SQL should be your default choice for data engineering pipelines

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com

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  1. distrobox

    Use any linux distribution inside your terminal. Enable both backward and forward compatibility with software and freedom to use whatever distribution you’re more comfortable with. Mirror available at: https://gitlab.com/89luca89/distrobox

    I use https://github.com/89luca89/distrobox if I want to try something on another distro

  2. CodeRabbit

    CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.

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  3. dbt-unit-testing

    This dbt package contains macros to support unit testing that can be (re)used across dbt projects.

    > How do you test some SQL logic in isolation?

    I do this using sql

    1. Extracting an 'ephemeral model' to different model file

    2. Mock out this model in upstream model in unit tests https://github.com/EqualExperts/dbt-unit-testing

    3. Write unit tests for this model.

    This is not different than regular software development in a language like java.

    I would argue its even better better because unit tests are always in tabular format and pretty easy to understand. Java unit tests on other hand are never read by devs in practice.

  4. clickhouse-java

    ClickHouse Java Clients & JDBC Driver

    Agree with the OP that SQL will almost assuredly still be in use for 20+ years in the future, given the simplicity and flexibility of the declarative language, standardization, and as applicable to today as it was then to our big data problems.

    Any discussion of SQL at scale must include ClickHouse [https://clickhouse.com/docs/en/install#self-managed-install], given it's broad open-source use, integrations available for Spark with JDBC [https://github.com/ClickHouse/clickhouse-jdbc/] or the open-source Spark-ClickHouse Connector [https://github.com/housepower/spark-clickhouse-connector], and capability to scale SQL as a network service.

    Disclosure: I work for ClickHouse

  5. spark-clickhouse-connector

    Spark ClickHouse Connector build on DataSourceV2 API

    Agree with the OP that SQL will almost assuredly still be in use for 20+ years in the future, given the simplicity and flexibility of the declarative language, standardization, and as applicable to today as it was then to our big data problems.

    Any discussion of SQL at scale must include ClickHouse [https://clickhouse.com/docs/en/install#self-managed-install], given it's broad open-source use, integrations available for Spark with JDBC [https://github.com/ClickHouse/clickhouse-jdbc/] or the open-source Spark-ClickHouse Connector [https://github.com/housepower/spark-clickhouse-connector], and capability to scale SQL as a network service.

    Disclosure: I work for ClickHouse

  6. prql

    PRQL is a modern language for transforming data — a simple, powerful, pipelined SQL replacement

  7. SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

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