News from the AI & ML world
DeeperML - #aidata
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Chris McKay@Maginative
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Snowflake is aggressively expanding its footprint in the cloud data platform market, moving beyond its traditional data warehousing focus to become a comprehensive AI platform. This strategic shift was highlighted at Snowflake Summit 2025, where the company showcased its vision of empowering business users with advanced AI capabilities for data exploration and analysis. A key element of this transformation is the recent acquisition of Crunchy Data, a move that brings enterprise-grade PostgreSQL capabilities into Snowflake’s AI Data Cloud. This acquisition is viewed as both a defensive and offensive maneuver in the competitive landscape of cloud-native data intelligence platforms.
The acquisition of Crunchy Data for a reported $250 million marks a significant step in Snowflake’s strategy to enable more complex data pipelines and enhance its AI-driven data workflows. Crunchy Data's expertise in PostgreSQL, a well-established open-source database, provides Snowflake with a FedRAMP-compliant, developer-friendly, and AI-ready database solution. Snowflake intends to provide enhanced scalability, operational governance, and performance tooling for its wider enterprise client base by incorporating Crunchy Data's technology. This strategy is meant to address the need for safe and scalable databases for mission-critical AI applications and also places Snowflake in closer competition with Databricks. Furthermore, Snowflake introduced new AI-powered services at the Summit, including Snowflake Intelligence and Cortex AI, designed to make business data more accessible and actionable. Snowflake Intelligence enables users to query data in natural language and take actions based on the insights, while Cortex AISQL embeds AI operations directly into SQL. These initiatives, coupled with the integration of Crunchy Data’s PostgreSQL capabilities, indicate Snowflake's ambition to be the operating system for enterprise AI. By integrating such features, Snowflake is trying to transform from a simple data warehouse to a fully developed platform for AI-native apps and workflows, setting the stage for further expansion and innovation in the cloud data space. Recommended read:
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Berry Zwets@Techzine Global
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Snowflake announced the acquisition of Crunchy Data at Snowflake Summit 2025 for a reported $250 million. Crunchy Data, based in South Carolina, has been building on the open-source Postgres database for over a decade. This move signals Snowflake's ambition to enhance its AI Data Cloud with enterprise-grade PostgreSQL capabilities, bringing it into closer competition with companies like Databricks. The acquisition aims to combine operational and analytical workloads, creating an enterprise-grade AI infrastructure.
The acquisition will add approximately 100 Crunchy Data employees to Snowflake. It will also power a new offering called Snowflake Postgres. Snowflake's acquisition is a defensive and offensive maneuver in the competition for cloud-native data intelligence platforms. Like Databricks' purchase of Neon, Snowflake is betting on the convergence of operational and analytical workloads, espoused in open data lakehouse architectures. Snowflake envisions delivering a FedRAMP-compliant, developer-friendly, and AI-ready Postgres solution within the Snowflake Data Cloud, targeting organizations needing secure, scalable databases for critical AI applications. The new Snowflake Postgres product is set for private preview. It will bring Crunchy Data’s scaling, operational governance, and performance tooling to a broader enterprise client base. Snowflake has rapidly become a staple for data professionals and has arguably changed how developers and data scientists interact with data. Recommended read:
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@openssf.org
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industrialcyber.co
, www.scworld.com
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Global cybersecurity agencies, including the U.S. Cybersecurity and Infrastructure Security Agency (CISA), the National Security Agency (NSA), the Federal Bureau of Investigation (FBI), and international partners, have jointly released guidance on AI data security best practices. The new Cybersecurity Information Sheet (CSI) aims to address the critical importance of securing data used to train and operate AI systems, emphasizing that the accuracy, integrity, and trustworthiness of AI outcomes are directly linked to the quality and security of the underlying data. The guidance identifies potential risks related to data security and integrity throughout the AI lifecycle, from initial planning and design to post-deployment operation and monitoring.
Building on previous guidance, the new CSI provides ten general best practices organizations can implement to enhance AI data security. These steps include ensuring data comes from trusted, reliable sources using provenance tracking to verify data changes, using checksums and cryptographic hashes to maintain data integrity during storage and transport, and employing quantum-resistant digital signatures to authenticate and verify trusted revisions during training and other post-training processes. The guidance also recommends using only trusted infrastructure, such as computing environments leveraging zero trust architecture, classifying data based on sensitivity to define proper access controls, and encrypting data using quantum-resistant methods like AES-256. The guidelines also emphasize the importance of secure data storage using certified devices compliant with NIST FIPS 140-3, which covers security requirements for cryptographic modules, and privacy preservation of sensitive data through methods like data masking. Furthermore, the agencies advise secure deletion of AI training data from repurposed or decommissioned storage devices. Owners and operators of National Security Systems, the Defense Industrial Base, federal agencies, and critical infrastructure sectors are urged to review the publication and implement its recommended best practices to mitigate risks like data supply chain poisoning and malicious data tampering. Recommended read:
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@insidehpc.com
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NVIDIA and Dataiku are collaborating on the NVIDIA AI Data Platform reference design to support organizations' generative AI strategies by simplifying unstructured data storage and access. This collaboration aims to democratize analytics, models, and agents within enterprises by enabling more users to harness high-performance NVIDIA infrastructure for transformative innovation. As a validated component of the full-stack reference architecture, any agentic application developed in Dataiku will work on the latest NVIDIA-Certified Systems, including NVIDIA RTX PRO Server and NVIDIA HGX B200 systems. Dataiku will also work with NVIDIA on the NVIDIA AI Data Platform reference design, built to support organizations’ generative AI strategies by simplifying unstructured data storage and access.
