If you’re in the same boat as most companies your data warehouse is an essential hub for business analytics and reporting. Additionally, you’re likely to load huge amounts of unstructured and structured data into your data lake to support machine learning and artificial intelligence (AI) use cases. With an old infrastructure, rising costs, and increasing demand, it’s time for you to consider upgrading to a modern cloud data platform.
To find the right solution, you need to consider your organization’s long-term strategy and the present business requirements. The most important consideration is the architecture, platform and tools. Will an enterprise data warehouse (EDW) or cloud data lake best suit your requirements? Utilize extract, transform and loads (ETL) or a flexible layer of source-agnostic integration? Do you want to build a cloud data warehouse yourself or utilize an managed service?
Cost: Look at pricing models and compare variables like compute and storage to ensure that your budget is in line with your requirements. Choose a vendor whose cost structure can support your short, intermediate and long-term data strategies.
Performance: Examine the volume of data currently and in the future and query complexity big data room to select the right system to support your data-driven initiatives. Choose a provider that has an adaptable data model that can adapt to the growth of your business.
Support for programming languages: Ensure that the cloud software for data warehouse you choose will work with your preferred coding language particularly if you are planning to use the software for testing, development or IT projects. Choose a vendor who also provides data handling services, such as data discovery and profiling, data compression, and efficient data transmission.