Cloud Computing

BigQuery now features a suite of augmented analytics Table-Valued Functions (TVFs) designed to automate complex data analysis at scale. Augmented analytics combines AI, ML and statistical methods to automate insight discovery and pattern explanation. These functions allow you to diagnose why metrics changed, uncover underlying trends and relationships across the data, and even isolate the […]

OverviewAs enterprises scale their AI and agentic workflows, they require serverless platforms that make data preparation for model training, evaluation, and inference effortless and efficient. Dataflow is a critical component of Google Cloud’s AI stack. It enables our customers to create batch and streaming pipelines that support a variety of analytics and AI use cases. 

Authors: Hyunsoo Kim, Chloe Kwak Learning level: 300 – Advanced Post type: Best Practices Every inventory manager faces the same question each morning: How much should I order today? The answer depends on dozens of variables (sales history, upcoming promotions, pricing changes, day-of-week seasonality, supplier lead times) and the cost of getting it wrong is

Agent skills fit well alongside documentation and remote MCP servers as ways of enabling the success of your AI workflows. They reduce context window usage for certain use cases, and they’re straightforward to install. However, you might have noticed that managing individual skills can be unwieldy, or that some skills are most useful when they

Consider a real-time transcription service processing 500 concurrent meetings. Each worker processing these meetings requires a dedicated outbound WebSocket connection to an upstream streaming source. When a single worker fails, it drops 100+ connections, causing 2 to 3 minutes of data loss per connection until operators manually restart services. Building real-time streaming workers that maintain

When your application can no longer send or receive messages through an Amazon Simple Queue Service (Amazon SQS) queue, downstream processing can stall. The cause might be a misconfigured Identity and Access Management (IAM) policy, a network partition, a bad deployment, or a transient service event. Your application sees much the same thing regardless: SQS

In October 2025, Athenahealth, a major North American Electronic Health Record (EHR) provider, discovered a gap. An AWS regional service event in us-east-1 made their single-Region HashiCorp Terraform Enterprise (TFE) deployment inaccessible to their developers. This post shares the architecture, the AWS Fault Injection Service (AWS FIS) validation approach, HashiCorp best practices, and lessons learned