DDN (DataDirect Networks) also announced its collaboration with NVIDIA on the NVIDIA AI Data Platform reference design. This collaboration aims to simplify how unstructured data is stored, accessed, and activated to support generative AI strategies. The DDN-NVIDIA offering combines DDN Infinia, an AI-native data platform, with NVIDIA NIM and NeMo Retriever microservices, NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, and NVIDIA Networking. This enables enterprises to deploy Retrieval-Augmented Generation (RAG) pipelines and intelligent AI applications grounded in their own proprietary data—securely, efficiently, and at scale. Starburst is also adding agentic AI capabilities to its platform, including a pre-built agent for insight exploration as well as tools and tech for building custom agents. These new agentic AI capabilities include Starburst AI Workflows, which includes a collection of capabilities, including vector-native AI search, AI SQL functions, and AI model access governance functions. The AI search functions include a built-in vector store that allows users to convert data into vector embeddings and then to search against them. Starburst is storing the vector embeddings in Apache Iceberg, which it has built its lakehouse around. Recommended read:
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@Dataconomy
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Databricks has announced its acquisition of Neon, an open-source database startup specializing in serverless Postgres, in a deal reportedly valued at $1 billion. This strategic move is aimed at enhancing Databricks' AI infrastructure, specifically addressing the database bottleneck that often hampers the performance of AI agents. Neon's technology allows for the rapid creation and deployment of database instances, spinning up new databases in milliseconds, which is critical for the speed and scalability required by AI-driven applications. The integration of Neon's serverless Postgres architecture will enable Databricks to provide a more streamlined and efficient environment for building and running AI agents.
Databricks plans to incorporate Neon's scalable Postgres offering into its existing big data platform, eliminating the need to scale separate server and storage components in tandem when responding to AI workload spikes. This resolves a common issue in modern cloud architectures where users are forced to over-provision either compute or storage to meet the demands of the other. With Neon's serverless architecture, Databricks aims to provide instant provisioning, separation of compute and storage, and API-first management, enabling a more flexible and cost-effective solution for managing AI workloads. According to Databricks, Neon reports that 80% of its database instances are provisioned by software rather than humans. The acquisition of Neon is expected to give Databricks a competitive edge, particularly against competitors like Snowflake. While Snowflake currently lacks similar AI-driven database provisioning capabilities, Databricks' integration of Neon's technology positions it as a leader in the next generation of AI application building. The combination of Databricks' existing data intelligence platform with Neon's serverless Postgres database will allow for the programmatic provisioning of databases in response to the needs of AI agents, overcoming the limitations of traditional, manually provisioned databases. Recommended read:
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Ashutosh Singh@The Tech Portal
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Apple is enhancing its AI capabilities, known as Apple Intelligence, by employing synthetic data and differential privacy to prioritize user privacy. The company aims to improve features like Personal Context and Onscreen Awareness, set to debut in the fall, without collecting or copying personal content from iPhones or Macs. By generating synthetic text and images that mimic user behavior, Apple can gather usage data and refine its AI models while adhering to its strict privacy policies.
Apple's approach involves creating artificial data that closely matches real user input to enhance Apple Intelligence features. This method addresses the limitations of training AI models solely on synthetic data, which may not always accurately reflect actual user interactions. When users opt into Apple's Device Analytics program, the AI models will compare these synthetic messages against a small sample of a user’s content stored locally on the device. The device then identifies which of the synthetic messages most closely matches its user sample, and sends information about the selected match back to Apple, with no actual user data leaving the device. To further protect user privacy, Apple utilizes differential privacy techniques. This involves adding randomized data to broader datasets to prevent individual identification. For example, when analyzing Genmoji prompts, Apple polls participating devices to determine the popularity of specific prompt fragments. Each device responds with a noisy signal, ensuring that only widely-used terms become visible to Apple, and no individual response can be traced back to a user or device. Apple plans to extend these methods to other Apple Intelligence features, including Image Playground, Image Wand, Memories Creation, and Writing Tools. This technique allows Apple to improve its models for longer-form text generation tasks without collecting real user content. Recommended read:
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