If you're like the majority of organizations the data warehouse is the central center for reporting and analytics. It is also likely that you are putting massive quantities of unstructured and structured data into your data lake for machine learning and artificial intelligence (AI) applications. With aging infrastructure, rising costs, and increasing demand, it's the right time to consider upgrading to a modern cloud-based data platform.
You must take into account the current needs of your business as well as the long-term strategy when deciding the right solution. A key consideration is the architecture, platform and tools. Do an enterprise data store (EDW), or a data lake that is cloud-based best meet your requirements? Do you need extract transform and load (ETL) tools or an easier to use source-agnostic integration layer? Do you want to build your own cloud-based data warehouse or utilize an managed service?
Cost Comparison of pricing models and analyze factors like storage and compute to ensure that your budget is compatible with your requirements. Choose a provider with an expense structure that fits your short, midand long-term data strategy.
Performance: Examine the present and anticipated amount of data and the query complexity to determine if you can select a system capable of supporting your data-driven initiatives. Select a vendor with the ability to scale data models, with flexibility to adapt as your business grows.
Support for programming languages: Make sure that the cloud data warehouse you select is compatible restructuring business software with your preferred programming language, especially if intend to use the product for IT projects testing, development, or for any other purpose. Select a vendor that offers data handling services including data profiling and discovery, data compression and efficient data transmission.