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If you’re like most organizations your data lake is an essential hub for business analytics and reporting. You’re likely to also load massive quantities of unstructured and structured data into your data lake to support machine learning and artificial intelligence (AI) use cases. It’s time to upgrade to a more modern data platform. With aging infrastructure and increasing costs, it’s time to consider a cloud-based data platform.
To find the most effective solution, you have to take into consideration your company’s long-term strategy as well as the current business needs. The architecture, platform and tools are all important factors to consider. What kind of enterprise data store (EDW), or cloud-based data lakes best meet your requirements? Utilize extract, transform and loads (ETL) or a scalable source-agnostic layer for integration? Do you plan to set up a cloud data warehouse yourself or employ an managed service?
Cost: Examine pricing models, comparing factors like compute and storage to ensure that your budget is in line with your needs. Choose a vendor whose pricing structure supports your short, medium and long-term data strategies.
Performance: Examine current and projected data volume and query complexity to select an option that will support your data-driven initiatives. Choose a vendor that offers a scalable data model, with the ability to change to the growth of your business.
Support for programming languages: Ensure that the cloud data warehouse software you select will work with your preferred coding language particularly if you are planning to utilize the product for development, testing, or IT projects. Choose a vendor who offers data handling services including data profiling and discovery, data compression, and efficient data transmission.
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