experienced developers, giving one-stop business IT solutions to the world.