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title: I Tried DataCamp's Data Engineer Course (Honest Review)
description: A sponsored but self-described honest review of DataCamp's data engineering career track, given by a 17-year data engineering veteran who previously led a data...
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# I Tried DataCamp's Data Engineer Course (Honest Review)

**[Data with Baraa](https://daily.dev/sources/datawithbaraa)** · 23 min read · 0 upvotes · 0 comments

## Summary

A sponsored but self-described honest review of DataCamp's data engineering career track, given by a 17-year data engineering veteran who previously led a data platform team at Mercedes-Benz. He walks through the SQL, Python, and advanced Python/professional data engineer tracks, calling out which courses to prioritize (data warehousing, joining data, PySpark, Git, Kafka, data architecture) and which to skip (Java, pandas-heavy sections, data visualization, software engineering principles, and either Snowflake or Databricks depending on career goals). He recommends learning SQL before Python, favors Databricks slightly over Snowflake, and stresses that hands-on projects matter far more than course completion or certificates. He closes with a 25% discount code and an offer to review other platforms if requested.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=iTz2SGn-ZuE>

## Questions this post answers

### Should I learn Databricks or Snowflake as a data engineer?

Companies typically use only one of the two platforms, so pick based on job listings in your area rather than learning both. One perspective favors Databricks because it was built by the same engineers who created Apache Spark, making it more tailored to data engineers' workflows than Snowflake, which pairs more naturally with dbt and SQL-centric teams.

_Comparing databricks and snowflake before committing study time is easier when daily.dev surfaces both sides of the debate._

### Do data engineers need to learn pandas or PySpark?

Data engineers should prioritize PySpark over pandas, since pandas is geared toward data analysts working with smaller datasets while PySpark scales to big data workloads typical of production pipelines. PySpark's syntax closely resembles SQL, so engineers who already know SQL can pick it up relatively quickly once they focus on it instead of spending time on pandas-heavy course material.

_Data engineers weighing pandas against pyspark can track real-world opinions like this on daily.dev._

### Do data engineers still need to learn Python if AI can write pipeline code?

Yes, Python remains the most requested skill for data engineering roles even with AI code generation available. AI-written pipeline code is risky in production because it lacks context about a company's specific source systems, which can introduce data quality issues; companies still need engineers who can debug, extend, and scale that code themselves.

_Developers deciding how much to rely on ai for pipeline code can follow ongoing debate on daily.dev._

## Similar posts on daily.dev

- [From Data Analyst to Data Engineer: My 12-Month Self-Study Roadmap](https://daily.dev/posts/from-data-analyst-to-data-engineer-my-12-month-self-study-roadmap-5o92l4od6) · Towards Data Science · 2 upvotes · 0 comments

---

Tags: [#sql](https://daily.dev/tags/sql), [#data-engineering](https://daily.dev/tags/data-engineering), [#apache-kafka](https://daily.dev/tags/apache-kafka), [#pyspark](https://daily.dev/tags/pyspark)

[View this post on daily.dev](https://daily.dev/posts/i-tried-datacamp-s-data-engineer-course-honest-review--yoxt3ka5x)

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