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What is Stream Processing? | Batch vs Stream Processing | Data Pipelines | Real-Time Data Processing

In this tutorial we are going to cover stream processing. What it is and how it differs from Batch Processing. So far, we have been processing data with Batch processing approach. Batch processing is a method of running repetitive, high-volume data jobs in a group on ad-hoc or schedule basis. Stream processing is the method of synching data from a source to a destination as the transactions are taking place at the source. We process data as it happens in the source. The latency is very low usually in seconds. So this is real-time data processing. In the next session we will start covering Stream Processing using PySpark and Apache Kafka. #streamprocessing #realtimedata #pyspark 🔗Link to GitHub: https://github.com/hnawaz007/pythonda... 💥Subscribe to our channel:    / haqnawaz   📌 Links ----------------------------------------- #️⃣ Follow me on social media! #️⃣ 🔗 GitHub: https://github.com/hnawaz007 📸 Instagram:   / bi_insights_inc   📝 LinkedIn:   / haq-nawaz   🔗   / hnawaz100   ----------------------------------------- Topics covered in this video: 0:00 - Introduction to Stream Processing 0:13 - Batch Processing 1:34 - Stream Processing 2:55 - Stream Processing Advantages 3:10 - Stream vs Batch Processing Comparison 4:38- Batch Processing Example 4:56 - Stream Processing Example 5:33 - Stream Processing Recap 5:57 - Stream Processing Coming Soon