Go concurrency simplified. Part 4: Post office as a data pipeline

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  • n0rdy-blog-code-samples

    Code samples for https://n0rdy.foo posts

  • The next step should be to replace the slice with a channel to define the customers' queue. But how should we get the customers to put in that channel? Another good question, you are on fire today! We need a function to generate random customers and, ideally, with random waiting to make it more realistic, as customers come to the post office at a different frequency. For that, I asked ChatGPT to generate a list of 50 random names and 50 random Xmas presents that fit into the postal package. I won't provide these lists here to save space, but feel free to check them out on a GitHub repo. Once we have it, the rest of the generator code is trivial:

  • pippin

    Go library to create and manage data pipelines on your machine

  • take a look at the concurrent code written by other devs out there: for example, feel free to check the internals of my library Pippin, but I bet there are many better projects out there to learn from - Google/Bing/DuckDuckGo/Kagi and ChatGPT can help to find the right one

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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  • Apache Spark

    Apache Spark - A unified analytics engine for large-scale data processing

  • also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.

  • ApacheKafka

    A curated re-sources list for awesome Apache Kafka

  • also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.

    also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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