The Premise Of Cloud Data Warehousing

Derik Hammer explains how cloud data warehouses differ from their on-prem cousins:

Given the data processing needs of a data warehouse, they tend to be implemented on massively parallel processing (MPP) systems. The MPP architecture replies upon a shared nothing concept for distributing data across various slices. Compute nodes are layered on top of the storage and processes queries for data residing in its local slice. The control node is responsible for taking a query and dividing it up into smaller queries to be run in parallel on the compute nodes.

Read the whole thing.

Related Posts

Using Databricks Delta In Lieu Of Lambda Architecture

Jose Mendes contrasts the Lambda architecture with the Databricks Delta architecture and gives us a quick example of using Databricks Delta: The major problem of the Lambda architecture is that we have to build two separate pipelines, which can be very complex, and, ultimately, difficult to combine the processing of batch and real-time data, however, […]

Read More

Integrating PowerApps With Power BI

Wolfgang Strasser continues a series on the PowerPlatform with a post showing how to integrate an existing PowerApp with Power BI: When creating a new PowerApp using the Power BI integration, you get an additional data source – PowerBIIntegration that serves as the connection to the Power BI report. Whenever a filtering action occurs in the Power […]

Read More

Categories

January 2018
MTWTFSS
« Dec Feb »
1234567
891011121314
15161718192021
22232425262728
293031