Systems | Development | Analytics | API | Testing

Monetizing Proprietary Data Through APIs: How to Unlock New Revenue in the AI World

A report by Bloomberg Intelligence projects the AI industry will reach $1.3 trillion by 2032, with proprietary data fueling much of this growth. As businesses increasingly adopt generative AI (genAI) to enhance efficiency, data is rapidly becoming one of the most valuable assets in the digital economy. Foundational AI models require vast amounts of data for training, and many AI products are now leveraging proprietary datasets alongside these models to power innovative applications and AI agents.

ImageView in Android: Everything You Need to Know

In Android, ImageView is a widget used to show an image, so it’s vital for every kind of app we build. In fact, it’s the most common widget that we’ll use as Android developers. The image can be a profile image, graphic, or any other type of image, and it can be loaded from a res/drawable folder or an external source. ImageView is pretty straightforward once you’ve got the hang of it, but there’s some stuff we need to know if we’re going to make the most of the widget.

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.

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.