Most companies already run their data assets through ETL processes and increasingly these processes are moving to cloud environments. Nevertheless, before committing to an ETL cloud service, there are certain considerations we should take into account.
ETL processes have become a staple of business operations. Large organisations have long used data extraction, transformation and loading to manage their data assets and consolidate them in order to obtain valuable insights and information through data analytics.
The ETL process also promotes other data-related good practices such as data quality, data integration, data security, etc. Also, as we have previously explained in this blog, the ETL process can promote the automation of data warehouses.
Probably because of its many competitive advantages, small and medium-sized companies are increasingly opting for this process. We can therefore say that ETL is now part of the business DNA of all kinds of companies, whether they are small, medium or large.
Beyond its expansion, another trend related to ETL is its execution in cloud environments. More and more organisations are deciding to carry out ETL processes in the cloud rather than on local servers. In fact, IDG reported in 2020 that 81% of companies already have at least one application or part of their IT infrastructure in the cloud and 92% have at least part of their IT environments in the cloud.
This can be explained by the general and global propensity towards digital environments, which entails businesses to have the vast majority of their data, applications, tools and software stored in the cloud.
On the other hand, another of the most cutting-edge trends related to the ETL process in the cloud is the transformation from ETL to ELT, which swaps the order of transforming and loading —Extract, Load and Transform—. The emergence of ELT cannot be understood without the progressive expansion of cloud data warehouses that offer unlimited data storage, allow a dynamic scalability of the number of nodes, support parallel queries and separate storage from computation. These and many other advantages make cloud data repositories the best option for performing data transformations without compromising query performance.
Differences between on-premises ETL and ETL Cloud Service
In on-premise ETL processes data is extracted and loaded into traditional data warehouses also known as local data warehousing. That is, on physical servers usually located within the company.
ETL cloud services perform the exact same function as on-premise ETL, but both the data sources and the data warehouse are digitised and stored in the cloud.
The main difference between the two is that while they essentially perform the same tasks —extract, transform and load— the way they are carried out differs depending on the environment.
In the cloud, the process can be carried out through shared computing clusters spread around the world that operate as individual entities. Computing processes are distributed across cloud environments through workspaces such as Data Factory, which achieve better connectivity between data sources and enable graphical management of data flow through interfaces that link to both the source and destination sources of data.
In addition, ETL cloud services solve many of the problems and limitations of traditional ETL processes, such as the high cost of physical servers and data warehouses and their maintenance, or the possible loss of all information in the event of technical failure, theft or natural disaster. The cloud also removes maintenance, updates and bug fixes from the process.
Without a doubt, the most significant advantage of ETL in the cloud is greater speed. Companies operating on local servers are at a disadvantage in terms of data competency because they cannot match the speed and agility of cloud services.
On the other hand, ETL cloud services provide greater scalability. That is, by not requiring hardware or installations, companies have the ability to automatically expand their resources when they need to, without requiring large amounts of money and time. Furthermore, in the cloud environment, organisations only pay for the space and processing they need, whereas in local servers, the ability to adapt to the on real time needs is virtually impossible to achieve.
In short, ETL tools are now essential in the business world and everything indicates that cloud services and environments are the future of this process.