Systems | Development | Analytics | API | Testing

Open a brokerage’s CRM instance a few years in, and it rarely looks like a sales tool anymore. Somewhere along the way, it picked up MLS feeds, transaction history, integration logic, reporting dashboards, and lately, the raw data behind a first AI pilot. None of that was the plan. Each piece got bolted on because the CRM was the system already sitting there. A CRM was built for a narrower job than that: logging a call, tracking a pipeline, managing the relationship an agent owns.

The fastest way to replace custom Python or PowerShell scripts for client data ingestion is to move each script's logic into a reusable, config-driven pipeline that stores connection details, field mappings, and schedules as metadata instead of code. This guide is for data integration managers and engineers who currently maintain a script per client or per source system. After following it, you'll have a repeatable pattern for onboarding new clients without writing a new script for each one.

One thing has become increasingly clear to me: the businesses that thrive in the AI era won't be the ones with the most data, they'll be the ones where every employee knows how to use it. The World Economic Forum's 2025 Future of Jobs Report names analytical thinking as the top core skill companies need today, and a 2024 Gartner survey found poor data literacy to be one of the top five obstacles to analytics success.

Amazon’s recent pivot on the Selling Partner API is an encouraging reminder that developer pushback can breach walled gardens.

An effective Java debugging strategy lets us pause execution, inspect data, and observe real execution rather than relying on vague assumptions. The complexity of the Java Virtual Machine creates unique challenges, but a focused approach will turn this complexity to our advantage. This guide will equip you with the tools to do this, looking at: By the end, you’ll have a practical workflow to debug Java reliably across local and remote environments.

Looking back on 2025, one idea anchors everything we set out to accomplish: make data accessible and trusted so organizations can generate better decisions for their business, their customers, and the world.

One of the busiest engineers at Bitrise doesn't have a laptop. It lives in Slack. Assign it a Jira ticket or @mention it in a thread, and it reads the requirements, posts a plan, spins up a cloud dev machine, writes and tests the code, opens a pull request, watches CI, reacts to review comments as they land and once the pull request merges, cleans up after itself and closes the ticket.

Most enterprises are spending on AI faster than they’re becoming ready to run it. Over the next two years, IT leaders plan to grow their AI investment by 70%, according to our 2025 State of Data Infrastructure Global Report. The capability keeps arriving. The readiness to operate it does not keep pace.

AI is becoming part of every stage of the testing lifecycle. Teams are using it to analyze requirements, design test cases, generate automation scripts, and accelerate execution activities that previously required significant manual effort. Within Xray, AI already helps transform Jira requirements into actionable test cases with AI Test Case Generation.