Netflix

Netflix / wick

Public

A zero cost type safe Apache Spark API

34
2
100% credibility
Found Apr 20, 2026 at 34 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Scala
AI Summary

Wick provides a safer way to work with large datasets using compile-time checks and helpful suggestions for building reliable analysis steps.

How It Works

1
🔍 Discover Wick

You hear about Wick from Netflix, a helpful tool that makes working with large datasets safer and easier right from the start.

2
📚 Follow the guide

You read the friendly getting started instructions and prepare a simple workspace to try your data ideas.

3
📊 Shape your data

You describe your information like employee names, departments, and roles using simple structures.

4
✨ Build safely

You filter, combine, and summarize data with smart checks that catch mistakes before you even run it, feeling confident as your editor suggests options.

5
▶️ See it work

You preview your results right away to confirm everything looks perfect without surprises.

🎉 Perfect pipelines

Your data processing flows smoothly every time, saving hours of fixes and letting you focus on insights.

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Star Growth

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AI-Generated Review

What is wick?

Wick delivers a zero cost abstraction over Apache Spark in Scala 3, turning untyped DataFrames into type-safe DataSeqs. Developers define case classes for schemas and chain operations like filter, select, join, groupBy, and aggregate with compile-time checks on columns, types, and nulls—full IDE autocompletion included. It matches DataFrame speed without Dataset's serialization overhead, loading from tables or creating from Seq data.

Why is it gaining traction?

Compile-time validation catches column typos, invalid sorts, or bad ops before cluster runs, slashing debug cycles. Named tuples enable clean syntax for complex joins and aggs, with explicit null tracking via `-Yexplicit-nulls`. Beats macros like Iskra (poor IDE support) or outdated Scala 2 tools like Frameless.

Who should use this?

Scala 3 Spark users building ETL pipelines, analytics jobs, or joins across departments/employees data. Data engineers at scale (think Netflix) tired of runtime schema mismatches in production. Pairs well with Iceberg/Hive tables for type-safe reads.

Verdict

Worth prototyping for new Spark work if you're on Scala 3.7+—excellent README examples compile and run locally. But 34 stars and 1.0% credibility score mean it's immature; watch for adoption beyond zero cost abstraction hype.

(198 words)

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