Data Warehouse Metadata Recreation/Entertainment 
Timo Elliott
NOVEMBER 30, 2022
Because of technology limitations, we have always had to start by ripping information from the business systems and moving it to a different platform—a data warehouse, data lake, data lakehouse, data cloud. You lose the roots: the business context, the metadata, the connections, the hierarchies and security.
Alation
OCTOBER 18, 2022
In the modern data stack, dbt is a key tool to make data ready for analysis. Data analysts and engineers use dbt to transform, test, and document data in the cloud data warehouse. Yet every dbt transformation contains vital metadata that is not captured – until now. These are key details. Conclusion.
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Cloudera
NOVEMBER 12, 2020
Your sunk costs are minimal and if a workload or project you are supporting becomes irrelevant, you can quickly spin down your cloud data warehouses and not be “stuck” with unused infrastructure. Cloud deployments for suitable workloads gives you the agility to keep pace with rapidly changing business and data needs.
Cloudera
JANUARY 21, 2021
While cloud-native, point-solution data warehouse services may serve your immediate business needs, there are dangers to the corporation as a whole when you do your own IT this way. Cloudera Data Warehouse (CDW) is here to save the day! CDW is an integrated data warehouse service within Cloudera Data Platform (CDP).
AWS Big Data
MARCH 7, 2023
Data lakes are more focused around storing and maintaining all the data in an organization in one place. And unlike data warehouses, which are primarily analytical stores, a data hub is a combination of all types of repositories—analytical, transactional, operational, reference, and data I/O services, along with governance processes.
Timo Elliott
DECEMBER 14, 2022
Most innovation platforms make you rip the data out of your existing applications and move it to some another environment—a data warehouse, or data lake, or data lake house or data cloud—before you can do any innovation. But that’s like ripping a tree out of the forest and trying to get it to grow elsewhere.
Domino Data Lab
JULY 2, 2019
In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. Less data gets decompressed, deserialized, loaded into memory, run through the processing, etc.
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