If you’re in the same boat as most organizations then your data warehouse is the main center for reporting and analytics. You probably also add massive amounts of structured and unstructured information into your data lake to be used in machine learning and AI applications. It’s time to upgrade to a more modern data platform. With aging infrastructure and rising costs, it is time to think about a cloud-based data platform.
To determine the best solution, you need to consider your organization’s long-term strategy and the present business requirements. A key consideration is the architecture, platform and tools. Are an enterprise-grade data store (EDW) or a cloud-based data lake, best meet your needs? Use extract, transform and loads (ETL) or a scalable source-agnostic layer for integration? Do you prefer to use a cloud service managed by a company or deploy your own data warehouse?
Cost: Assess pricing models, and compare factors like compute and storage to ensure your budget is aligned to your needs. Choose a vendor with a cost structure that supports your short-, midand long-term strategy for data.
Performance: Assess the present and anticipated amount of data and the query complexity in order to select an appropriate system that can support your data-driven initiatives. Select a vendor with a scalable data model, with flexibility to adapt as your business grows.
Programming language support: Ensure that the cloud software for your data warehouse supports your preferred coding languages especially if you intend to use the software for development, testing, or IT projects. Select a vendor that provides data handling services, like data discovery and profiling, as well as data compression and efficient data transmission.
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