Systems | Development | Analytics | API | Testing

For more than a decade, Cloudera has been an ardent supporter and committee member of Apache NiFi, long recognizing its power and versatility for data ingestion, transformation, and delivery. Our customers rely on NiFi as well as the associated sub-projects (Apache MiNiFi and Registry) to connect to structured, unstructured, and multi-modal data from a variety of data sources – from edge devices to SaaS tools to server logs and change data capture streams.

Welcome to the first installment of a series of posts discussing the recently announced Cloudera AI Inference service. Today, Artificial Intelligence (AI) and Machine Learning (ML) are more crucial than ever for organizations to turn data into a competitive advantage. To unlock the full potential of AI, however, businesses need to deploy models and AI applications at scale, in real-time, and with low latency and high throughput. This is where the Cloudera AI Inference service comes in.

Snowflake leaders offer insight on AI, open source and cybersecurity development — and the fundamental leadership skills required — in the years ahead. As we come to the end of a calendar year, it’s natural to contemplate what the new year will hold for us. It’s an understatement to say that the future is very hard to predict, but it’s possible to both prepare for the likeliest outcomes and stay ready to adapt to the unexpected.

SmartBear is thrilled to announce our acquisition of QMetry, a leader in AI-enhanced test management solutions, into the family. This merger is not merely about expanding our already robust solutions—it’s about revolutionizing the delivery of software quality across industries. In today’s fast-paced tech landscape, addressing software quality issues is critical; they can significantly impact customer satisfaction and financial growth, while flattening the quality cost curve.

AI is proving that it’s here to stay. While 2023 brought panic and wonder, and 2024 saw widespread experimentation, 2025 will be the year that financial services enterprises get serious about AI's applications. But it’s complicated: AI proofs of concept are graduating from the sandbox to production, just as some of AI’s biggest cheerleaders are turning a bit dour.

Today, Confluent, the data streaming pioneer, is excited to announce its entrance into MongoDB’s new AI Applications Program (MAAP). MAAP is designed to help organizations rapidly build and deploy modern generative AI (GenAI) applications at enterprise scale.

As organizations evolve – onboarding new team members, expanding use cases, and broadening the scope of model development, their compute infrastructure grows increasingly complex. What often begins as a single cloud account using available credits can quickly expand into a hybrid mix of on-prem and cloud resources that come with different associated costs and are tailored to diverse workloads.

The world of software development is undergoing a huge transformation, due to the emergence of artificial intelligence (AI). AI-powered tools and methodologies are reshaping how we write, test, and deploy code, which is making our programming faster, more efficient, and more accessible. In this blog, we’ll explore the concept of code with AI, its applications, benefits, and its potential to redefine the future of technology. So, let’s dive in!

AI is becoming a part of our everyday lives, and in the software testing industry, it is starting to show its impact as well. Traditional testing methods can often happen at a later stage of the development life cycle, which may present challenges for meeting the demands of modern software delivery. This is where shift-left testing comes to shine. This testing methodology has become one of the most used strategies for delivering high-quality software without missing bug findings along the way.