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GenAI and Flexible Consumption Models Reshape Hybrid Storage Infrastructure

The New Stack

Assessing AI Needs Developers should start by clearly understanding the workload and performance attributes of their Gen AI projects. Each part of the AI pipeline has different storage requirements, so it is often necessary to align the infrastructure to the workload on a bespoke basis. For example, train models using existing data.

Storage 130
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New OLTP: Postgres With Separate Compute and Storage

The New Stack

Such databases are monolithic, combining compute and storage in big machines, which leads to various problems, including over-provisioning, scaling challenges, performance issues and a range of system complexities. In the new Lakebase product from Databricks, compute and storage are separated. The storage formats are open.

Storage 96
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What is data architecture? A framework to manage data

CIO

Its an offshoot of enterprise architecture that comprises the models, policies, rules, and standards that govern the collection, storage, arrangement, integration, and use of data in organizations. It includes data collection, refinement, storage, analysis, and delivery. Cloud storage. Curate the data. Cloud computing.

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Comprehensive data management for AI: The next-gen data management engine that will drive AI to new heights

CIO

The data is spread out across your different storage systems, and you don’t know what is where. Enterprises need infrastructure that can scale and provide the high performance required for intensive AI tasks, such as training and fine-tuning large language models. How did we achieve this level of trust? Through relentless innovation.

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Cloudera Lakehouse Optimizer Makes it Easier Than Ever to Deliver High-Performance Iceberg Tables

Cloudera

It combines the flexibility and scalability of data lake storage with the data analytics, data governance, and data management functionality of the data warehouse. Compaction is a process that rewrites small files into larger ones to improve performance.

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Centralized Monitoring for Data Pipelines: Combining Azure Data Factory Diagnostics with Databricks System Tables

Xebia

Introduction Monitoring data pipelines is essential for ensuring reliability, troubleshooting issues, and tracking performance over time. The first step is to export the diagnostic settings to a storage account accessible by Databricks. The first step is to export the diagnostic settings to a storage account accessible by Databricks.

Azure 130
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Choosing the best AI models for your business

CIO

Fine-tuning model : By adjusting model weighting and incorporating proprietary data, fine-tuning models lets businesses get more out of their models with higher quality responses, with the models trained to address specific tasks for more accurate and specialized performance.

Training 196