If you’re like most organizations your data warehouse serves as the central point for crucial reporting and business analytics. You probably also populate massive amounts structured and unstructured information into your data lake to be used for machine learning and AI applications. It’s time to upgrade to a more modern data platform. With an outdated infrastructure and rising costs, it’s time to consider a cloud-based data platform.
To find the most effective solution, you should consider your company’s long-term strategies and current business requirements. A key consideration is the architecture, platform and tools. Do an enterprise data store (EDW), or a cloud-based data lake, best meet your requirements? Do you need extract transform and load (ETL) tools or a more flexible, source-agnostic layer? Do you prefer to use a cloud service managed by a company or deploy your own data warehouse?
Cost: Assess pricing models and compare variables such as compute and storage to ensure your budget is aligned with your requirements. Select a vendor that has a cost structure that supports your short, midand long-term strategy for data.
Performance: Consider the volume of data currently and in the future and query complexity to select a system that can assist your data-driven initiatives. Select a vendor that offers an adaptable data model that can adapt to the growth of your business.
Support for programming languages: Ensure that the cloud data warehouse you choose is compatible with your preferred programming language, particularly if you plan to use the product for IT projects, development, testing or for any other purpose. Choose a provider that offers data handling solutions, such as data profiling, discovery, data compression and efficient data transmission.