The rapid advances in information technology have given rise to different kinds of cloud innovations. This, in turn, has expanded the horizon of new technologies that are directly or indirectly related to data lakes. The evolution in the data landscape with respect to data lakes has not only incentivized companies but other research organizations to view data as a specific strategic entity. In the present times, the evolution of Information Technology is directly related to the advances in data processing and cloud etl.
We have also seen some of the most dramatic transformations with the evolution of the data landscape. Not only have new kinds of technologies related to data management emerged, but the entire process of ETL has undergone a rapid change. The shift from cloud-native technologies to digital lakes is becoming the most important and notable trend of the 21st century.
The evolution of information technology and aspects of ETL
There is no doubt in the fact that advancements in data processing would change the future of ETL. This would be a huge success for Big Data Analytics in general and data processing in particular. The advances in programming and data architecture would also shape the future of ETL in a considerable way.
In the past, we have seen that organizations have made greater advances while opting for a distributed framework of computing architecture. This has not only enabled powerful computing with the help of various processes but has also improved processing capacity, reliability and has decreased latency. Mention should also be made about analytics technologies that have made ETL widely accessible to business professionals of various industrial sectors.
In the present time, ETL is able to handle voluminous amounts of data and this has directly lead to great management of website traffic.
Impact with the cloud environs
The on-premise execution of cloud ETL processes in the past was limited by the vagaries of physical location with high latency and low integration. With the arrival of cloud-based services, ETL processes started to move from centralized data centers to cloud environs. This movement of ETL processes to the cloud was accompanied by various benefits. The first benefit among them was the prevention of data loss with faster internet speeds. The shift of ETL processes to cloud environments was also used for IoT devices.
In the present times, we have seen the proliferation of IoT-based devices. This has made it necessary for ETL to accommodate more volumes of data from different types of sources. These IoT-related devices would be able to handle better data streaming with help of ETL because data is generated in real-time and devices would prove efficient in responding with lower latency rates. This would be beneficial for business intelligence as actionable insights would be derived from large data set with a lot of ease.
Impact on eCommerce
ETL has had a great impact on the entire domain of e-commerce with the help of Big Data Analytics in general and Cloud Computing in particular. With the help of ETL-based processes, E-Commerce sales have risen from 55 million in 2019 to about 125 million in the first quarter of 2020. These statistics suggest that there would be an exponential increase in e-commerce based startups. Consequently, this would serve as a demand-pull on ETL technologies. In addition to this, we would also see the better handling of data from multiple sources with the help of ETL processes. With an increase in new customers and increased generation of data each day, the process of data integration will also have to be accentuated in a short span of time. This would not be possible without the aid of ETL processes in various domains.
Concluding remarks data lake and ETL
Data lakes are repositories of data that are able to not only store data but also transform and process it in an adequate manner. Data lakes are repositories of three main types of data that include structured, unstructured, and semi-structured prototypes. The shift from data warehouses to data lakes has occurred in a structured and organized manner. In the present times, we are seeing that ETL technologies have adapted to the various dimensions of data lakes. In the future, we might expect the transformation of data to occur in data lakes before it is processed to the final location for the extract, transform and load approach to be carried out.
