Systems | Development | Analytics | API | Testing

How Leaders in Financial Services and Manufacturing Accelerate Business Outcomes with Data and AI

Some 70% of organizations are actively exploring or implementing large language model (LLM) use cases, but fewer than a third of generative AI experiments have made it into production. A common hurdle? The inability to access and leverage the data crucial for running AI applications effectively. Snowflake’s Accelerate 2025 virtual events dive into the challenges and myriad opportunities offered by AI.

5 Key SaaS Features for Developing Successful PropTech Solutions

The PropTech market is more competitive than ever. More than 5,000 real estate SaaS companies are fighting for market share, with $9.35 billion in funding backing 1,000 of them. But funding alone doesn’t guarantee success. Plenty of well-funded SaaS platforms often fail to scale, integrate, or secure their data properly. Instead of improving operations, they create bottlenecks, inefficiencies, and frustrated users. So, what makes SaaS for PropTech truly effective?

Cluster Linking for Azure Private Link is Now Available in Confluent Cloud

Many organizations run Apache Kafka clusters in private Azure networks to meet stringent security, compliance, and operational requirements. However, securely replicating data across clusters without exposing traffic to the public internet has traditionally been complex, requiring self-managed mirroring solutions with significant operational overhead.

LLM Data Gateways: Bridging the Gap Between Raw Data and Enterprise-Ready AI

LLM Data Gateways are specialized tools that prepare and secure data for AI systems, ensuring better performance, compliance, and cost efficiency. They act as a bridge between raw data and large language models (LLMs), solving common challenges in AI like poor data quality and security risks.

AI Data Management: Best Practices & Tools

Artificial Intelligence (AI) is transforming the way businesses manage, process, and analyze data. AI Data Management involves the use of machine learning (ML), automation, and intelligent data pipelines to enhance data storage, governance, integration, and security. As organizations deal with ever-growing datasets, AI-driven data management solutions ensure efficiency, scalability, and accuracy.

Google Data Management: A Data Integration Perspective

Managing and integrating data efficiently is a critical requirement for businesses dealing with multi-source, real-time, and large-scale datasets. Google Data Management provides a scalable, cloud-native ecosystem designed for seamless data integration, transformation, and governance. This blog explores Google’s data integration solutions, including ETL/ELT pipelines, real-time data streaming, and AI-powered automation for enterprise-grade data workflows.

Agentic AI Needs an API Backbone: Cultivating Discipline & Governance for Scalable Success

For organizations seeking to leverage agentic AI, the journey begins with a steadfast commitment to discipline and governance. This talk underscores that the true foundation of success in agentic AI adoption is a culture that values structured API capabilities and a rigorous approach to digital integration. By fostering disciplined practices and robust governance frameworks, businesses can establish the resilient API foundations necessary for automating complex processes and scaling AI-driven initiatives.

Get the speed you need with our new Linux portfolio

Mobile app developers are obsessed with speed for one simple reason - their customers are too! We expect a lot from the apps we use. They've got to load super fast, always stay up to date, and offer a steady stream of valuable features. For developers, this means relentless pressure to reduce build times and drive efficiency to stay competitive.

Beyond the Hype: Gen AI Trends and Scaling Strategies for 2025 - MLOps Live #35 with Gartner

In this webinar, we explored the most pressing GenAI challenges and the newest strategies for implementing and scaling GenAI in 2025. Svetlana Sicualar and Yaron Haviv, AI industry leaders and veterans, referenced their work and vast experience with enterprise clients across regions and verticals. They explored key questions that every tech leader should be asking themselves.