data processing

Tech Optimizer
July 21, 2025
Data is crucial for artificial intelligence, especially for inference workloads used in real-time decision-making across various platforms. Traditional centralized cloud-based AI inference struggles with demands for low latency and high availability, particularly in applications like autonomous vehicles and healthcare. Shifting AI inference to the edge reduces latency, enhances data privacy, and lowers bandwidth costs. Antony Pegg emphasizes the need for a multi-master active-active architecture that allows read and write operations at any node, ensuring data synchronization and high availability. Misconceptions about edge AI include beliefs that edge hardware can't handle AI workloads, that edge inference is limited to low-stakes use cases, and that centralized systems are necessary for data integrity. The shift to distributed inference can lead to reduced latency, faster insights, and lower costs, while supporting data compliance with regulations. Companies are adopting distributed PostgreSQL solutions with multi-master architecture for low latency and high availability. Enquire AI is an example of a company that improved performance and compliance by transitioning to pgEdge Cloud. This architecture allows for consistent data availability and supports scalable AI solutions at the edge.
Tech Optimizer
July 18, 2025
Regions Financial Corporation is modernizing its technological infrastructure to enhance database capabilities and enable real-time data processing. The corporation is selecting a flexible database solution to align its application and technology teams, aiming to drive efficiency and improve customer experiences. A white paper detailing their transformation journey and the role of EDB Postgres is available for further insights.
Tech Optimizer
July 10, 2025
EnterpriseDB (EDB) has returned to the Goodwood Festival of Speed, focusing on the impact of advanced AI data systems in the automotive industry. A global survey by EDB of 190 automotive executives revealed that only 13% of organizations are successfully leveraging agentic and generative AI, with these companies achieving a fivefold return on investment through a focus on data sovereignty. The automotive sector is undergoing a significant transformation, with the U.K. automotive AI market projected to grow from £8 million in 2024 to £2 million by 2030, reflecting a 21.7% CAGR. EDB is also fostering future talent through initiatives that encourage students to propose innovative data and AI solutions, with a £2,000 prize for the winning idea. The U.K. STEM workforce is expected to grow by 10% by 2030, while many tech firms face challenges in filling roles. EDB has received multiple recognitions for its innovation and workplace culture, including listings in various industry awards. EDB Postgres® AI is described as an open, enterprise-grade sovereign data and AI platform that integrates various workloads while ensuring compliance and security.
Tech Optimizer
June 13, 2025
Databricks has introduced advancements at its user conference, focusing on agentic AI pipelines and a Postgres-powered Lakebase. The Lakebase streamlines data management and analytics by combining PostgreSQL with Databricks' data lake architecture. The agentic AI pipelines automate data workflows, allowing organizations to gain AI-driven insights with minimal manual intervention. Key features include automated workflows that reduce processing time, enhanced data management through a familiar relational database experience, and the ability to extract actionable insights using advanced AI capabilities.
Tech Optimizer
June 12, 2025
Databricks has launched Lakebase, a fully managed Postgres database, during the Data + AI Summit 2025. Lakebase is currently in public preview and integrates with Databricks' Data Intelligence Platform, targeting AI-driven application development. It introduces an operational layer to the lakehouse ecosystem, allowing developers to build AI applications on a multi-cloud platform. The database architecture, powered by Neon, separates compute from storage for independent scaling, ensuring low latency and high concurrency. Lakebase supports rapid deployment and a usage-based pricing model, and features include copy-on-write clones for development and testing. Databricks aims to create a new category in the database market with Lakebase, which is built on open-source Postgres and compatible with existing tools. The operational database market is valued at over 0 billion, and recent surveys indicate PostgreSQL's rising popularity among developers. The launch follows Snowflake's acquisition of Crunchy Data to enhance its Postgres offerings, indicating a trend towards PostgreSQL in the industry.
