Data Engineering With Python: Final Thoughts and Takeaways
That’s it. Fifteen chapters, seventeen posts, and one complete walkthrough of Paul Crickard’s Data Engineering with Python (Packt, 2020, ISBN: 978-1-83921-418-9).
Apache spark
Real-Time Edge Data With MiNiFi and Spark - Study Notes From Data Engineering With Python Ch 15
You have NiFi running. Kafka is streaming. Spark is processing. But what about the data source? What happens when your data comes from a tiny sensor or a Raspberry Pi that can barely run a web browser?
Data Processing With Apache Spark - Study Notes From Data Engineering With Python Ch 14
You have streaming data. You have batch data. You have a lot of it. Now you need to actually process it. Fast. On more than one machine.
Big Data and Distributed Systems - Chapter 11 Retelling
At some point, your data gets too big for one machine. That’s not a hypothetical. Netflix, Google, Amazon, they all hit that wall years ago. The question is: what do you do when a single server can’t keep up?
Data Engineering With GCP Chapter 5 Part 2: Working With Spark on Dataproc
In Part 1 we set up a Dataproc cluster, got familiar with HDFS, and touched on what a data lake actually is. Now it is time to get into the real work: writing PySpark code, understanding RDDs, moving data between HDFS, GCS, and BigQuery, and learning how to actually submit Spark jobs to Dataproc.