Why Flink stream jobs require microservice‑style discipline - capacity planning, checkpoint health, backpressure, ownership by multiple teams - and offers best practices for metrics, staging, and monitoring to manage complexity.
How Expedia uses real-time A/B test monitoring with Apache Flink to detect anomalies early, preventing revenue loss and improving experiment reliability.
Is dbt Core dead? What ‘source available’ really means, why dbt Labs’ shifting to Fusion, and how it marks the end of dbt’s open source innovation.
1/ Chain-of-thought prompting works because it slows down the model’s reasoning. Slower = smarter.
How Meta’s initial approach caused them troubles and their effort to fix them at the organizational scale.
A practical guide to dimensional data modeling in Databricks using Delta Lake, Unity Catalog, and Delta Live Tables to build scalable, BI-ready star schemas and fact/dimension tables.
How to bridge that gap by combining ADF diagnostic settings with Databricks system tables. How to create a centralized overview to analyze the amount of data ingested and the end-to-end runtime for a specific use case.
Enabling real-time ingestion of IoT data streams into Flink pipelines using Eclipse Paho.
Introduces a lightweight Python semantic layer built on Ibis, designed for simplicity and version control.
Polaris now supports more parallel transactions with lower latency, thanks to a refactored JDBC-backed persistence layer.
From database polling to event-time alerting, Nexthink explains how they rebuilt monitoring with Apache Flink on AWS.
A fragile payment flow became a scalable, event-driven architecture using Kafka. One topic, many consumers, instant results.