Systems | Development | Analytics | API | Testing

AI agents have exploded. And we use this term intentionally. Tech vendors rushed to put out agent products that don’t stand up to enterprise use with results that were mostly underwhelming and sometimes even catastrophic. Appian took a different approach. While companies are now scrambling to validate the efficacy of their AI, Appian’s AI is field-tested, so the products you get are ready to use and safe to deploy from day one.

Use the Fivetran Connector SDK to focus on integrating data instead of infrastructure management.

Data teams spend 45% of their time on data preparation, which stifles business growth and delays critical insights. With the ETL market projected to grow from $533 million to $1.28 billion by 2034, businesses face an overwhelming array of choices. Yet traditional ETL tools require specialized coding expertise that non-technical teams simply don't have, creating dangerous dependencies on overburdened IT departments.

Organizations waste an average of $12.9 million annually due to poor data quality. Traditional ETL processes built on hard-coded rules break with schema changes, batch processing misses real-time insights, and manual mapping creates bottlenecks that delay critical business decisions by days or weeks.

Like most pieces of software, DreamFactory requires various dependencies to run. This post will focus how DreamFactory specific packages are updated. Fortunately, DreamFactory is built on top of Laravel, meaning Composer is used as a package manager simplifying much of the process.

Data integration has long been one of the most time-intensive parts of enterprise IT. Connecting multiple systems, reconciling formats, and ensuring data reaches its destination reliably often requires weeks of preparation before the first record moves. But with AI-powered integration, that timeline compresses dramatically. What once took weeks can now be designed, validated, and delivered in minutes.

A test environment is a controlled setting that includes software, hardware, network configuration, test data, and testing tools, where applications can be set up and validated before they are delivered to real users. It can be understood as a safe space for developers and QA engineers to do an assessment of how an application performs under expected real-world usage conditions.

As the demand for data to power AI models and real-time decision making continues to grow, organizations are increasingly looking for ways to simplify and optimize the ways to ingest, and process fresh data within the enterprise. On average, organizations allocate 20–50% of their annual data warehouse spend on compute for data ingestion, amounting to millions of dollars in costs for large enterprises.

In the AI era – the need for enterprise-wide data—from every operational system—available for instant analytics is real. Microsoft Fabric has fundamentally simplified the modern data estate, centering everything around the flexible, unified power of OneLake. A cornerstone of this platform is Mirroring, a low-latency, low-cost solution designed to break down silos. But for data engineering pioneers, the most exciting development is Open Mirroring.