By Vikram Labhe It is a truism at this point to talk of the centrality of data for organisations. According to IDC, the global datasphere will rise at a compound annual growth rate (CAGR) of 23% between 2020-2025, highlighting the importance of responding to the surge in storage demand. For businesses to leverage data insights and drive growth, they must coordinate the dependencies and execute the different tasks on their data journey in the desired order, all while ensuring minimal impact from potential errors. Whether an organisation favours extract, transform, load (ETL) or extract, load, transform (ELT) will depend on their specific needs. Orchestration is fundamental for modern data processes, but for many businesses a modern data stack makes specific orchestration tools redundant.

The traditional data transformation procedure of extract, transform and load (ETL) is rapidly being turned on its head in a modern twist enabled by cloud technologies. The Cloud's lower costs, its flexibility and scalability, and the huge processing capability of cloud data warehouses, have driven a major change: the ability to load all data into the cloud, before transforming it. This trend means that ETL itself has been transformed--into extract, load and transform, or ELT. ELT offers several advantages, including retention of data granularity, reduced need for expensive software engineers and significantly reduced project turnaround times. Data is vital for organizations, who use it to understand their customers, identify new opportunities and support decision-makers with mission-critical and up-to-date information.