Data Engineering using Kafka and Spark Structured Streaming by Durga Viswanatha Raju Gadiraju, Naga Bhuwaneshwar, Kavitha Penmetsa

This course teaches you how to build efficient streaming pipelines by integrating Kafka and Spark Structured Streaming. You will gain hands-on experience in setting up a self-support lab and processing real-time data, essential for modern data engineering.

Key Skills You Will Acquire

• Kafka topic creation: Learn to create and manage Kafka topics, enabling effective message production and consumption for your streaming applications, which is crucial for real-time data processing.
• Data ingestion techniques: Utilize Kafka Connect to ingest data from web server logs into Kafka topics, ensuring seamless data flow into your processing pipeline and enhancing data accessibility.
• Incremental data processing: Master Spark Structured Streaming to process data incrementally from Kafka topics, significantly enhancing the efficiency of your data processing workflows and reducing latency.
• Hadoop cluster setup: Set up a single node Hadoop cluster, integrating Hive, Spark, and Kafka to create a cohesive environment for data engineering tasks, allowing for streamlined data management.

By completing this course, you will acquire practical skills in data ingestion and processing, preparing you for real-world data engineering challenges.

Who This Course Is For

• Data analysts seeking to enhance their skills in real-time data processing and streaming technologies.
• Software developers interested in integrating Kafka and Spark for building scalable data pipelines.
• IT professionals aiming to expand their knowledge in big data technologies and streaming analytics.

File Details
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Total Size: 4.3GB

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