A New Need for AWS ETL Tools
As many companies transition their analytics initiatives to cloud platforms, Amazon Web Services (AWS) is a top pick. Offloading data management expenses are a big reason why companies turn to the cloud; as such, many companies will also choose to leverage Amazon Redshift, a fully managed data warehouse, on top of AWS.
Migrating to AWS and AWS Redshift is a multistep process that is typically part of a larger enterprise transformation effort. One of the many questions that inevitably surface during this transformational period is how to do AWS ETL (Extract, Transform, Load) or more specifically, AWS Redshift ETL.
Under traditional data warehousing, ETL was often handled by a small technical team that would standardize and cleanse data before it was made available for business use. Certainly, a like-minded approach to AWS ETL exists today; to move data in and out of Redshift, companies can build a custom Redshift ETL pipeline or leverage an existing AWS ETL service.
However, the downside to these AWS ETL approaches is that they are limiting given their technical nature. Only IT can do AWS ETL. That means that business users are
Inevitably left waiting, if not for days then for weeks, to gain access to the data they need. On top of that, IT rarely has the deep business understanding needed to identify the insights and additional questions that can help to reshape the data during the AWS ETL process in new and useful ways.
The Other Piece of the AWS ETL Puzzle
Today’s data-driven organizations are not only considering how to do AWS ETL, but who should do this work. As data projects scale across the organization, reserving AWS ETL for IT will prove an even bigger bottleneck. Moving to the cloud for increased agility and speed should come with new thinking around how to do AWS ETL. Though certain organizations will still require AWS ETL, some are choosing to supplement or entirely replace AWS ETL with data preparation solutions.
Modern data preparation solutions combine visualization, machine learning, and human-computer interaction to appeal to business users—not IT. They were built to handle complex and diverse data, instead of simply structured data. Most importantly, they allow for a more flexible approach to AWS ETL. Inevitably, how certain data needs to be changed will depend on the context of its analytics project. The steps to prepare data for one use case may be very different from what is required for another. Unlike AWS ETL, data preparation solutions put the power of data transformation in the hands of business users.
How Designer Cloud Can Help
Alteryx Designer Cloud has been universally recognized as the leader in data preparation. Not only does its intuitive interface guided by machine learning allow business analysts to prepare data themselves in record time, but it also has a deep partnership with AWS. With Designer Cloud, teams can prepare data for Amazon Redshift at scale, schedule workflows and share work with colleagues within a managed cloud platform. Schedule a Demo with Alteryx for free today to see how it can fulfill your AWS ETL requirements.