Is more data always better?
Posted: Thu Feb 06, 2025 3:06 am
“The advancement of AI will result in more data coming back to the on-premises or hybrid world. Enterprises are unlikely to want to send all their data and AI models to the cloud because it is expensive to move,” Borgman says.
He points to the use of query and compute engines that are essentially decoupled from storage as a dominant trend that will work across the various data infrastructures people already have and across multiple data lakes. This is often referred to as “putting compute into data.”
AI workloads that rely on unsorted, inadequate, or incorrect data are becoming a growing problem. But as the evolution of data lakes shows, this is a known problem that can be solved with data governance.
What is DevSecOps and what hinders its implementation in Russia?
Anton Tarasenko, CEO DNA Team | 09.16.2024
The need to adapt to the departure of foreign software and the tightening of regulatory requirements have provoked an increase in demand for secure development tools, but their implementation is not an easy task. Let's consider what problems businesses face when implementing DevSecOps .
Before talking about the problems of implementing benin mobile database in Russia, we need to understand what this methodology is and what its basic differences are from DevOps. The abbreviation DevOps stands for “development” and “operations”. In simple terms, DevOps is the coordination of actions between IT specialists and other departments of the company, which reduces time-to-market when launching new products and makes developers faster and more competitive.
Clearly, having access to a lot of data is useless if you can’t understand it, says Merv Adrian, an independent analyst at IT Market Strategy. “The more data, the better, if you can use it. But if you can’t, it won’t do you any good,” he explains.
Adrian positions programs like Iceberg and Delta Lake as providing a narrative layer on top of rich data that will aid in AI and ML analytics. Organizations that have invested in these technologies will see benefits as they transition into this brave new world.
But the real benefit of developing AI is that skilled teams gain experience with these tools, Adrian believes.
“The data lake, data warehouse, and their offspring, the data lake-warehouse, allow companies to leverage more types and volumes of data. This is useful for generative AI models, which improve when trained on larger, more diverse data sets,” he says.
The data lake continues to exist in one form or another. Mohan put it this way: “Data lakes are here to stay. Long live the data lake!”
He points to the use of query and compute engines that are essentially decoupled from storage as a dominant trend that will work across the various data infrastructures people already have and across multiple data lakes. This is often referred to as “putting compute into data.”
AI workloads that rely on unsorted, inadequate, or incorrect data are becoming a growing problem. But as the evolution of data lakes shows, this is a known problem that can be solved with data governance.
What is DevSecOps and what hinders its implementation in Russia?
Anton Tarasenko, CEO DNA Team | 09.16.2024
The need to adapt to the departure of foreign software and the tightening of regulatory requirements have provoked an increase in demand for secure development tools, but their implementation is not an easy task. Let's consider what problems businesses face when implementing DevSecOps .
Before talking about the problems of implementing benin mobile database in Russia, we need to understand what this methodology is and what its basic differences are from DevOps. The abbreviation DevOps stands for “development” and “operations”. In simple terms, DevOps is the coordination of actions between IT specialists and other departments of the company, which reduces time-to-market when launching new products and makes developers faster and more competitive.
Clearly, having access to a lot of data is useless if you can’t understand it, says Merv Adrian, an independent analyst at IT Market Strategy. “The more data, the better, if you can use it. But if you can’t, it won’t do you any good,” he explains.
Adrian positions programs like Iceberg and Delta Lake as providing a narrative layer on top of rich data that will aid in AI and ML analytics. Organizations that have invested in these technologies will see benefits as they transition into this brave new world.
But the real benefit of developing AI is that skilled teams gain experience with these tools, Adrian believes.
“The data lake, data warehouse, and their offspring, the data lake-warehouse, allow companies to leverage more types and volumes of data. This is useful for generative AI models, which improve when trained on larger, more diverse data sets,” he says.
The data lake continues to exist in one form or another. Mohan put it this way: “Data lakes are here to stay. Long live the data lake!”