Dev At Rest

Overview

RDS with PostgreSQL is cost-effective for general-purpose databases, while Redshift seems more economical for large-scale data analytics due to lower storage costs.Matching use cases are using RDS for transactional applications and Redshift for analytical queries on big datasets, with costs varying by workload.An unexpected detail is that Redshift's compute costs per hour can be higher, but its storage is significantly cheaper, affecting total cost based on data size.Pricing Comparison

RDS with PostgreSQL pricing includes compute costs (e.g., $0.384/hour for a db.m5.xlarge instance) and storage costs (e.g., $0.125/GB-month for gp2). For example, a 1 TB setup might cost around $405.32 monthly.

Web Scraping

- https://www.scrapingdog.com/

Is common for ClickHouse to use S3 for data storage. And while it's not as common (yet) for ClickHouse to directly integrate with Iceberg as a table format, the integration is evolving and becoming increasingly relevant in modern data architectures.

Let's break down each part:

1. ClickHouse and S3 (Common and Yes):

Yes, it's very common for ClickHouse to interact with and utilize S3. In cloud deployments, especially on AWS (where S3 is native), it's a highly prevalent pattern.

Unraveling Flux: Why Your React Frontend Needs a Data Flow Blueprint

Hey Back-End Engineers, let's talk about frontends. Specifically, React frontends, which you've likely heard a lot about. You might be thinking, "Frontend? Isn't that just HTML, CSS, and a bit of JavaScript glue? We handle the real data logic on the server."  Well, in today's web applications, that "bit of JavaScript glue" is doing a lot more, and that's where things like Flux come in.

Imagine your backend system.

In today's data-driven world, operational metrics are the lifeblood of any organization running complex systems. They provide crucial insights into performance, availability, and user behavior. However, as systems become more intricate and user bases grow, we often encounter the daunting challenge of high cardinality metrics. Imagine tracking metrics not just by server, but by individual container, user session, product SKU, or geographical location.

Document Processing Pipelines vs. Kubernetes Containers: A Comparative Analysis with Orchestration Insights

When designing a document processing system, architects and engineers must choose between managed document processing pipelines (e.g., AWS Glue, Google Dataflow) and Kubernetes (k8s)-based containerized solutions.