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Streaming Ingestion

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May 1, 2024 2 minute read

Interested in data engineering and machine learning? Streaming ingestion is a crucial component of many modern data pipelines and machine learning systems, and there are many online courses that can help you learn it.

Why Learn About Streaming Ingestion?

Streaming ingestion refers to the process of continuously capturing and processing data as it is generated, in contrast to batch processing, where data is collected and processed periodically. This makes streaming ingestion ideal for real-time applications such as fraud detection, anomaly detection, and streaming analytics.

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Reading list

We've selected six books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Streaming Ingestion.
Deep dive into event streaming architectures. It addresses many advanced topics such as distributed stream processing, time, and state management. The authors are Apache Beam committers with extensive experience in building and operating streaming systems at Google.
Comprehensive guide to Apache Kafka, one of the most popular open-source streaming platforms. It covers everything from Kafka's architecture to its operation and administration.
Focuses on event-driven architecture using Apache Kafka, covering how to design and implement streaming ingestion pipelines.
Comprehensive guide to Apache Flink, a popular open-source streaming processing framework. It covers everything from Flink's architecture to its programming model.
Comprehensive overview of complex event processing (CEP). CEP technique for processing high-volume, real-time data in order to identify patterns and trends.
This pocket reference provides a concise overview of data pipelines and their components, including streaming ingestion.
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