Many companies seeking to modernize their data infrastructure often pair cloud computing with Data Automation to achieve common goals: reduce costs and increase agility. While the cloud provides companies with the option to avoid paying for unused storage and computing resources, Data Automation empowers teams to avoid spending time on repetitive hand coding tasks that are no longer relevant.
In addition to these complementary qualities, Data Automation plays a crucial role in reducing the time to value for cloud migration projects. Many companies struggle with the challenge of moving from their legacy systems to the cloud due to the time and cost implications associated with the migration process. Teams are discouraged by the prospect of a lengthy, complex project needing months or potentially years of work by developers working by hand. This creates a Catch 22 situation, where the fear of experiencing downtime during migration is daunting because they are always playing catch-up on projects and dealing with issues arising from their existing infrastructure.
In reality, the migration process doesn't have to be so challenging. A Data Automation tool can significantly reduce the obstacle by automating most of the code generation required for migration, reducing the technical complexities of the project, and ensuring that the infrastructure performs as intended the first time.
While it is possible to migrate to the cloud without an automation tool, hand coding increases project length and stress levels while limiting the number of new data features that can be delivered. Additionally, there is the danger of human errors surfacing later on, as well as the possibility that the entire process may not be adequately documented.
Data automation tools provide an integrated development environment and automated code generation capabilities that allow developers to reduce the time they spend writing code for repetitive processes. By eliminating the need to dedicate most of their time to this lengthy and now obsolete work, developers can concentrate on producing business logic tailored to their company's specific needs. The migration project can be completed using best-practice templates. These templates are fine-tuned based on the experience of hundreds of successful migrations.
An automated data warehouse is fully documented and continues to be maintained and changed rapidly by the automation tool. This means that even last-minute business requests, which were not part of the original plan, can be integrated with minimal impact, including their up- and down-stream. As a result, customers end up with a better data warehouse compared to what they would have obtained through hand coding, in a significantly shorter timeframe, with leaner and more efficient teams. This leads to increased collaboration with the business to ensure that the architecture meets its requirements. It also results in fewer errors to slow the delivery down.
Pairing an automation tool with the scalability and lower costs of the cloud means that data warehouses and data warehouse teams can be agile and run more efficiently. With the ability to do more with less, they can deliver better value to the business. However, moving from on-premises hardware to a public cloud is only the beginning.
An "automation-first" approach presents numerous additional benefits. As developers get used to relying on Data Automation tools to handle repetitive tasks quickly and to a high standard, they typically start to recognize the potential for automating other tasks. Gradually, processes become more streamlined, wasting less time.
Learn more about data warehouse automation and how it can help your data warehousing team unite and fast-track the entire data warehousing lifecycle to deliver data warehouse projects faster.