Tech Optimizer
June 12, 2025
Databricks has launched Lakebase, a fully managed Postgres database designed for AI applications, currently in Public Preview. It integrates an operational database layer into Databricks' Data Intelligence Platform, facilitating the development of data applications and AI agents in a multi-cloud environment. Lakebase uses Neon technology within a lakehouse architecture, allowing for efficient real-time data processing and scalable operations. Key features include independent scaling of compute and storage, low latency under 10 milliseconds, high concurrency over 10,000 queries per second, rapid launch times under a second, and a consumption-based payment model. It also offers data synchronization with lakehouse tables, an online feature store for machine learning, and is managed entirely by Databricks with built-in security features. During its Private Preview, Lakebase attracted participation from hundreds of enterprises across various sectors. It is supported by a partner network including Accenture, Deloitte, and others, and will receive further enhancements in the coming months.
Tech Optimizer
June 9, 2025
Snowflake has acquired Crunchy Data for [openai_gpt model="gpt-4o-mini" prompt="Summarize the content and extract only the fact described in the text bellow. The summary shall NOT include a title, introduction and conclusion. Text: The rivalry between Snowflake and Databricks has taken an intriguing turn, extending its reach into the burgeoning realm of PostgreSQL. This shift signifies a strategic pivot as both companies seek to capitalize on the growing demand for robust database solutions tailored for AI applications. In a bold move, Snowflake has recently acquired Crunchy Data for 0 million, while Databricks has made headlines by purchasing Neon for a staggering billion. These acquisitions are not merely about expanding their portfolios; they represent a calculated effort to harness the capabilities of PostgreSQL in the context of AI-driven data management. Snowflake's acquisition announcement, detailed in a blog post, highlights PostgreSQL as a favored choice among developers, owing to its flexibility, cost efficiency, and inherent AI features, such as vector support (pg vector). The open-source nature of PostgreSQL, coupled with its vibrant ecosystem, further enhances its appeal. “We’re tackling a massive 0 billion market opportunity and a real need for our customers to bring Postgres to the Snowflake AI Data Cloud,” stated Vivek Raghunathan, Snowflake’s SVP of engineering. Why are the Giants Betting on PostgreSQL? “PostgreSQL’s ecosystem and extensions are growing fast. More people now know this database better than any other. pgvector gave it a big push,” remarked Arpit Bhayani, creator of DiceDB, when asked about PostgreSQL's rise as the preferred database for AI-native applications. Snowflake Postgres builds upon the company's earlier foray into transactional data with Unistore, which integrates transactional and analytical workloads within a single system. By enhancing native PostgreSQL support, Snowflake Postgres aims to provide enterprises with a production-ready solution for transactional applications that require compatibility with PostgreSQL. The open-source relational database PostgreSQL has seen a surge in popularity, surpassing MySQL as the most favored database among developers, according to Stack Overflow’s 2023 and 2024 Developer Surveys. Its capabilities in handling geospatial data (via PostGIS), time series data (via TimescaleDB), JSON, and vector embeddings (via pgvector) position it as an ideal choice for AI applications. In a recent LinkedIn post, senior data engineer Avinash S emphasized that these acquisitions signify more than just the addition of another database. He views them as a strategic bet on PostgreSQL as the backbone of the AI-native era, particularly in its serverless and cloud-native forms. “Imagine AI agents spinning up databases for every real-time task or experiment, then discarding them. Traditional databases can’t handle this ‘disposable’ scale. Serverless Postgres delivers the rapid provisioning, elasticity, and cost-efficiency that AI agents desperately need to work autonomously and at speed,” he elaborated. “It’s not just agentic. Because many people are talking about it and using it, it has become the de facto standard,” Bhayani noted, although he cautioned that the assumption of AI agents creating databases may be overly optimistic. Factorial Advisors echoed this sentiment in a blog post, asserting that Databricks’ acquisition of Neon aligns with its broader ambition to construct a unified data intelligence platform. “With over billion in financing and a recent billion valuation, Databricks has the financial muscle to keep acquiring,” they wrote, highlighting previous acquisitions like Tabular ( billion) and MosaicML (.3 billion). Neon addresses the increasing demand for databases that operate at ‘agentic speed’ while maintaining cost-effectiveness through pay-as-you-go models. These strategic moves position both Snowflake and Databricks to challenge hyperscalers like AWS, Microsoft Azure, and Google Cloud, which offer managed PostgreSQL services seamlessly integrated with their AI stacks. Neon vs Crunchy Data Established in 2012, Crunchy Data specializes