Chapter X: Getting Started with AWS Step Functions

We've successfully navigated the world of serverless compute with AWS Lambda. You've learned how to create event-driven functions and unlock the power of code without managing servers. Now, let's elevate our serverless capabilities by delving into AWS Step Functions, a service that allows you to coordinate multiple AWS services into serverless workflows so you can build and run sophisticated applications.

Think of Step Functions as the conductor of your serverless orchestra. While Lambda functions are the individual instruments performing specific tasks, Step Functions defines the order in which these instruments play, ensuring a harmonious and well-structured performance.

Introduction

Tools that enhance productivity and streamline workflows are in high demand. Among these, AI-powered coding assistants have taken center stage, promising to act as virtual pair programmers. While GitHub Copilot and JetBrains AI Assistant have become household names in this space, a lesser-known but intriguing contender, Aider, is making waves.

Introduction

The line between batch and streaming processing is blurring. Apache Iceberg, an open table format, is at the forefront of this shift, promising to unify these paradigms in a single, scalable framework. But how does it achieve this? Can it replace traditional streaming systems like Kafka? And how do tools like Flink and Spark Streaming leverage Iceberg for streaming workloads? Let’s dive in.

502 Bad Gateway Errors with Reflex Python Behind NGINX

Overview

You're experiencing a 502 Bad Gateway error every few hours with your Reflex Python application behind NGINX, which gets resolved by restarting the app but recurs later. This suggests the problem lies with the Reflex app or its connection to NGINX.

Likely Cause

It seems likely that the Reflex Python application is becoming unresponsive after a few hours, possibly due to memory leaks, resource exhaustion, or unhandled exceptions.

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

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.
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