While GoodData’s headless BI engine offers developers the ability to build modular, scalable, and decoupled analytics consumable anywhere, Dremio connects to multiple data lake sources and enables the user to query data directly on the data lake storage without having to move or copy the data. GoodData and Dremio have implemented integration between GoodData.CN, the cloud-native analytics platform, and Dremio’s SQL Lakehouse Platform to better meet the needs of developers looking for real-time, consistent, and open analytics capabilities - without moving any data. To solve these shortcomings, we need to replace cumbersome data pipelines and decouple analytics from the presentation layer to provide consistent metrics to our data consumers. Meanwhile, the second issue is that data tools and applications consuming the data yield inconsistent results due to them using their own proprietary data models, calculations, and metric definitions. ![]() The data-movement problem arises at any step in the analytical stack that requires data to be physically moved or copied with the resulting side effect being that of data latency and duplication. ![]() Two such pain points are the physical movement of data between different systems and the tight coupling between analytics and consumption. Building analytics has become faster and easier with the latest advances in cloud technologies, but current analytical solutions still have critical drawbacks as we strive to provide consistent, real-time analytics for various use cases.
0 Comments
Leave a Reply. |