You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
"SSO lets you log into dozens of apps with a single set of credentials. But how does it actually work under the hood? A beginner-friendly walkthrough of the full flow — from clicking 'Sign in with Google' to getting access — step by step.",
"SSO lets you log into dozens of apps with a single set of credentials. But how does it actually work under the hood? A beginner-friendly walkthrough of the full flow — from clicking 'Sign in with Google' to getting access — step by step.",
title: "Why We Rolled Back Our Kafka Pipeline to Batch After 6 Months",
172
-
image: "/img/blogs/batch-vs-stream-cover.png",
173
-
description:
174
-
"Streaming pipelines are powerful for real-time data processing, but they come with hidden costs that are often overlooked. These costs include increased complexity, higher resource consumption, and potential challenges in maintaining data consistency and reliability. This article explores these hidden costs and provides insights on how to mitigate them.",
175
-
slug: "batch-vs-stream-processing",
176
-
authors: ["Aditya-Singh-Rathore"],
177
-
category: "data engineering",
178
-
tags: ["Streaming Pipelines","Real-Time Data Processing","Data Consistency","Data Reliability","Resource Consumption","Complexity","Data Engineering"],
179
-
},
180
169
{
181
170
id: 15,
182
171
title: "Azure Synapse Analytics: When to Use It (And When to Choose Fabric Instead)",
title: "Why We Rolled Back Our Kafka Pipeline to Batch After 6 Months",
183
+
image: "/img/blogs/batch-vs-stream-cover.png",
184
+
description:
185
+
"Streaming pipelines are powerful for real-time data processing, but they come with hidden costs that are often overlooked. These costs include increased complexity, higher resource consumption, and potential challenges in maintaining data consistency and reliability. This article explores these hidden costs and provides insights on how to mitigate them.",
186
+
slug: "batch-vs-stream-processing",
187
+
authors: ["Aditya-Singh-Rathore"],
188
+
category: "data engineering",
189
+
tags: ["Streaming Pipelines","Real-Time Data Processing","Data Consistency","Data Reliability","Resource Consumption","Complexity","Data Engineering"],
190
+
},
191
+
191
192
{
192
193
id: 17,
193
194
title: "Azure Data Pipeline Cost Optimization: How We Cut a $4,200 Bill by 73%",
"PySpark is a powerful tool for big data processing, but it can be challenging to optimize for performance. In this article, we discuss six common mistakes that beginners make when optimizing their PySpark pipelines, which can lead to slow performance and increased costs. We provide practical tips and techniques to help you avoid these pitfalls and improve the efficiency of your PySpark applications.",
description="Complete guide to GitHub achievements and badges. Learn how to unlock and showcase your GitHub contributions with the recode hive community."
The GitHub Certified: Agentic AI Developer certification validates your expertise in creating intelligent automated workflows, building AI-powered agents, and extending GitHub's capabilities.
0 commit comments