If you’re like most organizations your data warehouse serves as an important hub for business analytics and reporting. You’re likely to also load massive amounts of structured and unstructured data into your data lake to support machine learning and artificial intelligence (AI) applications. With the aging infrastructure, increasing costs and a growing demand, it’s time for you to consider upgrading to a modern cloud-based data platform.
To find the right solution, you need to consider your company’s long-term strategies and current business requirements. The platform, architecture and tool set are important aspects to consider. Are an enterprise-level data warehouse (EDW) or cloud-based data lakes best suit your needs? Utilize extract, transform, and loads (ETL) or a flexible layer of source-agnostic integration? Do you intend to create an on-premise cloud data warehouse or utilize a managed service?
Cost Comparing pricing models and other factors like storage and compute to ensure that your budget is in line with the amount you use. Choose a vendor with an appropriate cost structure that is compatible with your short-, mid-and long-term strategy for data.
Performance: Consider current and projected data volume and query complexity to select an option that will support your data-driven initiatives. Choose a vendor that offers the ability to scale data models, that is flexible enough to change www.bigdataroom.info/vdr-for-insolvency-bankruptcy-restructuring/ to your business’s growth.
Support for programming languages: Ensure that the cloud data warehouse you choose supports your preferred coding language, particularly if intend to use the product for IT projects, development, testing or other purposes. Choose a vendor who offers data handling services, including data profiling and discovery, data compression, and efficient data transmission.