Description:
⬇️ Land A High Paying Data Role ⬇️
https://dataengineerinterviews.com/yt-organic?el=11-10-26-James-Skew&htrafficsource=ytorganic
Two data engineers can use the same tools and build completely different pipelines. So which one is right?
The answer depends on something many beginners overlook.
The business requirements.
Learning AWS Glue, Databricks, Snowflake, and Apache Spark is important. But knowing when to use them, how to scale them, and why one architecture makes more sense than another is what develops real engineering judgment.
In this Data Engineer Academy training session, we explore the decisions behind building reliable data pipelines.
Starting with data skew in Apache Spark.
Imagine one partition contains significantly more data than the others. While most tasks finish quickly, one overloaded task keeps running.
Adding more compute doesn't automatically solve that imbalance.
You need to understand partitioning, executors, and how Spark distributes work across its cluster.
Then comes another critical decision.
Batch processing or streaming?
If a business only needs sales reports refreshed every hour, a batch pipeline might be sufficient.
But when IoT sensors continuously generate time-sensitive readings, streaming may be the better fit.
And what about ETL vs. ELT?
There's no universal winner.
In fact, the session explores a real supply-chain example where Kafka change data capture supported different pipelines: one following an ELT approach with bronze, silver, and gold layers, and another applying transformations before loading data for a dashboard.
Same source data. Different business needs. Different designs.
That's why building a portfolio project shouldn't mean simply copying an architecture from a tutorial.
Think about data volume, processing frequency, storage, compute, partitioning, and the trade-offs behind every service you choose.
Why use AWS Lambda for lightweight API ingestion instead of a heavier Glue job?
When should you consider Delta Lake?
Does your use case actually require streaming?
The strongest projects don't just show that a pipeline works. They show why it was designed that way.
Curious what the path from where you are to a 200 grand data role actually looks like? I lay it out in this free video →
https://dataengineerinterviews.com/yt-organic?el=11-10-26-James-Skew&htrafficsource=ytorganic
#DataEngineerAcademy #DataEngineering #ApacheSpark #DataPipelines #ETL #ELT #AWSGlue #Databricks #ApacheKafka
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