If you’re in the same boat as most organizations your data warehouse is the primary point for reporting and business analytics. Additionally, you’re likely to load huge quantities of unstructured and structured data into your data lake for machine learning and artificial intelligence (AI) use cases. It’s time to upgrade to a modern data platform. With aging infrastructure and rising costs, it is time to think about the cloud data platform.
You should consider the current needs of your business and long-term goals when selecting the right solution. The architecture, platform and tool set are key factors to consider. Do an enterprise data warehouse (EDW) or cloud-based data lakes best suit your requirements? Do you need extract transform and load (ETL) tools or a more flexible source-agnostic layer? Do you want to utilize a managed cloud service or deploy your own data warehouse?
Cost Pricing: Review pricing models and compare factors such as storage and compute to ensure that your budget is in line with your needs. Select a vendor that has the cost structure that will support your short-, midand long-term data strategy.
Performance: Examine current and projected data volume and query complexity before deciding on the best system to help you with your data-driven projects. Choose a provider that provides an scalable data model with flexibility www.bigdataroom.info/ to adapt as your business expands.
Support for programming languages: Make sure that the cloud data warehouse you select supports your preferred coding language, especially if intend to use the software for IT projects testing, development, or other purposes. Select a vendor that offers data handling services such as data profiling and discovery, data compression, and efficient data transmission.
