If you’re like many organizations your data lake is an essential hub for business analytics and reporting. You also likely populate massive quantities of unstructured and structured data into your data lake for machine learning and artificial intelligence (AI) use cases. With an old infrastructure, rising costs and an increasing demand, it’s time for you to look into upgrading to a more modern cloud data platform.
To find the most effective solution, you must 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 meet your needs? Should you employ extract transform and load (ETL) tools or a more flexible source-agnostic layer? Do you wish to use an managed cloud service or even build your own data warehouse?
Cost: Compare pricing models and compare factors like compute and storage to ensure that your budget is compatible with your requirements. Select a company with the cost structure that will support your short-, mid-and long-term strategy for data.
Performance: Consider the current and projected volume of data and query complexity before deciding on the best system to assist your data-driven initiatives. Choose a provider that has flexible data models that can adapt to your business growth.
Support for programming languages: Ensure that the cloud data warehouse you select will work with your preferred programming language, especially if you plan to use the product for IT projects, development, testing or for any other purpose. Choose a vendor who also offers data handling services like data discovery, profiling, data compression and efficient data transmission.