If you’re anything like most organizations then your data warehouse is the central hub for reporting and business analytics. You probably also add massive amounts of structured and unstructured data into your data lake, which can be used for machine learning and AI use cases. With the aging infrastructure, increasing costs, and increasing demand, it’s time to think about upgrading to a modern cloud data platform.
To find the ideal solution, you need to consider your company’s long-term strategy as well as the needs of your business today. The platform, architecture and tool set are crucial aspects to consider. What kind of enterprise data warehouse (EDW) or a cloud data lake best suit your needs? Should you choose to use extract transform and load (ETL) tools or a more flexible source-agnostic integration layer? Do you prefer to use a cloud service managed by a company or even build your own data warehouse?
Cost Pricing: Review pricing models and other factors such as compute and storage to ensure that your budget is compatible with your requirements. Select a vendor that has view it now an expense structure that fits your short, midand long-term data strategy.
Performance: Examine current and projected data volume and query complexity to choose the right system to support your data-driven initiatives. Select a vendor that offers a flexible data model that can be adapted to your business’s growth.
Programming language support: Make sure that the cloud software for your data warehouse will work with your preferred coding language, especially if you plan to use the software for testing, development or IT projects. Choose a provider that offers data handling services, such as data profiling, discovery, data compression, and efficient data transmission.
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