Stream Processing 101 | Basics

Code with Irtiza · Beginner ·🔄 Data Engineering ·3y ago

About this lesson

Real-time processing of data using stream processors has become a fundamental part of any system design. Let’s discuss some of the use cases and complexities related to stream processing. We will also talk about Lambda architecture which is a system composed of both batch processing and stream processing. Notes: https://www.docdroid.net/TtAqw0t/youtube-stream-processing-101-pdf 🥹 If you found this helpful, follow me online here: ✍️ Blog https://irtizahafiz.medium.com 👨‍💻 Website https://irtizahafiz.com 📲 Instagram https://www.instagram.com/irtiza.hafiz/ 0:00 Unbounded Data 00:56 What is Streaming? 04:38 Real-Life Examples 06:25 Lambda Architecture (Batch + Stream?) 10:04 Outro #kafka #streamProcessing #flink #systemDesign #programming #softwareDevelopment

Original Description

Real-time processing of data using stream processors has become a fundamental part of any system design. Let’s discuss some of the use cases and complexities related to stream processing. We will also talk about Lambda architecture which is a system composed of both batch processing and stream processing. Notes: https://www.docdroid.net/TtAqw0t/youtube-stream-processing-101-pdf 🥹 If you found this helpful, follow me online here: ✍️ Blog https://irtizahafiz.medium.com 👨‍💻 Website https://irtizahafiz.com 📲 Instagram https://www.instagram.com/irtiza.hafiz/ 0:00 Unbounded Data 00:56 What is Streaming? 04:38 Real-Life Examples 06:25 Lambda Architecture (Batch + Stream?) 10:04 Outro #kafka #streamProcessing #flink #systemDesign #programming #softwareDevelopment
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related AI Lessons

⚡
How I built the OSS alternatives directory: GitHub ETL, Turso, and the UPSERT trap I hit
Learn how to build a data pipeline for an open-source alternatives directory using GitHub ETL, Turso, and Claude Haiku summaries
Dev.to · MORINAGA
 ⚡
Apache Iceberg in Production: Compaction, Catalogs, and the Pitfalls Nobody Warns You About
Learn how to use Apache Iceberg in production, including compaction, catalogs, and common pitfalls to avoid, to improve data engineering workflows
Dev.to · Gabriel Henrique
 ⚡
Your First Task as a Data Engineer in a New Company? Make the ETL Pipeline Testable
As a new data engineer, make the ETL pipeline testable to ensure data quality and reliability
Towards Data Science
 ⚡
From DataStage and Informatica to Databricks Medallion Architecture: Why Migration Is More Than Code Conversion
Learn how to migrate legacy ETL systems like DataStage to modern architectures like Databricks Medallion, and why it's more than just code conversion
Dev.to · Amit Kumar Singh

Chapters (5)

Unbounded Data
0:56 What is Streaming?
4:38 Real-Life Examples
6:25 Lambda Architecture (Batch + Stream?)
10:04 Outro
Up next
A Moment Frozen in Time | Arnav Iyengar | TEDxJenks Youth
TEDx Talks
Watch →