The text “System Design Series: The Ultimate Guide for Building High-Performance Data Streaming Systems from Scratch!” provides a comprehensive overview of creating high-performance data streaming systems. It delves into the process of building a recommendation system for an e-commerce website, highlighting the importance of data streaming pipelines, data ingestion, processing, data sinks, and querying. Additionally, it addresses the use of Apache Kafka for ingestion and Cassandra for data storage. The text emphasizes the significance of recommendation systems and offers additional resources for further learning.
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System Design Series: The Ultimate Guide for Building High-Performance Data Streaming Systems from Scratch!
Setting up an example problem: A Recommendation System
Data Streaming sounds complex, but let’s start with a simple problem: building a recommendation system for an e-commerce website. This system returns product recommendations for users based on their preferences.
What is a Data Streaming Pipeline?
A Data Streaming Pipeline ingests continuous data, performs processing steps, and stores results for future use. In our case, events come from multiple services, undergo processing steps to compute recommendations, and then the recommendations are stored for later use.
Creating a Data Streaming Pipeline: Step-by-step
Data Ingestion
Data ingestion involves ingesting events from multiple sources. To handle the high scale of data and ensure real-time ingestion, we use Apache Kafka, an event streaming platform that facilitates a decoupled architecture between producers and consumers.
Data Processing
Data processing involves updating user embeddings and generating recommendations for each event. This is achieved through Python microservices that listen to Kafka topics, process the event, and send it to the next topic for further processing.
Data Sinks
Once events are processed and recommendations are generated, the data is stored in Cassandra, a database that can handle high write throughput and scale linearly to accommodate the incoming data.
Querying
Querying the recommendations is a simple process of fetching the precomputed recommendations for a particular user from the database.
Full Architecture
The complete architecture involves data ingestion, processing, storage, and querying, all orchestrated to handle the high scale of data and provide real-time recommendations to users.
For more learning
Explore more about Kafka, Cassandra, and recommendation systems to enhance your understanding of building high-performance data streaming systems.
Conclusion
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