Systems | Development | Analytics | API | Testing

Query templates in BigQuery data clean rooms let you create pre-defined queries that run against specific tables, reducing the risk of data exfiltration.

Federal agencies are right to prioritize commercial off-the-shelf (COTS) solutions for procurement modernization. Recent executive orders have provided a clear mandate for 'buy before build'—steering agencies toward proven, market-ready technology to accelerate mission delivery. But not all off-the-shelf procurement solutions are created equal. The technology landscape has evolved, revealing a critical divide in the world of off-the-shelf acquisition software.

Healthcare organizations are under growing pressure to connect legacy EHR (Electronic Health Record) and ERP (Enterprise Resource Planning) systems while safeguarding patient privacy and meeting strict compliance standards. Most of these systems — Epic, Cerner, MEDITECH, Infor, Oracle, SAP, and others — rely on enterprise-grade databases like Oracle, SQL Server, IBM DB2, SAP HANA, InterSystems IRIS, and PostgreSQL.

In Europe, trust is everything, and the bar is set by law. GDPR, the AI Act, NIS2, DORA, and the Data Act shape how data and AI must operate. Leaders need to show where data lives, who can touch it, and how it moves, and they want cloud speed and flexibility without giving up control, so sovereignty and transparency must be built in from day one.

In part one of this series, we walked through how to use Showcase in a Rails app. It's now time to read some Ruby code written by experienced Rails developers. To do this without getting lost, we'll choose one feature of the showcase engine and analyze how it works: rendering a preview of a component. Let's get started!

Key Takeaways Enterprise data teams spend millions on warehouse infrastructure while still designing schemas the way they did in 1995—one entity at a time, one relationship at a time, hoping the model survives its first encounter with production data. The irony runs deep: organizations racing to deploy real-time analytics are bottlenecked by modeling processes that take six to eight weeks before a single pipeline runs. Data warehouses succeed or fail on design.

https://www.trustwave.com/hubfs/Blogs/Trustwave_Blog/Headers/Cybersecurity_Awareness_Month.png.

Enterprise data storage comprises a patchwork of systems: ERP databases, CRM platforms, spreadsheets, cloud apps, and legacy files. These systems do their own jobs well individually, but collectively they create a fragmented landscape. For anyone tasked with building a migration, an integration, or even a simple report, the first challenge is not moving data. It’s understanding what exists and how it all connects.

In our previous blog post, we introduced two ways that Confluent Cloud can integrate with AWS Lambda. One option is using Lambda’s Event Source Mapping (ESM) for Apache Kafka, wherein Lambda creates a consumer group, consumes records off the provided topic, and triggers the Lambda function. The record is polled by the ESM, and the consumed record subsequently acts as the event data provided to (and processed by) the Lambda function.

Traditional test metrics like automation %, pass/fail rates, and defect counts don’t reflect the impact of introducing agents into the QA process. This blog explores a new class of KPIs designed to measure how well your virtual test team is performing including Agent Assist Rate, Human Override Rate, Scenario Coverage Delta, and Review Time Saved.