If you’re similar to the majority of organizations then your data warehouse is your primary center for reporting and analytics. You’re likely to also load massive amounts of unstructured and structured data into your data lake for machine learning and artificial intelligence (AI) use cases. With an outdated infrastructure, rising costs and an increasing demand, it’s the right time to think about upgrading to a modern cloud data platform.
To find the ideal solution, you have to take into consideration your company’s long-term strategy as well as the needs of your business today. The most important thing to consider is architecture, platform and tools. What kind of enterprise data store (EDW), or a cloud-based data lake, best suit your needs? Do you need extract, transform and load (ETL) tools or a more flexible source-agnostic layer? Do you intend to create an on-premise cloud data warehouse or employ an managed service?
Cost: Assess pricing models and compare variables such as compute and storage to ensure your budget is in line with your requirements. Choose a vendor whose pricing structure will support your short, mid and long-term data strategies.
Performance: Consider the volume of data currently and in the future and query complexity to choose the best system to assist your data-driven initiatives. Choose a provider that has an adaptable data M&A activities files model that is able to adapt 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 use the software for testing, development or IT projects. Choose a provider that offers data handling services such as data profiling and discovery, data compression and efficient data transmission.
