The separation of data storage and computing
Posted: Mon Dec 23, 2024 11:00 am
Before the release of this press release, K had been hiding itself as "Ki" for more than two years, and it had not even been able to gain customers. It was done secretly by sending emails. In this way, the first batch of seed users were gradually accumulated. The product and market matching () was also initially verified. At this time, k already had:) a clear, clear and unique positioning data warehouse () and It is not a new concept, but cloud data warehouse is. The concept of data warehouse was proposed in 2016.
Before k, data warehouse has gone through japan mobile phone number the process from local proprietary hardware (representative product: ), to shared storage (representative product: ). The development history of big data. But the data warehouse product k, which is completely based on cloud computing architecture, is the first one.) Product selling points that fit the user’s pain points. User pain points: Traditional data warehouses are too complex, inflexible and expensive. Product selling points: k’s cloud services will of the elasticity, scalability and flexibility of the cloud to provide the powerful functions of the data warehouse, the flexibility of the big data platform, and the elasticity of the cloud at a lower cost than local data warehouses.
User pain points: emerging big data. Data platforms still rely on the expertise of professionals. Product selling points: k As a native relational database that fully supports standards, any analyst can access the data self-service, allowing organizations to leverage the tools and skills they already have.) Unique product value ( i vi Data Warehousing as a Service). k Eliminates the hassles associated with managing and tuning databases. This enables self-service data access so analysts can focus on getting value from the data rather than managing the underlying hardware and software. Dimensional elasticity. Unlike existing products, k's elastic scaling technology can scale users, data and workloads independently to provide optimal performance at any scale.
Before k, data warehouse has gone through japan mobile phone number the process from local proprietary hardware (representative product: ), to shared storage (representative product: ). The development history of big data. But the data warehouse product k, which is completely based on cloud computing architecture, is the first one.) Product selling points that fit the user’s pain points. User pain points: Traditional data warehouses are too complex, inflexible and expensive. Product selling points: k’s cloud services will of the elasticity, scalability and flexibility of the cloud to provide the powerful functions of the data warehouse, the flexibility of the big data platform, and the elasticity of the cloud at a lower cost than local data warehouses.
User pain points: emerging big data. Data platforms still rely on the expertise of professionals. Product selling points: k As a native relational database that fully supports standards, any analyst can access the data self-service, allowing organizations to leverage the tools and skills they already have.) Unique product value ( i vi Data Warehousing as a Service). k Eliminates the hassles associated with managing and tuning databases. This enables self-service data access so analysts can focus on getting value from the data rather than managing the underlying hardware and software. Dimensional elasticity. Unlike existing products, k's elastic scaling technology can scale users, data and workloads independently to provide optimal performance at any scale.