#serverless-postgres

#serverless-postgres

S3 Files is built on Amazon EFS and automatically translates file system operations into S3 requests, allowing applications to work with S3 data without code changes.

With this feature, you can bring entire folders, ML models, dbt adapters, utilities, directly into UDxFs and Stored Procedures without zipping, file-by-file bookkeeping, or manual updates.

The request for its API val request = Request[IO](Method.POST, uri"/jobs")val api = new AsyncJobApi // this will not compile since AsyncJobApi is not defined yet Minimal implementation to make it green: class AsyncJobApi Red test: The API should return a 202 Accepted response: "POST /jobs returns Accepted" in { val request = Request[IO](Method.POST, uri"/jobs") val api = new AsyncJobApi api.routes.orNotFound.run(request).asserting : response => response.status shouldBe Status.Accepted} Make it green: class AsyncJobApi { val routes: HttpRoutes[IO] = HttpRoutes.of[IO] : case req @ POST -> Root / "jobs" => Accepted()} 5.2 Add headers (Trivial Implementation) Red test: add X-Total-Count and Location headers with job ID (only the assertion is shown)

Observability in serverless environments can be challenging, but AWS Distro for OpenTelemetry (ADOT) simplifies this by providing a standardized, vendor-neutral way to collect and export telemetry. ADOT allows you to leverage industry-standard OpenTelemetry APIs to instrument your applications without being locked into a single observability backend. The challenge with containerized Lambdas is that they do not support standard Lambda Layers. Since ADOT is typically deployed as a layer for Lambda functions, we need an alternative way to get the telemetry agent into our execution environment.

An observability control plane isn't just a dashboard. It's the operational authority system. It defines alert rules, routing, ownership, escalation policy, and notification endpoints. When that layer is wrong, the impact is immediate. The wrong team gets paged. The right team never hears about the incident. Your service level indicators look clean while production burns.

A North American manufacturer spent most of 2024 and early 2025 doing what many innovative enterprises did: aggressively standardizing on the public cloud by using data lakes, analytics, CI/CD, and even a good chunk of ERP integration. The board liked the narrative because it sounded like simplification, and simplification sounded like savings. Then generative AI arrived, not as a lab toy but as a mandate. "Put copilots everywhere," leadership said. "Start with maintenance, then procurement, then the call center, then engineering change orders."

Databricks today announced the general availability of Lakebase on AWS, a new database architecture that separates compute and storage. The managed serverless Postgres service is designed to help organizations build faster without worrying about infrastructure management. When databases link compute and storage, every query must use the same CPU and memory resources. This can cause a single heavy query to affect all other operations. By separating compute and storage, resources automatically scale with the actual load.

Over the past decade, software development has undergone a massive transformation due to continuous innovations in tools, processors and novel architectures. In the past, most applications were monoliths and then shifted to microservices, and now we find ourselves embracing composability - a paradigm that prioritizes modular, reusable, and flexible software design. Instead of writing separate, tightly coupled applications, developers now compose software using reusable business capabilities that can be plugged into multiple projects. This enables greater scalability, maintainability, and collaboration across teams and organizations. At the heart of this movement is Bit Harmony, a framework designed to make composability a first-class citizen in modern web development.

The main advantage of going the Multi-Cloud way is that organizations can "put their eggs in different baskets" and be more versatile in their approach to how they do things. For example, they can mix it up and opt for a cloud-based Platform-as-a-Service (PaaS) solution when it comes to the database, while going the Software-as-a-Service (SaaS) route for their application endeavors.

When I manage infrastructure for major events (whether it is the Olympics, a Premier League match or a season finale) I am dealing with a "thundering herd" problem that few systems ever face. Millions of users log in, browse and hit "play" within the same three-minute window. But this challenge isn't unique to media. It is the same nightmare that keeps e-commerce CTOs awake before Black Friday or financial systems architects up during a market crash. The fundamental problem is always the same: How do you survive when demand exceeds capacity by an order of magnitude?