Systems | Development | Analytics | API | Testing

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Using GitLab With Helix QAC

The cloud-based GitLab CI/CD platform allows development teams to streamline their Continuous integration (CI) and Continuous Delivery (CD) pipelines and accelerate their Software Development Lifecycle (SDLC). Adding strict, compliance-based static analysis — as provided by Helix QAC — as new stages to existing GitLab CI/CD pipelines will enhance the SDLC still further, and move your DevOps workflows from Continuous Integration to Continuous Compliance.

Using GitLab With Helix QAC

There are many ways to integrate Helix QAC static analysis within a continuous integration system like GitLab. Helix QAC's full CI/CD integration accelerates the development pipeline and provides maximum flexibility across the entire organization. In this video, we demonstrate the GitLab and Helix QAC integration with a merge request example. Watch to see how easy it is to compare branches, find and fix issues, and automate CI analysis during key phases of development.

Enhancing AI Customer Experience: A Practical Guide

Organizations are harnessing the power of AI to revolutionize products and services across industries. But AI-powered solutions have been getting more sophisticated. We need to redesign and amend our approach to understanding how customers experience these solutions. Unlike traditional products, AI solutions are dynamic, continuously learning and adapting. Traditional metrics may fall short of capturing the nuances of how users interact with AI.

Shift left to write data once, read as tables or streams

Shift Left is a rethink of how we circulate, share and manage data in our organizations using DataStreams, Change Data Capture, FlinkSQL and Tableflow. It addresses the challenges with multi-hop and medallion architectures using batch pipelines by shifting the data preparation, cleaning and schemas to the point where data is created and as a result, you can build fresh trustworthy datasets as streams for operational use cases or Apache Iceberg tables for analytical use cases.

How Developers Can Use Generative AI to Improve Data Quality

It sounds counterintuitive—using a technology that has trust issues to create more trustworthy data. But smart engineers can put generative AI to work to improve the quality of their data, allowing them to build more accurate and trustworthy AI-powered applications.

API-First: Shifting Your Focus from UI to Product Value

If you’re interested in #apifirst but struggle to understand the organisational or operational drivers that might indicate a positive return on investment for adopting such approaches, then check out this session from Frank Kilcommins and Nauman Ali. They cover the topics above and showcase how visual tools, like the new Form Editor within #Swaggerhub can help break down the barriers to participation in API delivery.

Moving Your AI Pilot Projects to Production

Without a doubt, Artificial Intelligence (AI) is revolutionizing businesses, with Australia’s AI spending expected to hit $6.4 billion by 2026. However, according to The State of Enterprise AI and Modern Data Architecture report, while 88% of enterprises adopt AI, many still lack the data infrastructure and team skilling to fully reap its benefits. In fact, over 25% of respondents stated they don’t have the data infrastructure required to effectively power AI.

How to source data from AWS DynamoDB to Confluent using DynamoDB Streams and AWS Lambda

This is a one-minute video showing an animated architectural diagram of the integration between Amazon DynamoDB and Confluent Cloud using DynamoDB Streams and AWS Lambda. Details of the integration are provided via narration.

Q&A with Bitrise's CSO on Gartner's Magic Quadrant: What's next for DevOps?

The DevOps landscape continues to evolve rapidly. As more organizations embrace DevOps to stay competitive, the landscape is shifting to include more diverse players and specialized offerings. But what's next for the space? Gartner's latest Magic Quadrant for DevOps Platforms report highlights the latest developments and standout players in the space, including Bitrise's growing impact on mobile DevOps.

The Real Cost of Ignoring Red Alerts in Software Quality

57% of consumers are willing to switch to a competitor after three or four negative interactions. And that's being generous—sometimes it only takes one or two. Companies can’t afford to ignore red alerts anymore. Take Amazon's checkout flow, for example. It always works because they prioritize testing it rigorously to ensure their core business can generate revenue, even if other features are less polished.