Data Engineering

Collect and transform data on a large scale. Build data pipelines, work with a horizontally scalable architecture, or simply scrape and collect data.

Simplify BigQuery ETL jobs using SQLAlchemy

Extract and move data between BigQuery and relational databases using PyBigQuery: a connector for SQLAlchemy.

Get the most out of Redshift by performance tuning your cluster and learning how to query your data optimally.

Perform SQL-like joins and aggregations on your PySpark DataFrames.

Working with Spark's original data structure API: Resilient Distributed Datasets.

Use Apache Airflow to build and monitor better data pipelines.

Become familiar with building a structured stream in PySpark using the Databricks interface.

An overview of how Kafka works, as well as equivalent message brokers.

Easy DataFrame cleaning techniques ranging from dropping rows to selecting important data.