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Streaming Processing

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May 11, 2024 4 minute read
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Streaming Processing is a real-time data processing technique that involves continuously receiving, processing, and analyzing data as it arrives in a continuous stream. Unlike traditional batch processing, which processes data in batches at specific intervals, streaming processing handles data in real-time, enabling immediate responses and proactive decision-making.

Why Learn Streaming Processing

There are several reasons why learning Streaming Processing is beneficial:

Path to Streaming Processing

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We've curated two courses to help you on your path to Streaming Processing. Use these to develop your skills, build background knowledge, and put what you learn to practice.
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Reading list

We've selected four 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 Processing.
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Learning Spark
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A comprehensive guide to building real-time applications with Apache Spark's streaming capabilities, covering fundamentals, advanced concepts, and best practices. Particularly useful for developers and architects interested in implementing streaming data pipelines using Spark.
Learning Spark: Lightning-Fast Data Analytics
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Learning Spark: Lightning-Fast Data Analytics
Kindle Edition
$$
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Stream Processing with Apache Flink
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An official guide from the Apache Flink team, providing a comprehensive overview of Flink's architecture, programming model, and advanced features. Best for developers and data engineers who want to build sophisticated streaming applications with Flink.
Stream Processing with Apache Flink
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Stream Processing with Apache Flink
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Data Algorithms with Spark
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Examines the integration of stream processing and machine learning, focusing on real-time data analytics and predictive modeling. It provides practical examples of building end-to-end streaming machine learning pipelines.
Data Algorithms with Spark: Recipes and Design...
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$$$
Data Algorithms: Recipes for Scaling Up with Hadoop...
Paperback
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Data Algorithms with Spark: Recipes and Design...
Kindle Edition
$$$
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Real-time Linked Dataspaces
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Combines streaming data analysis with machine learning techniques. It covers topics like data preprocessing, feature engineering, model selection, and evaluation for streaming data. It also discusses real-world applications and case studies.
Real-time Linked Dataspaces
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Real-time Linked Dataspaces
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Relevant careers
Data Engineer
Data Analyst
Machine Learning Engineer
Software Engineer
Related topics
Big Data
Cloud Computing
Database Management
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