in providing a comprehensive, production-ready PostgreSQL solution that encompasses backups, high availability, disaster recovery, connection scaling, and monitoring. This service supports mission-critical deployments across cloud, on-premises, and hybrid environments. Snowflake has asserted that its new offering, Snowflake Postgres, will facilitate the integration of transactional Postgres data into its platform, thereby accelerating innovation and granting developers enhanced agility, visibility, and control to construct reliable AI agents and applications more swiftly. Crunchy’s expertise ensures that Postgres-powered applications can operate natively on Snowflake without necessitating code rewrites. Developers stand to gain from built-in connection pooling, performance metrics, and logging, simplifying the process of building and managing scalable applications. Conversely, Databricks CEO Ali Ghodsi emphasized that frontier LLMs have been trained on extensive datasets from the Postgres open-source ecosystem, rendering AI agents inherently adept at utilizing Neon, which is built on PostgreSQL. He highlighted that Databricks and Neon share a foundational technical infrastructure and a commitment to open source, noting that Databricks originated the Apache Spark project at UC Berkeley, the same institution where PostgreSQL was developed. Ghodsi pointed out that OLTP databases, a 0 billion market, remain largely dominated by legacy products. With Neon, Databricks aims to disrupt this landscape by crafting the most developer- and AI agent-friendly database platform available. Since Neon became generally available last year, the proportion of databases created by AI agents has surged from around 30% to over 80%, indicating a significant shift towards AI-driven database creation. The rush towards PostgreSQL is palpable, with Snowflake and Databricks actively acquiring niche providers to enhance their offerings. This trend transcends mere database proliferation; it signifies a readiness for AI, real-time data processing, and the evolving demands of large-scale enterprises. These acquisitions also reflect a broader consolidation trend within the data and AI infrastructure market. Recent transactions, such as Salesforce’s billion acquisition of Informatica, ServiceNow’s purchase of Data.World, and Alation’s acquisition of Numbers Station, illustrate how companies are racing to establish comprehensive AI-ready platforms. According to Bhayani, much of this activity is driven by the imperative to acquire customers and specialized expertise." max_tokens="3500" temperature="0.3" top_p="1.0" best_of="1" presence_penalty="0.1" frequency_penalty="frequency_penalty"] million, while Databricks has purchased Neon for billion. Both companies are focusing on PostgreSQL to enhance their database solutions for AI applications. PostgreSQL has gained popularity among developers, surpassing MySQL as the most favored database, due to its flexibility and features like vector support. Snowflake aims to integrate PostgreSQL data into its platform with Snowflake Postgres, while Databricks seeks to leverage Neon to create a developer-friendly database platform. The trend towards PostgreSQL is part of a broader consolidation in the data and AI infrastructure market, with companies acquiring specialized expertise to build comprehensive AI-ready platforms.
AppWizard
May 28, 2025
The One UI 8 beta is now available for Galaxy S25 models in select regions, featuring enhanced AI capabilities, a tailored user experience for different device types, and proactive suggestions. It introduces improvements to the Reminder app, Quick Share, multitasking, Samsung Internet, and accessibility features. The rollout is limited to regions including Germany, Korea, the U.K., and the U.S., excluding the Galaxy S25 Edge. A stable version is expected to launch with new foldable devices this summer. Key features include multimodal capabilities, enhanced Now Bar and Now Brief features, local data processing options, and improvements to the Auracast feature. The Reminder app will consolidate tasks into a single interface, and Quick Share will receive enhancements. Additional features include improved file search, a redesign of Samsung Internet, multitasking enhancements, new Calendar features, and social health management options through Samsung Health. More features may be revealed as the beta progresses.
AppWizard
May 16, 2025
Google is expanding its Gemini Nano AI model by introducing new ML Kit GenAI APIs, expected to be unveiled at the I/O 2025 event. These APIs will allow developers to integrate features such as text summarization, proofreading, rewriting, and image description generation into their applications. Gemini Nano operates on devices, enhancing privacy by processing data locally. The ML Kit GenAI APIs will support various languages and functionalities, including generating concise summaries, correcting grammar, transforming chat messages, and providing image descriptions. Unlike the experimental AI Edge SDK, the GenAI APIs will be in beta, allowing for broader device compatibility beyond the Pixel 9 series, including other Android devices. Public documentation for the ML Kit GenAI APIs is now available for developers.
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