In the age of big data, information processing has become the lifeline of various business operations. For processing data and managing information across various networks, cloud processes have been the unanimous solution to rely on. Given the present competitive landscape, these processes have gained much more importance than ever before. Let’s first start with a brief overview of the extract, transform and load processes.
An abstract of ETL
For processing voluminous amounts of information, ETL is the most preferred option. As the name suggests, the entire process of ETL is divided into 3 stages. The first stage is called the extraction stage. In the extraction stage, unstructured data is mined out from a large number of sources like database applications and security hardware. The voluminous streams of data are collected in real-time at this stage. The second stage is the transformation stage. It is in this stage that the large volumes of data are converted into structured data sets and any sort of duplicate data is eliminated. The validation of the collected data sets takes place and these sets are later standardized for further analysis. In the final loading stage, the processed data is deposited into specific locations. These locations may be network repositories or other kinds of analysis tools. In this way, the stage is set for precise analysis that acts as a boon for business intelligence.
Few glimpses of the traditional approach
Before the large network of cloud environs came into being, different kinds of ETL processes were handled locally. This not only led to a burden on the available infrastructure but also limited the scope of research and development in this field. This is because data had to be analyzed through a system of cables and advanced algorithms needed to be deployed to extract huge data sets. After this, the data needed to be standardized before it could be fed into various databases. From here, the task of manipulation of data and related information started. This approach had many foundational issues and other limitations. In addition to this, the cost incurred on the entire process of ETL limited the development and investment in this field. As the volume of data started to expand exponentially, migration to the cloud environment became the only suitable option.
A prospective cloud journey
Migration to cloud environments became inevitable when the storage capacity of the local site reached its limits. With its rapid processing speed, the entire process of extraction, transformation, and loading was capitalized and cloud etl started to assume significant prominence. As per a report by IDG, more than 75 percent of the businesses would shift to cloud environs partially or fully by the end of 2022.
In the present time, ETL processes successfully function within the cloud environment in consonance with other technologies like the development of applications. The process through which ETL was carried out has undergone a paradigm shift. Apache Hadoop has laid the roadmap in the Hadoop Certification through which ETL processes have been further improvised. The remote extraction of data located across various geographic sources and their successful transformation via the cloud computing network is a point in the case.
By utilizing the power of distributed computing clusters, ETL processes have gained significant importance in the IT industry. Be it the massive computational tasks or the processing of logical entities at the individual level, cloud ETL has proved to be a unanimous solution to all our data woes.
As far as the process of analytics is concerned, the cloud processes have significantly streamlined them. New tools and techniques have been devised which can mine data from the remotest of the locations and process them on the cloud. This has proved to be a game-changer for the entire industry as business intelligence has been significantly transformed to meet the individual needs of a company. This has incentivized other companies to manage all their data management problems with the help of cloud-based processes. It needs to be noted at this point in time cloud-based ETL processes have also been at the center of resolving data integration challenges that companies have faced in the past.
It is high time to integrate ETL processes with the cloud given the amount of data that business organizations are generating today. Ranging from the extraction of data to its cleansing, validation, and processing, cloud ETL cannot be ignored by businesses for long enough. In one word, cloud ETL is the ultimate panacea to all the data woes of a modern business that wants to make a mark in industry 4.0.