ETL vs. ELT and the Benefits of Data Transformation in the Cloud
The prizefight between ETL vs. ELT rages on. This post highlights key differences in the two data transformation processes and provides three reasons or benefits to working in the cloud. What’s the difference between ETL and ELT? Read on to find out.
Enterprises are embracing digital transformation and moving as quickly as their strategies allow. As they continue toward the digital promised land, they are realizing that legacy, on-prem infrastructures are not built to withstand the journey. While 89 percent of enterprises have plans to adopt or have already adopted a digital-first business strategy, a third of organizations say that the need to replace legacy systems is one of the biggest obstacles.
Digital transformation – the use of technology to create new value in business models, customer experiences and operations – is driven by data and insights. When incorporating insights into business strategy and planning, organizations are seeing impressive “return on insights” and growing at an average of more than 30 percent annually. But legacy systems and on-prem data warehouses are inhibiting businesses from gleaning insights fast enough to stay competitive.
Enterprises are great at collecting data, but many struggle to join together siloed data from different sources; to add business logic, and embellish metrics to take raw “captured” data and turn it into something useful. Organizations need to transform data in order to make it useful and this needs to happen at scale, inside of an already complex IT environment. Merely ingesting data into a cloud data warehouse – or rather a cloud data warehouse engine – does not necessarily make data useful or usable. It is the transformation of that data; taking it from a raw, normalized state to data that is denormalized and ready for analysis. Transforming data is challenging but also important. Once it’s done, companies can benefit from an actual data warehouse model running on top of the cloud data warehouse engine.
As it stands, many enterprises do not have the right technologies to transform data into an analytics-ready format. The data management strategy of large enterprises varies wildly. Some remain completely on-prem, some are in the cloud (or multiple clouds), and many fall somewhere in between, with a hybrid of both. In fact, 77 percent of enterprises have at least one application or a portion of their enterprise computing infrastructure in the cloud. For companies looking to be insights-driven, cloud data warehouses like Snowflake, Amazon Redshift, Google BigQuery and Azure Synapse are the practical choice over their on-prem counterparts.
As companies transition from on-prem to the cloud, they can also move toward a better data transformation architecture using ELT rather than ETL. ETL is the process by which you extract data from a source or multiple sources, transform it with an ETL engine, and then load it into its permanent home, usually a data warehouse. In ELT, you extract data from the source, load it unchanged into a target platform (database, data warehouse, data store), and then transform the data inside the warehouse itself – something only practical in the cloud.
With ELT, businesses gain better performance and cost-savings because they can leverage the cloud data warehouse to transform data. With ELT, data professionals work directly and natively on data inside the warehouse for higher fidelity, faster productivity, increased scalability, and fewer errors. Longer batch processes and slow development are essentially eliminated. The infrastructure and architecture are far simpler and can be scaled up and down as needed.
