#data-warehouse

#data-warehouse

Galileo provides AI teams with tools to evaluate the quality of AI outputs, detect errors before they reach end users, and continuously improve the behavior of AI agents in production.

When civilian banks, logistics platforms, and payment processors share physical data center infrastructure with military AI systems, those facilities become legitimate military targets under international humanitarian law - and the civilian services housed inside lose their legal protection.

"This is more likely to complement existing SIEMs than replace them. Early adoption will come from large enterprises already committed to Databricks, especially those seeking flexibility or cost control."

Automating Kubernetes workloads with Operators can provide the same level of functionality as DBaaS, while still avoiding lock-in to a specific provider.

S3 Files is built on Amazon EFS and automatically translates file system operations into S3 requests, allowing applications to work with S3 data without code changes.

There is a growing emphasis on database compliance today due to the stricter enforcement of compliance rules and regulations to safeguard user privacy. For example, GDPR fines can reach £17.5 million or 4% of annual global turnover (the higher of the two applies). Besides the direct monetary implications, companies also need to prioritize compliance to protect their brand reputation and achieve growth.

Developers have spent the past decade trying to forget databases exist. Not literally, of course. We still store petabytes. But for the average developer, the database became an implementation detail; an essential but staid utility layer we worked hard not to think about. We abstracted it behind object-relational mappers (ORM). We wrapped it in APIs. We stuffed semi-structured objects into columns and told ourselves it was flexible.

A future-proof IT infrastructure is often positioned as a universal solution that can withstand any change. However, such a solution does not exist. Nevertheless, future-proofing is an important concept for IT leaders navigating continuous technological developments and security risks, all while ensuring that daily business operations continue. The challenge is finding a balance between reactive problem solving and proactive planning, because overlooking a change can cost your organization. So, how do you successfully prepare for the future without that one-size-fits-all solution?

Uber has built HiveSync, a sharded batch replication system that keeps Hive and HDFS data synchronized across multiple regions, handling millions of Hive events daily. HiveSync ensures cross-region data consistency, enables Uber's disaster recovery strategy, and eliminates inefficiency caused by the secondary region sitting idle, which previously incurred hardware costs equal to the primary, while still maintaining high availability. Built initially on the open-source Airbnb ReAir project, HiveSync has been extended with sharding, DAG-based orchestration, and a separation of control and data planes.

Databricks today announced the general availability of Lakebase on AWS, a new database architecture that separates compute and storage. The managed serverless Postgres service is designed to help organizations build faster without worrying about infrastructure management. When databases link compute and storage, every query must use the same CPU and memory resources. This can cause a single heavy query to affect all other operations. By separating compute and storage, resources automatically scale with the actual load.

Organizations are drowning in dashboards, KPIs, performance metrics, behavioral traces, biometric indicators, predictive scores, engagement rates, and AI-generated forecasts. We have more data than we know what to do with. We pretend that the mere presence of data guarantees clarity. It does not. That's data hubris—the arrogant belief that because something can be measured, it can be mastered.

Snowflake offers a fully managed data platform, but Sumo Logic users often lack insight into performance, login activity, and operational health. The Sumo Logic Snowflake Logs App analyzes login and access activity to identify anomalies or suspicious behavior. It also optimizes data pipelines with insights into long-running or failing queries. Teams can centralize log data to facilitate correlation across applications, cloud services, and data platforms.

The rise of generative AI is often seen as an existential threat to the SaaS model. Interfaces would disappear, software would fade away, and existing players would become irrelevant. However, new figures from Databricks paint a different picture. Rather than undermining SaaS, AI appears to be increasing its use. This week, Databricks reported a revenue run rate of $5.4 billion, a 65 percent year-on-year increase. More than a quarter of that now comes from AI-related products.

Unverified and low quality data generated by artificial intelligence (AI) models - often known as AI slop - is forcing more security leaders to look to zero-trust models for data governance, with 50% of organisations likely to start adopting such policies by 2028, according to Gartner's seers. Currently, large language models (LLMs) are typically trained on data scraped - with or without permission - from the world wide web and other sources including books, research papers, and code repositories.

SHAP for feature attribution SHAP quantifies each feature's contribution to a model prediction, enabling: LIME for local interpretability LIME builds simple local models around a prediction to show how small changes influence outcomes. It answers questions like: "Would correcting age change the anomaly score?" "Would adjusting the ZIP code affect classification?" Explainability makes AI-based data remediation acceptable in regulated industries.

"If you look at the enterprise, there's just enormous enthusiasm to deploy AI, but the problem is that the infrastructure, the power, and the operational foundation that is required to run it just aren't there," Alex Bouzari, CEO of DDN, told The Register. "And so as a result, it pops up in the financial elements with IT projects getting delayed, the GPUs being underutilized, power costs going up. And so the economics, I think, for lots of organizations don't pencil out because of these challenges."