Choosing a Cloud Data Warehouse

If you’re like many organizations, your data warehouse is an essential hub for business analytics and reporting. You likely also load massive amounts of structured and unstructured information into your data lake to be used for machine learning and AI use cases. With the aging infrastructure, increasing costs and a growing demand, it’s time to consider upgrading to a modern cloud-based data platform.

You must consider your company’s current business requirements and long-term strategy when choosing the right solution. The most important thing to consider is architecture, platform and tools. Do an enterprise data store (EDW) or a data lake that is cloud-based most suitable for your needs? Utilize 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 build your own data warehouse?

Cost Comparison of pricing models and other factors such as compute and storage to ensure that your budget is compatible with your requirements. Choose a vendor with an expense structure that fits your short-, middle-and long-term data strategy.

Performance: Assess the current and projected data volumes and complexity of queries to determine if you want the best system for your initiatives based on data. Select a vendor with an scalable data bigdataroom.info/vdr-for-insolvency-bankruptcy-restructuring model with flexibility to adapt to the growth of your business.

Support for programming languages: Make sure that the cloud data warehouse you select will work with your preferred programming language, especially if you intend to use the product for IT projects, development, testing or for other purposes. Choose a vendor who also provides data handling services, like data discovery, profiling, data compression, and efficient data transmission.

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